chore: release v1.87.2

refactor(baoyu-translate): simplify translation prompts and workflow
Condense 15+ verbose translation principles to 7 concise ones. Consolidate analysis from 8 sections to 4, review from 8 to 3. Simplify subagent prompt template accordingly.
2026-07-12 13:59:47 +08:00 · 2026-03-26 23:03:02 -05:00 · 2026-03-26 23:02:28 -05:00 · 2026-03-26 09:52:54 -05:00 · 2026-03-26 09:51:51 -05:00 · 2026-03-26 09:51:48 -05:00
119 changed files with 13892 additions and 679 deletions
@@ -6,48 +6,33 @@
  },
  "metadata": {
    "description": "Skills shared by Baoyu for improving daily work efficiency",
-    "version": "1.73.0"
+    "version": "1.87.2"
  },
  "plugins": [
    {
-      "name": "content-skills",
-      "description": "Content generation and publishing skills",
+      "name": "baoyu-skills",
+      "description": "Content generation, AI backends, and utility tools for daily work efficiency",
      "source": "./",
      "strict": true,
      "skills": [
-        "./skills/baoyu-xhs-images",
-        "./skills/baoyu-post-to-x",
-        "./skills/baoyu-post-to-wechat",
-        "./skills/baoyu-post-to-weibo",
        "./skills/baoyu-article-illustrator",
-        "./skills/baoyu-cover-image",
-        "./skills/baoyu-slide-deck",
        "./skills/baoyu-comic",
-        "./skills/baoyu-infographic"
-      ]
-    },
-    {
-      "name": "ai-generation-skills",
-      "description": "AI-powered generation backends",
-      "source": "./",
-      "strict": true,
-      "skills": [
-        "./skills/baoyu-danger-gemini-web",
-        "./skills/baoyu-image-gen"
-      ]
-    },
-    {
-      "name": "utility-skills",
-      "description": "Utility tools for content processing",
-      "source": "./",
-      "strict": true,
-      "skills": [
-        "./skills/baoyu-danger-x-to-markdown",
        "./skills/baoyu-compress-image",
-        "./skills/baoyu-url-to-markdown",
+        "./skills/baoyu-cover-image",
+        "./skills/baoyu-danger-gemini-web",
+        "./skills/baoyu-danger-x-to-markdown",
        "./skills/baoyu-format-markdown",
+        "./skills/baoyu-imagine",
+        "./skills/baoyu-infographic",
        "./skills/baoyu-markdown-to-html",
-        "./skills/baoyu-translate"
+        "./skills/baoyu-post-to-weibo",
+        "./skills/baoyu-post-to-wechat",
+        "./skills/baoyu-post-to-x",
+        "./skills/baoyu-slide-deck",
+        "./skills/baoyu-translate",
+        "./skills/baoyu-url-to-markdown",
+        "./skills/baoyu-xhs-images",
+        "./skills/baoyu-youtube-transcript"
      ]
    }
  ]
@@ -166,3 +166,4 @@ posts/
 .clawdhub/
 .release-artifacts/
 .worktrees/
+youtube-transcript/
@@ -2,6 +2,169 @@

 English | [中文](./CHANGELOG.zh.md)

+## 1.87.2 - 2026-03-26
+
+### Refactor
+- `baoyu-translate`: simplify translation prompts from 15+ verbose principles to 7 concise ones, consolidate analysis and review steps in workflow references
+
+## 1.87.1 - 2026-03-26
+
+### Maintenance
+- Add deprecation notice to `baoyu-image-gen` SKILL.md redirecting users to `baoyu-imagine`
+- Document deprecated skills policy in CLAUDE.md
+
+## 1.87.0 - 2026-03-26
+
+### Maintenance
+- Remove deprecated `baoyu-image-gen` redirect skill and plugin manifest entry — migration to `baoyu-imagine` is complete
+
+## 1.86.0 - 2026-03-25
+
+### Features
+- `baoyu-translate`: enrich translation prompt with full analysis context — source voice assessment, structured figurative language mapping, comprehension challenge reasoning, structural/creative challenges, and chunk position context for subagents
+
+## 1.85.0 - 2026-03-25
+
+### Features
+- `baoyu-imagine`: auto-migrate legacy `baoyu-image-gen` EXTEND.md config path at runtime
+- Add `baoyu-image-gen` deprecation redirect skill to guide users to install `baoyu-imagine` and remove the old skill
+
+## 1.84.0 - 2026-03-25
+
+### Features
+- Rename `baoyu-image-gen` skill to `baoyu-imagine` — shorter command name, all references updated across docs, configs, and dependent skills
+
+## 1.83.0 - 2026-03-25
+
+### Features
+- `baoyu-image-gen`: add MiniMax provider (`image-01` / `image-01-live`) with subject_reference for character/portrait consistency, custom sizes, and aspect ratio support
+
+## 1.82.0 - 2026-03-24
+
+### Features
+- `baoyu-url-to-markdown`: add browser fallback strategy — headless first, automatic retry in visible Chrome on technical failure; new `--browser auto|headless|headed` flag with `--headless`/`--headed` shortcuts
+- `baoyu-url-to-markdown`: add content cleaner module for HTML preprocessing before extraction (remove ads, base64 images, scripts, styles)
+- `baoyu-url-to-markdown`: support base64 data URI images in media localizer alongside remote URLs
+- `baoyu-url-to-markdown`: capture final URL from browser to track redirects for output path generation
+- `baoyu-url-to-markdown`: add agent quality gate documentation for post-capture content validation
+
+### Dependencies
+- `baoyu-url-to-markdown`: upgrade defuddle ^0.12.0 → ^0.14.0
+
+### Tests
+- `baoyu-url-to-markdown`: add unit tests for content-cleaner, html-to-markdown, legacy-converter, media-localizer
+
+## 1.81.0 - 2026-03-24
+
+### Features
+- `baoyu-youtube-transcript`: add yt-dlp fallback when YouTube blocks direct InnerTube API, with alternate client identity retry and cookie support via `YOUTUBE_TRANSCRIPT_COOKIES_FROM_BROWSER` env var
+
+### Refactor
+- `baoyu-youtube-transcript`: split monolithic script into typed modules (youtube, transcript, storage, shared, types) and add unit tests
+
+## 1.80.1 - 2026-03-24
+
+### Fixes
+- `baoyu-image-gen`: use correct `prompt` field name for Jimeng API request
+
+## 1.80.0 - 2026-03-24
+
+### Features
+- `baoyu-image-gen`: add Azure OpenAI as independent image generation provider with flexible endpoint parsing, deployment-name resolution, quality mapping, and reference image validation
+
+## 1.79.2 - 2026-03-23
+
+### Fixes
+- `baoyu-cover-image`: simplify reference image handling — use `--ref` when model supports it, only create description files for models without reference image support
+- `baoyu-post-to-weibo`: add no-theme rule for article markdown-to-HTML conversion
+
+### Tests
+- Fix Node-compatible parser tests and add parser test dependencies
+
+## 1.79.1 - 2026-03-23
+
+### Fixes
+- Consolidate to single plugin to prevent duplicate skill registration (by @TyrealQ)
+- `baoyu-article-illustrator`: remove opacity parameter from watermark prompt
+- `baoyu-comic`: fix Doraemon naming spacing and remove opacity from watermark prompt
+- `baoyu-xhs-images`: remove opacity from watermark prompt and fix CJK spacing
+
+### Documentation
+- Update project documentation to reflect single-plugin architecture
+
+## 1.79.0 - 2026-03-22
+
+### Features
+- `baoyu-post-to-wechat`: improve credential loading with multi-source resolution, priority ordering, and diagnostics for skipped incomplete sources
+
+## 1.78.0 - 2026-03-22
+
+### Features
+- `baoyu-url-to-markdown`: add URL-specific parser layer for X/Twitter and archive.ph sites
+- `baoyu-url-to-markdown`: improved slug generation with stop words removal and subdirectory output structure
+
+### Fixes
+- `baoyu-url-to-markdown`: preserve anchor elements containing media in legacy converter
+- `baoyu-url-to-markdown`: smarter title deduplication to avoid redundant headings
+
+## 1.77.0 - 2026-03-22
+
+### Features
+- `baoyu-youtube-transcript`: add end times to chapter data (by @jzOcb)
+
+### Fixes
+- `sync-clawhub`: skip failed skills instead of aborting
+
+## 1.76.1 - 2026-03-21
+
+### Documentation
+- `baoyu-youtube-transcript`: fix zsh glob issue — always single-quote YouTube URLs when running the script
+
+## 1.76.0 - 2026-03-21
+
+### Features
+- `baoyu-youtube-transcript`: add title heading, description summary, and cover image to markdown output
+
+### Fixes
+- `baoyu-markdown-to-html`: use process.execPath and tsx import in test runner
+
+## 1.75.0 - 2026-03-21
+
+### Features
+- `baoyu-youtube-transcript`: new skill — download YouTube video transcripts/subtitles and cover images with multi-language, chapters, and speaker identification support
+
+## 1.74.1 - 2026-03-21
+
+### Fixes
+- `baoyu-image-gen`: align OpenRouter image generation with current API, harden image support, and narrow Gemini aspect ratios (by @cwandev)
+- `baoyu-image-gen`: broaden OpenRouter model detection and aspect ratio validation
+
+## 1.74.0 - 2026-03-20
+
+### Features
+- `baoyu-markdown-to-html`: CLI now supports all rendering options — color, font-family, font-size, code-theme, mac-code-block, line-number, count, legend
+
+### Fixes
+- `baoyu-markdown-to-html`: fix CSS custom property regex to handle quoted values; grace/simple themes now layer default CSS
+
+## 1.73.3 - 2026-03-20
+
+### Fixes
+- `baoyu-post-to-wechat`: fix placeholder replacement to avoid shorter placeholders matching longer numbered variants
+
+## 1.73.2 - 2026-03-20
+
+### Fixes
+- `baoyu-post-to-wechat`: fix body image upload to correctly use media/uploadimg API with format and size validation (by @AICreator-Wind)
+
+### Refactor
+- `baoyu-post-to-wechat`: extract image processor module for local format conversion (WebP/BMP/GIF → JPEG/PNG) instead of material API fallback
+
+## 1.73.1 - 2026-03-18
+
+### Refactor
+- `baoyu-danger-x-to-markdown`: migrate tests from bun:test to node:test
+
 ## 1.73.0 - 2026-03-18

 ### Features
@@ -2,6 +2,169 @@

 [English](./CHANGELOG.md) | 中文

+## 1.87.2 - 2026-03-26
+
+### 重构
+- `baoyu-translate`：精简翻译提示词，将 15+ 条冗长原则压缩为 7 条，合并分析和审校步骤
+
+## 1.87.1 - 2026-03-26
+
+### 维护
+- 在 `baoyu-image-gen` SKILL.md 中添加废弃提示，引导用户使用 `baoyu-imagine`
+- 在 CLAUDE.md 中记录废弃技能策略
+
+## 1.87.0 - 2026-03-26
+
+### 维护
+- 移除已废弃的 `baoyu-image-gen` 重定向技能及插件清单条目 — 向 `baoyu-imagine` 的迁移已完成
+
+## 1.86.0 - 2026-03-25
+
+### 新功能
+- `baoyu-translate`：丰富翻译提示词的分析上下文 — 加入原文语气评估、结构化比喻映射表、理解难点推理、结构性/创造性翻译挑战，以及分块翻译的位置上下文
+
+## 1.85.0 - 2026-03-25
+
+### 新功能
+- `baoyu-imagine`：运行时自动迁移旧版 `baoyu-image-gen` 的 EXTEND.md 配置路径
+- 新增 `baoyu-image-gen` 废弃重定向技能，引导用户安装 `baoyu-imagine` 并移除旧技能
+
+## 1.84.0 - 2026-03-25
+
+### 新功能
+- 将 `baoyu-image-gen` 技能重命名为 `baoyu-imagine` — 更简短的命令名，所有文档、配置和依赖技能中的引用已同步更新
+
+## 1.83.0 - 2026-03-25
+
+### 新功能
+- `baoyu-image-gen`：新增 MiniMax 服务商（`image-01` / `image-01-live`），支持 subject_reference 角色/肖像一致性、自定义尺寸和宽高比
+
+## 1.82.0 - 2026-03-24
+
+### 新功能
+- `baoyu-url-to-markdown`：新增浏览器回退策略 — 默认无头模式优先，技术故障时自动重试有头 Chrome；新增 `--browser auto|headless|headed` 参数及 `--headless`/`--headed` 快捷方式
+- `baoyu-url-to-markdown`：新增内容清理模块，提取前预处理 HTML（移除广告、base64 图片、脚本、样式）
+- `baoyu-url-to-markdown`：媒体本地化支持 base64 data URI 图片
+- `baoyu-url-to-markdown`：从浏览器捕获最终 URL 以跟踪重定向，用于输出路径生成
+- `baoyu-url-to-markdown`：新增 Agent 质量门控文档，规范捕获后的内容验证流程
+
+### 依赖
+- `baoyu-url-to-markdown`：升级 defuddle ^0.12.0 → ^0.14.0
+
+### 测试
+- `baoyu-url-to-markdown`：新增 content-cleaner、html-to-markdown、legacy-converter、media-localizer 单元测试
+
+## 1.81.0 - 2026-03-24
+
+### 新功能
+- `baoyu-youtube-transcript`：YouTube 封锁直连 InnerTube API 时自动回退到 yt-dlp，支持备用客户端身份重试及通过 `YOUTUBE_TRANSCRIPT_COOKIES_FROM_BROWSER` 环境变量传递浏览器 Cookie
+
+### 重构
+- `baoyu-youtube-transcript`：将单体脚本拆分为类型化模块（youtube、transcript、storage、shared、types）并添加单元测试
+
+## 1.80.1 - 2026-03-24
+
+### 修复
+- `baoyu-image-gen`：修正即梦 API 请求中的 `prompt` 字段名
+
+## 1.80.0 - 2026-03-24
+
+### 新功能
+- `baoyu-image-gen`：新增 Azure OpenAI 作为独立图像生成服务商，支持灵活的端点解析、部署名称推断、质量映射及参考图片格式校验
+
+## 1.79.2 - 2026-03-23
+
+### 修复
+- `baoyu-cover-image`：简化参考图片处理流程 — 模型支持 `--ref` 时直接传递，仅在模型不支持参考图时创建描述文件
+- `baoyu-post-to-weibo`：文章 Markdown 转 HTML 时不传递 --theme 参数
+
+### 测试
+- 修复 Node 兼容的解析器测试，添加解析器测试依赖
+
+## 1.79.1 - 2026-03-23
+
+### 修复
+- 合并为单一插件，防止 skill 重复注册 (by @TyrealQ)
+- `baoyu-article-illustrator`：移除水印提示词中的不透明度参数
+- `baoyu-comic`：修正哆啦 A 梦命名间距，移除水印不透明度参数
+- `baoyu-xhs-images`：移除水印不透明度参数，修正中英文间距
+
+### 文档
+- 更新项目文档以反映单一插件架构
+
+## 1.79.0 - 2026-03-22
+
+### 新功能
+- `baoyu-post-to-wechat`：改进凭据加载机制，支持多来源优先级解析，并提供不完整凭据来源的诊断信息
+
+## 1.78.0 - 2026-03-22
+
+### 新功能
+- `baoyu-url-to-markdown`：新增 URL 专用解析层，支持 X/Twitter 和 archive.ph 站点的定制化 HTML 提取
+- `baoyu-url-to-markdown`：改进 slug 生成算法，去除停用词并采用子目录输出结构
+
+### 修复
+- `baoyu-url-to-markdown`：旧版转换器保留包含媒体元素的锚标签
+- `baoyu-url-to-markdown`：更智能的标题去重，避免重复添加标题
+
+## 1.77.0 - 2026-03-22
+
+### 新功能
+- `baoyu-youtube-transcript`：为章节数据添加结束时间 (by @jzOcb)
+
+### 修复
+- `sync-clawhub`：跳过失败的技能而不是中止同步
+
+## 1.76.1 - 2026-03-21
+
+### 文档
+- `baoyu-youtube-transcript`：修复 zsh glob 问题 — 运行脚本时始终对 YouTube URL 使用单引号
+
+## 1.76.0 - 2026-03-21
+
+### 新功能
+- `baoyu-youtube-transcript`：Markdown 输出中新增标题、描述摘要和封面图片
+
+### 修复
+- `baoyu-markdown-to-html`：测试运行器改用 process.execPath 和 tsx import
+
+## 1.75.0 - 2026-03-21
+
+### 新功能
+- `baoyu-youtube-transcript`：新技能 — 下载 YouTube 视频字幕/转录文本和封面图片，支持多语言、章节分段和说话人识别
+
+## 1.74.1 - 2026-03-21
+
+### 修复
+- `baoyu-image-gen`：对齐 OpenRouter 图像生成与当前 API，增强图像支持，收窄 Gemini 宽高比范围 (by @cwandev)
+- `baoyu-image-gen`：扩展 OpenRouter 模型检测和宽高比验证
+
+## 1.74.0 - 2026-03-20
+
+### 新功能
+- `baoyu-markdown-to-html`：CLI 支持全部渲染选项 — color、font-family、font-size、code-theme、mac-code-block、line-number、count、legend
+
+### 修复
+- `baoyu-markdown-to-html`：修复 CSS 自定义属性正则无法处理带引号值的问题；grace/simple 主题现在会叠加 default 主题 CSS
+
+## 1.73.3 - 2026-03-20
+
+### 修复
+- `baoyu-post-to-wechat`：修复占位符替换时短占位符错误匹配更长编号变体的问题
+
+## 1.73.2 - 2026-03-20
+
+### 修复
+- `baoyu-post-to-wechat`：修复正文图片上传，正确使用 media/uploadimg 接口并处理格式和大小限制 (by @AICreator-Wind)
+
+### 重构
+- `baoyu-post-to-wechat`：提取图片处理模块，本地转换不支持的格式（WebP/BMP/GIF → JPEG/PNG）而非回退到 material 接口
+
+## 1.73.1 - 2026-03-18
+
+### 重构
+- `baoyu-danger-x-to-markdown`：测试从 bun:test 迁移至 node:test
+
 ## 1.73.0 - 2026-03-18

 ### 新功能
@@ -1,16 +1,16 @@
 # CLAUDE.md

-Claude Code marketplace plugin providing AI-powered content generation skills. Version: **1.73.0**.
+Claude Code marketplace plugin providing AI-powered content generation skills. Version: **1.87.2**.

 ## Architecture

-Skills organized into three categories in `.claude-plugin/marketplace.json` (defines plugin metadata, version, and skill paths):
+Skills are exposed through the single `baoyu-skills` plugin in `.claude-plugin/marketplace.json` (which defines plugin metadata, version, and skill paths). The repo docs still group them into three logical areas:

-| Category | Description |
-|----------|-------------|
-| `content-skills` | Generate or publish content (images, slides, comics, posts) |
-| `ai-generation-skills` | AI generation backends |
-| `utility-skills` | Content processing (conversion, compression, translation) |
+| Group | Description |
+|-------|-------------|
+| Content Skills | Generate or publish content (images, slides, comics, posts) |
+| AI Generation Skills | AI generation backends |
+| Utility Skills | Content processing (conversion, compression, translation) |

 Each skill contains `SKILL.md` (YAML front matter + docs), optional `scripts/`, `references/`, `prompts/`.

@@ -31,7 +31,7 @@ Execute: `${BUN_X} skills/<skill>/scripts/main.ts [options]`

 - **Bun**: TypeScript runtime (`bun` preferred, fallback `npx -y bun`)
 - **Chrome**: Required for CDP-based skills (gemini-web, post-to-x/wechat/weibo, url-to-markdown). All CDP skills share a single profile, override via `BAOYU_CHROME_PROFILE_DIR` env var. Platform paths: [docs/chrome-profile.md](docs/chrome-profile.md)
- **Image generation APIs**: `baoyu-image-gen` requires API key (OpenAI, Google, OpenRouter, DashScope, or Replicate) configured in EXTEND.md
+- **Image generation APIs**: `baoyu-imagine` requires API key (OpenAI, Azure OpenAI, Google, OpenRouter, DashScope, or Replicate) configured in EXTEND.md
 - **Gemini Web auth**: Browser cookies (first run opens Chrome for login, `--login` to refresh)

 ## Security
@@ -46,10 +46,16 @@ Execute: `${BUN_X} skills/<skill>/scripts/main.ts [options]`
 | Rule | Description |
 |------|-------------|
 | **Load project skills first** | Project skills override system/user-level skills with same name |
-| **Default image generation** | Use `skills/baoyu-image-gen/SKILL.md` unless user specifies otherwise |
+| **Default image generation** | Use `skills/baoyu-imagine/SKILL.md` unless user specifies otherwise |

 Priority: project `skills/` → `$HOME/.baoyu-skills/` → system-level.

+## Deprecated Skills
+
+| Skill | Note |
+|-------|------|
+| `baoyu-image-gen` | Migrated to `baoyu-imagine`. Do NOT add to `.claude-plugin/marketplace.json`. Do NOT update README for this skill. |
+
 ## Release Process

 Use `/release-skills` workflow. Never skip:
@@ -32,7 +32,7 @@ This repository now supports publishing each `skills/baoyu-*` directory as an in
 ClawHub installs skills individually, not as one marketplace bundle. After publishing, users can install specific skills such as:

 ```bash
-clawhub install baoyu-image-gen
+clawhub install baoyu-imagine
 clawhub install baoyu-markdown-to-html
 ```

@@ -52,16 +52,14 @@ Run the following command in Claude Code:

 1. Select **Browse and install plugins**
 2. Select **baoyu-skills**
-3. Select the plugin(s) you want to install
+3. Select the **baoyu-skills** plugin
 4. Select **Install now**

 **Option 2: Direct Install**

 ```bash
-# Install specific plugin
-/plugin install content-skills@baoyu-skills
-/plugin install ai-generation-skills@baoyu-skills
-/plugin install utility-skills@baoyu-skills
+# Install the marketplace's single plugin
+/plugin install baoyu-skills@baoyu-skills
 ```

 **Option 3: Ask the Agent**
@@ -70,13 +68,13 @@ Simply tell Claude Code:

 > Please install Skills from github.com/JimLiu/baoyu-skills

-### Available Plugins
+### Available Plugin

-| Plugin | Description | Skills |
-|--------|-------------|--------|
-| **content-skills** | Content generation and publishing | [xhs-images](#baoyu-xhs-images), [infographic](#baoyu-infographic), [cover-image](#baoyu-cover-image), [slide-deck](#baoyu-slide-deck), [comic](#baoyu-comic), [article-illustrator](#baoyu-article-illustrator), [post-to-x](#baoyu-post-to-x), [post-to-wechat](#baoyu-post-to-wechat), [post-to-weibo](#baoyu-post-to-weibo) |
-| **ai-generation-skills** | AI-powered generation backends | [image-gen](#baoyu-image-gen), [danger-gemini-web](#baoyu-danger-gemini-web) |
-| **utility-skills** | Utility tools for content processing | [url-to-markdown](#baoyu-url-to-markdown), [danger-x-to-markdown](#baoyu-danger-x-to-markdown), [compress-image](#baoyu-compress-image), [format-markdown](#baoyu-format-markdown), [markdown-to-html](#baoyu-markdown-to-html), [translate](#baoyu-translate) |
+The marketplace now exposes a single plugin so each skill is registered exactly once.
+
+| Plugin | Description | Includes |
+|--------|-------------|----------|
+| **baoyu-skills** | Content generation, AI backends, and utility tools for daily work efficiency | All skills in this repository, organized below as Content Skills, AI Generation Skills, and Utility Skills |

 ## Update Skills

@@ -663,40 +661,58 @@ Post content to Weibo (微博). Supports regular posts with text, images, and vi

 AI-powered generation backends.

-#### baoyu-image-gen
+#### baoyu-imagine

-AI SDK-based image generation using OpenAI, Google, OpenRouter, DashScope (Aliyun Tongyi Wanxiang), Jimeng (即梦), Seedream (豆包), and Replicate APIs. Supports text-to-image, reference images, aspect ratios, and quality presets.
+AI SDK-based image generation using OpenAI, Azure OpenAI, Google, OpenRouter, DashScope (Aliyun Tongyi Wanxiang), MiniMax, Jimeng (即梦), Seedream (豆包), and Replicate APIs. Supports text-to-image, reference images, aspect ratios, custom sizes, batch generation, and quality presets.

 ```bash
 # Basic generation (auto-detect provider)
-/baoyu-image-gen --prompt "A cute cat" --image cat.png
+/baoyu-imagine --prompt "A cute cat" --image cat.png

 # With aspect ratio
-/baoyu-image-gen --prompt "A landscape" --image landscape.png --ar 16:9
+/baoyu-imagine --prompt "A landscape" --image landscape.png --ar 16:9

 # High quality (2k)
-/baoyu-image-gen --prompt "A banner" --image banner.png --quality 2k
+/baoyu-imagine --prompt "A banner" --image banner.png --quality 2k

 # Specific provider
-/baoyu-image-gen --prompt "A cat" --image cat.png --provider openai
+/baoyu-imagine --prompt "A cat" --image cat.png --provider openai
+
+# Azure OpenAI (model = deployment name)
+/baoyu-imagine --prompt "A cat" --image cat.png --provider azure --model gpt-image-1.5

 # OpenRouter
-/baoyu-image-gen --prompt "A cat" --image cat.png --provider openrouter
+/baoyu-imagine --prompt "A cat" --image cat.png --provider openrouter
+
+# OpenRouter with reference images
+/baoyu-imagine --prompt "Make it blue" --image out.png --provider openrouter --model google/gemini-3.1-flash-image-preview --ref source.png

 # DashScope (Aliyun Tongyi Wanxiang)
-/baoyu-image-gen --prompt "一只可爱的猫" --image cat.png --provider dashscope
+/baoyu-imagine --prompt "一只可爱的猫" --image cat.png --provider dashscope
+
+# DashScope with custom size
+/baoyu-imagine --prompt "为咖啡品牌设计一张 21:9 横幅海报，包含清晰中文标题" --image banner.png --provider dashscope --model qwen-image-2.0-pro --size 2048x872
+
+# MiniMax
+/baoyu-imagine --prompt "A fashion editorial portrait by a bright studio window" --image out.jpg --provider minimax
+
+# MiniMax with subject reference
+/baoyu-imagine --prompt "A girl stands by the library window, cinematic lighting" --image out.jpg --provider minimax --model image-01 --ref portrait.png --ar 16:9

 # Replicate
-/baoyu-image-gen --prompt "A cat" --image cat.png --provider replicate
+/baoyu-imagine --prompt "A cat" --image cat.png --provider replicate

 # Jimeng (即梦)
-/baoyu-image-gen --prompt "一只可爱的猫" --image cat.png --provider jimeng
+/baoyu-imagine --prompt "一只可爱的猫" --image cat.png --provider jimeng

 # Seedream (豆包)
-/baoyu-image-gen --prompt "一只可爱的猫" --image cat.png --provider seedream
+/baoyu-imagine --prompt "一只可爱的猫" --image cat.png --provider seedream

-# With reference images (Google, OpenAI, OpenRouter, Replicate, or Seedream 5.0/4.5/4.0)
-/baoyu-image-gen --prompt "Make it blue" --image out.png --ref source.png
+# With reference images (Google, OpenAI, Azure OpenAI, OpenRouter, Replicate, MiniMax, or Seedream 5.0/4.5/4.0)
+/baoyu-imagine --prompt "Make it blue" --image out.png --ref source.png
+
+# Batch mode
+/baoyu-imagine --batchfile batch.json --jobs 4 --json
 ```

 **Options**:
@@ -705,44 +721,73 @@ AI SDK-based image generation using OpenAI, Google, OpenRouter, DashScope (Aliyu
 | `--prompt`, `-p` | Prompt text |
 | `--promptfiles` | Read prompt from files (concatenated) |
 | `--image` | Output image path (required) |
-| `--provider` | `google`, `openai`, `openrouter`, `dashscope`, `jimeng`, `seedream` or `replicate` (default: auto-detect; prefers google) |
-| `--model`, `-m` | Model ID |
+| `--batchfile` | JSON batch file for multi-image generation |
+| `--jobs` | Worker count for batch mode |
+| `--provider` | `google`, `openai`, `azure`, `openrouter`, `dashscope`, `minimax`, `jimeng`, `seedream`, or `replicate` |
+| `--model`, `-m` | Model ID or deployment name. Azure uses deployment name; OpenRouter uses full model IDs; MiniMax uses `image-01` / `image-01-live` |
 | `--ar` | Aspect ratio (e.g., `16:9`, `1:1`, `4:3`) |
 | `--size` | Size (e.g., `1024x1024`) |
 | `--quality` | `normal` or `2k` (default: `2k`) |
-| `--ref` | Reference images (Google, OpenAI, OpenRouter, Replicate, or Seedream 5.0/4.5/4.0) |
+| `--imageSize` | `1K`, `2K`, or `4K` for Google/OpenRouter |
+| `--ref` | Reference images (Google, OpenAI, Azure OpenAI, OpenRouter, Replicate, MiniMax, or Seedream 5.0/4.5/4.0) |
+| `--n` | Number of images per request |
+| `--json` | JSON output |

 **Environment Variables** (see [Environment Configuration](#environment-configuration) for setup):
 | Variable | Description | Default |
 |----------|-------------|---------|
 | `OPENAI_API_KEY` | OpenAI API key | - |
+| `AZURE_OPENAI_API_KEY` | Azure OpenAI API key | - |
 | `OPENROUTER_API_KEY` | OpenRouter API key | - |
 | `GOOGLE_API_KEY` | Google API key | - |
+| `GEMINI_API_KEY` | Alias for `GOOGLE_API_KEY` | - |
 | `DASHSCOPE_API_KEY` | DashScope API key (Aliyun) | - |
+| `MINIMAX_API_KEY` | MiniMax API key | - |
 | `REPLICATE_API_TOKEN` | Replicate API token | - |
 | `JIMENG_ACCESS_KEY_ID` | Jimeng Volcengine access key | - |
 | `JIMENG_SECRET_ACCESS_KEY` | Jimeng Volcengine secret key | - |
 | `ARK_API_KEY` | Seedream Volcengine ARK API key | - |
 | `OPENAI_IMAGE_MODEL` | OpenAI model | `gpt-image-1.5` |
+| `AZURE_OPENAI_DEPLOYMENT` | Azure default deployment name | - |
+| `AZURE_OPENAI_IMAGE_MODEL` | Backward-compatible Azure deployment/model alias | `gpt-image-1.5` |
 | `OPENROUTER_IMAGE_MODEL` | OpenRouter model | `google/gemini-3.1-flash-image-preview` |
 | `GOOGLE_IMAGE_MODEL` | Google model | `gemini-3-pro-image-preview` |
 | `DASHSCOPE_IMAGE_MODEL` | DashScope model | `qwen-image-2.0-pro` |
+| `MINIMAX_IMAGE_MODEL` | MiniMax model | `image-01` |
 | `REPLICATE_IMAGE_MODEL` | Replicate model | `google/nano-banana-pro` |
 | `JIMENG_IMAGE_MODEL` | Jimeng model | `jimeng_t2i_v40` |
 | `SEEDREAM_IMAGE_MODEL` | Seedream model | `doubao-seedream-5-0-260128` |
 | `OPENAI_BASE_URL` | Custom OpenAI endpoint | - |
+| `OPENAI_IMAGE_USE_CHAT` | Use `/chat/completions` for OpenAI image generation | `false` |
+| `AZURE_OPENAI_BASE_URL` | Azure resource or deployment endpoint | - |
+| `AZURE_API_VERSION` | Azure image API version | `2025-04-01-preview` |
 | `OPENROUTER_BASE_URL` | Custom OpenRouter endpoint | `https://openrouter.ai/api/v1` |
+| `OPENROUTER_HTTP_REFERER` | Optional app/site URL for OpenRouter attribution | - |
+| `OPENROUTER_TITLE` | Optional app name for OpenRouter attribution | - |
 | `GOOGLE_BASE_URL` | Custom Google endpoint | - |
 | `DASHSCOPE_BASE_URL` | Custom DashScope endpoint | - |
+| `MINIMAX_BASE_URL` | Custom MiniMax endpoint | `https://api.minimax.io` |
 | `REPLICATE_BASE_URL` | Custom Replicate endpoint | - |
 | `JIMENG_BASE_URL` | Custom Jimeng endpoint | `https://visual.volcengineapi.com` |
 | `JIMENG_REGION` | Jimeng region | `cn-north-1` |
 | `SEEDREAM_BASE_URL` | Custom Seedream endpoint | `https://ark.cn-beijing.volces.com/api/v3` |
+| `BAOYU_IMAGE_GEN_MAX_WORKERS` | Override batch worker cap | `10` |
+| `BAOYU_IMAGE_GEN_<PROVIDER>_CONCURRENCY` | Override provider concurrency | provider-specific |
+| `BAOYU_IMAGE_GEN_<PROVIDER>_START_INTERVAL_MS` | Override provider request start gap | provider-specific |
+
+**Provider Notes**:
+- Azure OpenAI: `--model` means Azure deployment name, not the underlying model family.
+- DashScope: `qwen-image-2.0-pro` is the recommended default for custom `--size`, `21:9`, and strong Chinese/English text rendering.
+- MiniMax: `image-01` supports documented custom `width` / `height`; `image-01-live` is lower latency and works best with `--ar`.
+- MiniMax reference images are sent as `subject_reference`; the current API is specialized toward character / portrait consistency.
+- Jimeng does not support reference images.
+- Seedream reference images are supported by Seedream 5.0 / 4.5 / 4.0, not Seedream 3.0.

 **Provider Auto-Selection**:
-1. If `--provider` specified → use it
-2. If only one API key available → use that provider
-3. If multiple available → default to Google
+1. If `--provider` is specified → use it
+2. If `--ref` is provided and no provider is specified → try Google, then OpenAI, Azure, OpenRouter, Replicate, Seedream, and finally MiniMax
+3. If only one API key is available → use that provider
+4. If multiple providers are available → default to Google

 #### baoyu-danger-gemini-web

@@ -766,6 +811,40 @@ Interacts with Gemini Web to generate text and images.

 Utility tools for content processing.

+#### baoyu-youtube-transcript
+
+Download YouTube video transcripts/subtitles and cover images. Supports multiple languages, translation, chapters, and speaker identification. Caches raw data for fast re-formatting.
+
+```bash
+# Default: markdown with timestamps
+/baoyu-youtube-transcript https://www.youtube.com/watch?v=VIDEO_ID
+
+# Specify languages (priority order)
+/baoyu-youtube-transcript https://youtu.be/VIDEO_ID --languages zh,en,ja
+
+# With chapters and speaker identification
+/baoyu-youtube-transcript https://youtu.be/VIDEO_ID --chapters --speakers
+
+# SRT subtitle format
+/baoyu-youtube-transcript https://youtu.be/VIDEO_ID --format srt
+
+# List available transcripts
+/baoyu-youtube-transcript https://youtu.be/VIDEO_ID --list
+```
+
+**Options**:
+| Option | Description | Default |
+|--------|-------------|---------|
+| `<url-or-id>` | YouTube URL or video ID | Required |
+| `--languages <codes>` | Language codes, comma-separated | `en` |
+| `--format <fmt>` | Output format: `text`, `srt` | `text` |
+| `--translate <code>` | Translate to specified language | |
+| `--chapters` | Chapter segmentation from video description | |
+| `--speakers` | Speaker identification (requires AI post-processing) | |
+| `--no-timestamps` | Disable timestamps | |
+| `--list` | List available transcripts | |
+| `--refresh` | Force re-fetch, ignore cache | |
+
 #### baoyu-url-to-markdown

 Fetch any URL via Chrome CDP and convert to clean markdown. Saves rendered HTML snapshot alongside the markdown, and automatically falls back to a legacy extractor when Defuddle fails.
@@ -966,7 +1045,7 @@ Custom style descriptions are also accepted, e.g., `--style "poetic and lyrical"
 Some skills require API keys or custom configuration. Environment variables can be set in `.env` files:

 **Load Priority** (higher priority overrides lower):
-1. CLI environment variables (e.g., `OPENAI_API_KEY=xxx /baoyu-image-gen ...`)
+1. CLI environment variables (e.g., `OPENAI_API_KEY=xxx /baoyu-imagine ...`)
 2. `process.env` (system environment)
 3. `<cwd>/.baoyu-skills/.env` (project-level)
 4. `~/.baoyu-skills/.env` (user-level)
@@ -983,11 +1062,20 @@ cat > ~/.baoyu-skills/.env << 'EOF'
 OPENAI_API_KEY=sk-xxx
 OPENAI_IMAGE_MODEL=gpt-image-1.5
 # OPENAI_BASE_URL=https://api.openai.com/v1
+# OPENAI_IMAGE_USE_CHAT=false
+
+# Azure OpenAI
+AZURE_OPENAI_API_KEY=xxx
+AZURE_OPENAI_BASE_URL=https://your-resource.openai.azure.com
+AZURE_OPENAI_DEPLOYMENT=gpt-image-1.5
+# AZURE_API_VERSION=2025-04-01-preview

 # OpenRouter
 OPENROUTER_API_KEY=sk-or-xxx
 OPENROUTER_IMAGE_MODEL=google/gemini-3.1-flash-image-preview
 # OPENROUTER_BASE_URL=https://openrouter.ai/api/v1
+# OPENROUTER_HTTP_REFERER=https://your-app.example.com
+# OPENROUTER_TITLE=Your App Name

 # Google
 GOOGLE_API_KEY=xxx
@@ -999,6 +1087,11 @@ DASHSCOPE_API_KEY=sk-xxx
 DASHSCOPE_IMAGE_MODEL=qwen-image-2.0-pro
 # DASHSCOPE_BASE_URL=https://dashscope.aliyuncs.com/api/v1

+# MiniMax
+MINIMAX_API_KEY=xxx
+MINIMAX_IMAGE_MODEL=image-01
+# MINIMAX_BASE_URL=https://api.minimax.io
+
 # Replicate
 REPLICATE_API_TOKEN=r8_xxx
 REPLICATE_IMAGE_MODEL=google/nano-banana-pro
@@ -32,7 +32,7 @@ npx skills add jimliu/baoyu-skills
 ClawHub 按“单个 skill”安装，不是把整个 marketplace 一次性装进去。发布后，用户可以按需安装：

 ```bash
-clawhub install baoyu-image-gen
+clawhub install baoyu-imagine
 clawhub install baoyu-markdown-to-html
 ```

@@ -52,16 +52,14 @@ clawhub install baoyu-markdown-to-html

 1. 选择 **Browse and install plugins**
 2. 选择 **baoyu-skills**
-3. 选择要安装的插件
+3. 选择 **baoyu-skills** 插件
 4. 选择 **Install now**

 **方式二：直接安装**

 ```bash
-# 安装指定插件
-/plugin install content-skills@baoyu-skills
-/plugin install ai-generation-skills@baoyu-skills
-/plugin install utility-skills@baoyu-skills
+# 安装 marketplace 中唯一的插件
+/plugin install baoyu-skills@baoyu-skills
 ```

 **方式三：告诉 Agent**
@@ -72,11 +70,11 @@ clawhub install baoyu-markdown-to-html

 ### 可用插件

-| 插件 | 说明 | 包含技能 |
+现在 marketplace 只暴露一个插件，这样每个 skill 只会注册一次。
+
+| 插件 | 说明 | 包含内容 |
 |------|------|----------|
-| **content-skills** | 内容生成和发布 | [xhs-images](#baoyu-xhs-images), [infographic](#baoyu-infographic), [cover-image](#baoyu-cover-image), [slide-deck](#baoyu-slide-deck), [comic](#baoyu-comic), [article-illustrator](#baoyu-article-illustrator), [post-to-x](#baoyu-post-to-x), [post-to-wechat](#baoyu-post-to-wechat), [post-to-weibo](#baoyu-post-to-weibo) |
-| **ai-generation-skills** | AI 生成后端 | [image-gen](#baoyu-image-gen), [danger-gemini-web](#baoyu-danger-gemini-web) |
-| **utility-skills** | 内容处理工具 | [url-to-markdown](#baoyu-url-to-markdown), [danger-x-to-markdown](#baoyu-danger-x-to-markdown), [compress-image](#baoyu-compress-image), [format-markdown](#baoyu-format-markdown), [markdown-to-html](#baoyu-markdown-to-html), [translate](#baoyu-translate) |
+| **baoyu-skills** | 提供内容生成、AI 后端和日常效率工具技能 | 仓库中的全部 skills，仍按下方的内容技能、AI 生成技能、工具技能三个分类展示 |

 ## 更新技能

@@ -663,40 +661,58 @@ accounts:

 AI 驱动的生成后端。

-#### baoyu-image-gen
+#### baoyu-imagine

-基于 AI SDK 的图像生成，支持 OpenAI、Google、OpenRouter、DashScope（阿里通义万相）、即梦（Jimeng）、豆包（Seedream）和 Replicate API。支持文生图、参考图、宽高比和质量预设。
+基于 AI SDK 的图像生成，支持 OpenAI、Azure OpenAI、Google、OpenRouter、DashScope（阿里通义万相）、MiniMax、即梦（Jimeng）、豆包（Seedream）和 Replicate API。支持文生图、参考图、宽高比、自定义尺寸、批量生成和质量预设。

 ```bash
 # 基础生成（自动检测服务商）
-/baoyu-image-gen --prompt "一只可爱的猫" --image cat.png
+/baoyu-imagine --prompt "一只可爱的猫" --image cat.png

 # 指定宽高比
-/baoyu-image-gen --prompt "风景图" --image landscape.png --ar 16:9
+/baoyu-imagine --prompt "风景图" --image landscape.png --ar 16:9

 # 高质量（2k 分辨率）
-/baoyu-image-gen --prompt "横幅图" --image banner.png --quality 2k
+/baoyu-imagine --prompt "横幅图" --image banner.png --quality 2k

 # 指定服务商
-/baoyu-image-gen --prompt "一只猫" --image cat.png --provider openai
+/baoyu-imagine --prompt "一只猫" --image cat.png --provider openai
+
+# Azure OpenAI（model 为部署名称）
+/baoyu-imagine --prompt "一只猫" --image cat.png --provider azure --model gpt-image-1.5

 # OpenRouter
-/baoyu-image-gen --prompt "一只猫" --image cat.png --provider openrouter
+/baoyu-imagine --prompt "一只猫" --image cat.png --provider openrouter
+
+# OpenRouter + 参考图
+/baoyu-imagine --prompt "把它变成蓝色" --image out.png --provider openrouter --model google/gemini-3.1-flash-image-preview --ref source.png

 # DashScope（阿里通义万相）
-/baoyu-image-gen --prompt "一只可爱的猫" --image cat.png --provider dashscope
+/baoyu-imagine --prompt "一只可爱的猫" --image cat.png --provider dashscope
+
+# DashScope 自定义尺寸
+/baoyu-imagine --prompt "为咖啡品牌设计一张 21:9 横幅海报，包含清晰中文标题" --image banner.png --provider dashscope --model qwen-image-2.0-pro --size 2048x872
+
+# MiniMax
+/baoyu-imagine --prompt "A fashion editorial portrait by a bright studio window" --image out.jpg --provider minimax
+
+# MiniMax + 角色参考图
+/baoyu-imagine --prompt "A girl stands by the library window, cinematic lighting" --image out.jpg --provider minimax --model image-01 --ref portrait.png --ar 16:9

 # Replicate
-/baoyu-image-gen --prompt "一只猫" --image cat.png --provider replicate
+/baoyu-imagine --prompt "一只猫" --image cat.png --provider replicate

 # 即梦（Jimeng）
-/baoyu-image-gen --prompt "一只可爱的猫" --image cat.png --provider jimeng
+/baoyu-imagine --prompt "一只可爱的猫" --image cat.png --provider jimeng

 # 豆包（Seedream）
-/baoyu-image-gen --prompt "一只可爱的猫" --image cat.png --provider seedream
+/baoyu-imagine --prompt "一只可爱的猫" --image cat.png --provider seedream

-# 带参考图（Google、OpenAI、OpenRouter、Replicate 或 Seedream 5.0/4.5/4.0）
-/baoyu-image-gen --prompt "把它变成蓝色" --image out.png --ref source.png
+# 带参考图（Google、OpenAI、Azure OpenAI、OpenRouter、Replicate、MiniMax 或 Seedream 5.0/4.5/4.0）
+/baoyu-imagine --prompt "把它变成蓝色" --image out.png --ref source.png
+
+# 批量模式
+/baoyu-imagine --batchfile batch.json --jobs 4 --json
 ```

 **选项**：
@@ -705,44 +721,73 @@ AI 驱动的生成后端。
 | `--prompt`, `-p` | 提示词文本 |
 | `--promptfiles` | 从文件读取提示词（多文件拼接） |
 | `--image` | 输出图片路径（必需） |
-| `--provider` | `google`、`openai`、`openrouter`、`dashscope`、`jimeng`、`seedream` 或 `replicate`（默认：自动检测，优先 google） |
-| `--model`, `-m` | 模型 ID |
+| `--batchfile` | 多图批量生成的 JSON 文件 |
+| `--jobs` | 批量模式的并发 worker 数 |
+| `--provider` | `google`、`openai`、`azure`、`openrouter`、`dashscope`、`minimax`、`jimeng`、`seedream` 或 `replicate` |
+| `--model`, `-m` | 模型 ID 或部署名。Azure 使用部署名；OpenRouter 使用完整模型 ID；MiniMax 使用 `image-01` / `image-01-live` |
 | `--ar` | 宽高比（如 `16:9`、`1:1`、`4:3`） |
 | `--size` | 尺寸（如 `1024x1024`） |
 | `--quality` | `normal` 或 `2k`（默认：`2k`） |
-| `--ref` | 参考图片（Google、OpenAI、OpenRouter、Replicate 或 Seedream 5.0/4.5/4.0） |
+| `--imageSize` | Google/OpenRouter 使用的 `1K`、`2K`、`4K` |
+| `--ref` | 参考图片（Google、OpenAI、Azure OpenAI、OpenRouter、Replicate、MiniMax 或 Seedream 5.0/4.5/4.0） |
+| `--n` | 单次请求生成图片数量 |
+| `--json` | 输出 JSON 结果 |

 **环境变量**（配置方法见[环境配置](#环境配置)）：
 | 变量 | 说明 | 默认值 |
 |------|------|--------|
 | `OPENAI_API_KEY` | OpenAI API 密钥 | - |
+| `AZURE_OPENAI_API_KEY` | Azure OpenAI API 密钥 | - |
 | `OPENROUTER_API_KEY` | OpenRouter API 密钥 | - |
 | `GOOGLE_API_KEY` | Google API 密钥 | - |
+| `GEMINI_API_KEY` | `GOOGLE_API_KEY` 的别名 | - |
 | `DASHSCOPE_API_KEY` | DashScope API 密钥（阿里云） | - |
+| `MINIMAX_API_KEY` | MiniMax API 密钥 | - |
 | `REPLICATE_API_TOKEN` | Replicate API Token | - |
 | `JIMENG_ACCESS_KEY_ID` | 即梦火山引擎 Access Key | - |
 | `JIMENG_SECRET_ACCESS_KEY` | 即梦火山引擎 Secret Key | - |
 | `ARK_API_KEY` | 豆包火山引擎 ARK API 密钥 | - |
 | `OPENAI_IMAGE_MODEL` | OpenAI 模型 | `gpt-image-1.5` |
+| `AZURE_OPENAI_DEPLOYMENT` | Azure 默认部署名 | - |
+| `AZURE_OPENAI_IMAGE_MODEL` | 兼容旧配置的 Azure 部署/模型别名 | `gpt-image-1.5` |
 | `OPENROUTER_IMAGE_MODEL` | OpenRouter 模型 | `google/gemini-3.1-flash-image-preview` |
 | `GOOGLE_IMAGE_MODEL` | Google 模型 | `gemini-3-pro-image-preview` |
 | `DASHSCOPE_IMAGE_MODEL` | DashScope 模型 | `qwen-image-2.0-pro` |
+| `MINIMAX_IMAGE_MODEL` | MiniMax 模型 | `image-01` |
 | `REPLICATE_IMAGE_MODEL` | Replicate 模型 | `google/nano-banana-pro` |
 | `JIMENG_IMAGE_MODEL` | 即梦模型 | `jimeng_t2i_v40` |
 | `SEEDREAM_IMAGE_MODEL` | 豆包模型 | `doubao-seedream-5-0-260128` |
 | `OPENAI_BASE_URL` | 自定义 OpenAI 端点 | - |
+| `OPENAI_IMAGE_USE_CHAT` | OpenAI 改走 `/chat/completions` | `false` |
+| `AZURE_OPENAI_BASE_URL` | Azure 资源或部署端点 | - |
+| `AZURE_API_VERSION` | Azure 图像 API 版本 | `2025-04-01-preview` |
 | `OPENROUTER_BASE_URL` | 自定义 OpenRouter 端点 | `https://openrouter.ai/api/v1` |
+| `OPENROUTER_HTTP_REFERER` | OpenRouter 归因用站点 URL | - |
+| `OPENROUTER_TITLE` | OpenRouter 归因用应用名 | - |
 | `GOOGLE_BASE_URL` | 自定义 Google 端点 | - |
 | `DASHSCOPE_BASE_URL` | 自定义 DashScope 端点 | - |
+| `MINIMAX_BASE_URL` | 自定义 MiniMax 端点 | `https://api.minimax.io` |
 | `REPLICATE_BASE_URL` | 自定义 Replicate 端点 | - |
 | `JIMENG_BASE_URL` | 自定义即梦端点 | `https://visual.volcengineapi.com` |
 | `JIMENG_REGION` | 即梦区域 | `cn-north-1` |
 | `SEEDREAM_BASE_URL` | 自定义豆包端点 | `https://ark.cn-beijing.volces.com/api/v3` |
+| `BAOYU_IMAGE_GEN_MAX_WORKERS` | 批量模式最大 worker 数 | `10` |
+| `BAOYU_IMAGE_GEN_<PROVIDER>_CONCURRENCY` | 覆盖 provider 并发数 | provider 默认值 |
+| `BAOYU_IMAGE_GEN_<PROVIDER>_START_INTERVAL_MS` | 覆盖 provider 请求启动间隔 | provider 默认值 |
+
+**Provider 说明**：
+- Azure OpenAI：`--model` 表示 Azure deployment name，不是底层模型家族名。
+- DashScope：`qwen-image-2.0-pro` 是自定义 `--size`、`21:9` 和中英文排版的推荐默认模型。
+- MiniMax：`image-01` 支持官方文档里的自定义 `width` / `height`；`image-01-live` 更偏低延迟，适合配合 `--ar` 使用。
+- MiniMax 参考图会走 `subject_reference`，当前能力更偏角色 / 人像一致性。
+- 即梦不支持参考图。
+- 豆包参考图能力仅适用于 Seedream 5.0 / 4.5 / 4.0，不适用于 Seedream 3.0。

 **服务商自动选择**：
 1. 如果指定了 `--provider` → 使用指定的
-2. 如果只有一个 API 密钥 → 使用对应服务商
-3. 如果多个可用 → 默认使用 Google
+2. 如果传了 `--ref` 且未指定 provider → 依次尝试 Google、OpenAI、Azure、OpenRouter、Replicate、Seedream，最后是 MiniMax
+3. 如果只有一个 API 密钥 → 使用对应服务商
+4. 如果多个可用 → 默认使用 Google

 #### baoyu-danger-gemini-web

@@ -766,6 +811,40 @@ AI 驱动的生成后端。

 内容处理工具。

+#### baoyu-youtube-transcript
+
+下载 YouTube 视频字幕/转录文本和封面图片。支持多语言、翻译、章节分段和说话人识别。缓存原始数据以便快速重新格式化。
+
+```bash
+# 默认：带时间戳的 Markdown
+/baoyu-youtube-transcript https://www.youtube.com/watch?v=VIDEO_ID
+
+# 指定语言（按优先级排列）
+/baoyu-youtube-transcript https://youtu.be/VIDEO_ID --languages zh,en,ja
+
+# 章节分段 + 说话人识别
+/baoyu-youtube-transcript https://youtu.be/VIDEO_ID --chapters --speakers
+
+# SRT 字幕格式
+/baoyu-youtube-transcript https://youtu.be/VIDEO_ID --format srt
+
+# 列出可用字幕
+/baoyu-youtube-transcript https://youtu.be/VIDEO_ID --list
+```
+
+**选项**：
+| 选项 | 说明 | 默认值 |
+|------|------|--------|
+| `<url-or-id>` | YouTube URL 或视频 ID | 必填 |
+| `--languages <codes>` | 语言代码，逗号分隔 | `en` |
+| `--format <fmt>` | 输出格式：`text`、`srt` | `text` |
+| `--translate <code>` | 翻译为指定语言 | |
+| `--chapters` | 根据视频描述进行章节分段 | |
+| `--speakers` | 说话人识别（需 AI 后处理） | |
+| `--no-timestamps` | 禁用时间戳 | |
+| `--list` | 列出可用字幕 | |
+| `--refresh` | 强制重新获取，忽略缓存 | |
+
 #### baoyu-url-to-markdown

 通过 Chrome CDP 抓取任意 URL 并转换为 Markdown。同时保存渲染后的 HTML 快照，Defuddle 失败时自动回退到旧版提取器。
@@ -966,7 +1045,7 @@ AI 驱动的生成后端。
 部分技能需要 API 密钥或自定义配置。环境变量可以在 `.env` 文件中设置：

 **加载优先级**（高优先级覆盖低优先级）：
-1. 命令行环境变量（如 `OPENAI_API_KEY=xxx /baoyu-image-gen ...`）
+1. 命令行环境变量（如 `OPENAI_API_KEY=xxx /baoyu-imagine ...`）
 2. `process.env`（系统环境变量）
 3. `<cwd>/.baoyu-skills/.env`（项目级）
 4. `~/.baoyu-skills/.env`（用户级）
@@ -983,11 +1062,20 @@ cat > ~/.baoyu-skills/.env << 'EOF'
 OPENAI_API_KEY=sk-xxx
 OPENAI_IMAGE_MODEL=gpt-image-1.5
 # OPENAI_BASE_URL=https://api.openai.com/v1
+# OPENAI_IMAGE_USE_CHAT=false
+
+# Azure OpenAI
+AZURE_OPENAI_API_KEY=xxx
+AZURE_OPENAI_BASE_URL=https://your-resource.openai.azure.com
+AZURE_OPENAI_DEPLOYMENT=gpt-image-1.5
+# AZURE_API_VERSION=2025-04-01-preview

 # OpenRouter
 OPENROUTER_API_KEY=sk-or-xxx
 OPENROUTER_IMAGE_MODEL=google/gemini-3.1-flash-image-preview
 # OPENROUTER_BASE_URL=https://openrouter.ai/api/v1
+# OPENROUTER_HTTP_REFERER=https://your-app.example.com
+# OPENROUTER_TITLE=你的应用名

 # Google
 GOOGLE_API_KEY=xxx
@@ -999,6 +1087,11 @@ DASHSCOPE_API_KEY=sk-xxx
 DASHSCOPE_IMAGE_MODEL=qwen-image-2.0-pro
 # DASHSCOPE_BASE_URL=https://dashscope.aliyuncs.com/api/v1

+# MiniMax
+MINIMAX_API_KEY=xxx
+MINIMAX_IMAGE_MODEL=image-01
+# MINIMAX_BASE_URL=https://api.minimax.io
+
 # Replicate
 REPLICATE_API_TOKEN=r8_xxx
 REPLICATE_IMAGE_MODEL=google/nano-banana-pro
@@ -34,20 +34,22 @@ metadata:
 1. Create `skills/baoyu-<name>/SKILL.md` with YAML front matter
 2. Add TypeScript in `skills/baoyu-<name>/scripts/` (if applicable)
 3. Add prompt templates in `skills/baoyu-<name>/prompts/` if needed
-4. Register in `marketplace.json` under appropriate category
+4. Register the skill in `.claude-plugin/marketplace.json` under the `baoyu-skills` plugin entry
 5. Add Script Directory section to SKILL.md if skill has scripts
 6. Add openclaw metadata to frontmatter

-## Category Selection
+## Skill Grouping

-| If your skill... | Use category |
-|------------------|--------------|
-| Generates visual content (images, slides, comics) | `content-skills` |
-| Publishes to platforms (X, WeChat, Weibo) | `content-skills` |
-| Provides AI generation backend | `ai-generation-skills` |
-| Converts or processes content | `utility-skills` |
+All skills are registered under the single `baoyu-skills` plugin. Use these logical groups when deciding where the skill should appear in the docs:

-New category: add plugin object to `marketplace.json` with `name`, `description`, `skills[]`.
+| If your skill... | Use group |
+|------------------|-----------|
+| Generates visual content (images, slides, comics) | Content Skills |
+| Publishes to platforms (X, WeChat, Weibo) | Content Skills |
+| Provides AI generation backend | AI Generation Skills |
+| Converts or processes content | Utility Skills |
+
+If you add a new logical group, update the docs that present grouped skills, but keep the skill registered under the single `baoyu-skills` plugin entry.

 ## Writing Descriptions

@@ -4,7 +4,7 @@ Skills that require image generation MUST delegate to available image generation

 ## Skill Selection

-**Default**: `skills/baoyu-image-gen/SKILL.md` (unless user specifies otherwise).
+**Default**: `skills/baoyu-imagine/SKILL.md` (unless user specifies otherwise).

 1. Read skill's SKILL.md for parameters and capabilities
 2. If user requests different skill, check `skills/` for alternatives
@@ -16,7 +16,7 @@ Skills that require image generation MUST delegate to available image generation
 ### Step N: Generate Images

 **Skill Selection**:
-1. Check available skills (`baoyu-image-gen` default, or `baoyu-danger-gemini-web`)
+1. Check available skills (`baoyu-imagine` default, or `baoyu-danger-gemini-web`)
 2. Read selected skill's SKILL.md for parameters
 3. If multiple skills available, ask user to choose

@@ -27,7 +27,7 @@ Skills that require image generation MUST delegate to available image generation
 4. On failure, auto-retry once before reporting error
 ```

-**Batch Parallel** (`baoyu-image-gen` only): concurrent workers with per-provider throttling via `batch.max_workers` in EXTEND.md.
+**Batch Parallel** (`baoyu-imagine` only): concurrent workers with per-provider throttling via `batch.max_workers` in EXTEND.md.

 ## Output Path Convention

@@ -9,7 +9,11 @@
        "packages/*"
      ],
      "devDependencies": {
-        "tsx": "^4.20.5"
+        "@mozilla/readability": "^0.6.0",
+        "linkedom": "^0.18.12",
+        "tsx": "^4.20.5",
+        "turndown": "^7.2.2",
+        "turndown-plugin-gfm": "^1.0.2"
      }
    },
    "node_modules/@esbuild/aix-ppc64": {
@@ -454,6 +458,23 @@
        "node": ">=18"
      }
    },
+    "node_modules/@mixmark-io/domino": {
+      "version": "2.2.0",
+      "resolved": "https://registry.npmjs.org/@mixmark-io/domino/-/domino-2.2.0.tgz",
+      "integrity": "sha512-Y28PR25bHXUg88kCV7nivXrP2Nj2RueZ3/l/jdx6J9f8J4nsEGcgX0Qe6lt7Pa+J79+kPiJU3LguR6O/6zrLOw==",
+      "dev": true,
+      "license": "BSD-2-Clause"
+    },
+    "node_modules/@mozilla/readability": {
+      "version": "0.6.0",
+      "resolved": "https://registry.npmjs.org/@mozilla/readability/-/readability-0.6.0.tgz",
+      "integrity": "sha512-juG5VWh4qAivzTAeMzvY9xs9HY5rAcr2E4I7tiSSCokRFi7XIZCAu92ZkSTsIj1OPceCifL3cpfteP3pDT9/QQ==",
+      "dev": true,
+      "license": "Apache-2.0",
+      "engines": {
+        "node": ">=14.0.0"
+      }
+    },
    "node_modules/@types/debug": {
      "version": "4.1.12",
      "resolved": "https://registry.npmjs.org/@types/debug/-/debug-4.1.12.tgz",
@@ -615,6 +636,13 @@
        "url": "https://github.com/sponsors/fb55"
      }
    },
+    "node_modules/cssom": {
+      "version": "0.5.0",
+      "resolved": "https://registry.npmjs.org/cssom/-/cssom-0.5.0.tgz",
+      "integrity": "sha512-iKuQcq+NdHqlAcwUY0o/HL69XQrUaQdMjmStJ8JFmUaiiQErlhrmuigkg/CU4E2J0IyUKUrMAgl36TvN67MqTw==",
+      "dev": true,
+      "license": "MIT"
+    },
    "node_modules/debug": {
      "version": "4.4.3",
      "resolved": "https://registry.npmjs.org/debug/-/debug-4.4.3.tgz",
@@ -896,6 +924,13 @@
        "node": ">=12.0.0"
      }
    },
+    "node_modules/html-escaper": {
+      "version": "3.0.3",
+      "resolved": "https://registry.npmjs.org/html-escaper/-/html-escaper-3.0.3.tgz",
+      "integrity": "sha512-RuMffC89BOWQoY0WKGpIhn5gX3iI54O6nRA0yC124NYVtzjmFWBIiFd8M0x+ZdX0P9R4lADg1mgP8C7PxGOWuQ==",
+      "dev": true,
+      "license": "MIT"
+    },
    "node_modules/htmlparser2": {
      "version": "9.1.0",
      "resolved": "https://registry.npmjs.org/htmlparser2/-/htmlparser2-9.1.0.tgz",
@@ -984,6 +1019,51 @@
        "node": ">=18.17"
      }
    },
+    "node_modules/linkedom": {
+      "version": "0.18.12",
+      "resolved": "https://registry.npmjs.org/linkedom/-/linkedom-0.18.12.tgz",
+      "integrity": "sha512-jalJsOwIKuQJSeTvsgzPe9iJzyfVaEJiEXl+25EkKevsULHvMJzpNqwvj1jOESWdmgKDiXObyjOYwlUqG7wo1Q==",
+      "dev": true,
+      "license": "ISC",
+      "dependencies": {
+        "css-select": "^5.1.0",
+        "cssom": "^0.5.0",
+        "html-escaper": "^3.0.3",
+        "htmlparser2": "^10.0.0",
+        "uhyphen": "^0.2.0"
+      },
+      "engines": {
+        "node": ">=16"
+      },
+      "peerDependencies": {
+        "canvas": ">= 2"
+      },
+      "peerDependenciesMeta": {
+        "canvas": {
+          "optional": true
+        }
+      }
+    },
+    "node_modules/linkedom/node_modules/htmlparser2": {
+      "version": "10.1.0",
+      "resolved": "https://registry.npmjs.org/htmlparser2/-/htmlparser2-10.1.0.tgz",
+      "integrity": "sha512-VTZkM9GWRAtEpveh7MSF6SjjrpNVNNVJfFup7xTY3UpFtm67foy9HDVXneLtFVt4pMz5kZtgNcvCniNFb1hlEQ==",
+      "dev": true,
+      "funding": [
+        "https://github.com/fb55/htmlparser2?sponsor=1",
+        {
+          "type": "github",
+          "url": "https://github.com/sponsors/fb55"
+        }
+      ],
+      "license": "MIT",
+      "dependencies": {
+        "domelementtype": "^2.3.0",
+        "domhandler": "^5.0.3",
+        "domutils": "^3.2.2",
+        "entities": "^7.0.1"
+      }
+    },
    "node_modules/longest-streak": {
      "version": "3.1.0",
      "resolved": "https://registry.npmjs.org/longest-streak/-/longest-streak-3.1.0.tgz",
@@ -1768,6 +1848,30 @@
        "fsevents": "~2.3.3"
      }
    },
+    "node_modules/turndown": {
+      "version": "7.2.2",
+      "resolved": "https://registry.npmjs.org/turndown/-/turndown-7.2.2.tgz",
+      "integrity": "sha512-1F7db8BiExOKxjSMU2b7if62D/XOyQyZbPKq/nUwopfgnHlqXHqQ0lvfUTeUIr1lZJzOPFn43dODyMSIfvWRKQ==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "@mixmark-io/domino": "^2.2.0"
+      }
+    },
+    "node_modules/turndown-plugin-gfm": {
+      "version": "1.0.2",
+      "resolved": "https://registry.npmjs.org/turndown-plugin-gfm/-/turndown-plugin-gfm-1.0.2.tgz",
+      "integrity": "sha512-vwz9tfvF7XN/jE0dGoBei3FXWuvll78ohzCZQuOb+ZjWrs3a0XhQVomJEb2Qh4VHTPNRO4GPZh0V7VRbiWwkRg==",
+      "dev": true,
+      "license": "MIT"
+    },
+    "node_modules/uhyphen": {
+      "version": "0.2.0",
+      "resolved": "https://registry.npmjs.org/uhyphen/-/uhyphen-0.2.0.tgz",
+      "integrity": "sha512-qz3o9CHXmJJPGBdqzab7qAYuW8kQGKNEuoHFYrBwV6hWIMcpAmxDLXojcHfFr9US1Pe6zUswEIJIbLI610fuqA==",
+      "dev": true,
+      "license": "ISC"
+    },
    "node_modules/undici": {
      "version": "6.24.0",
      "resolved": "https://registry.npmjs.org/undici/-/undici-6.24.0.tgz",
@@ -10,6 +10,10 @@
    "test:coverage": "node --import tsx --experimental-test-coverage --test"
  },
  "devDependencies": {
+    "@mozilla/readability": "^0.6.0",
+    "linkedom": "^0.18.12",
+    "turndown": "^7.2.2",
+    "turndown-plugin-gfm": "^1.0.2",
    "tsx": "^4.20.5"
  }
 }
@@ -9,12 +9,26 @@ import { COLOR_PRESETS, FONT_FAMILY_MAP } from "./constants.ts";
 import {
  buildMarkdownDocumentMeta,
  formatTimestamp,
+  renderMarkdownDocument,
  resolveColorToken,
  resolveFontFamilyToken,
  resolveMarkdownStyle,
  resolveRenderOptions,
 } from "./document.ts";

+function escapeRegExp(value: string): string {
+  return value.replace(/[.*+?^${}()|[\]\\]/g, `\\$&`);
+}
+
+function findInlineStyle(html: string, tagName: string, text: string): string {
+  const pattern = new RegExp(
+    `<${tagName}[^>]*style="([^"]*)"[^>]*>${escapeRegExp(text)}</${tagName}>`,
+  );
+  const match = html.match(pattern);
+  assert.ok(match, `Expected inline style for <${tagName}>${text}</${tagName}>`);
+  return match![1]!;
+}
+
 function useCwd(t: TestContext, cwd: string): void {
  const previous = process.cwd();
  process.chdir(cwd);
@@ -138,3 +152,23 @@ keep_title: true
  assert.equal(explicit.fontSize, "18px");
  assert.equal(explicit.keepTitle, false);
 });
+
+test("renderMarkdownDocument layers default rules into grace theme before CSS inlining", async () => {
+  const { html } = await renderMarkdownDocument(
+    `## Section\n\nParagraph with **bold** text.`,
+    { keepTitle: true, theme: "grace" },
+  );
+
+  const h2Style = findInlineStyle(html, "h2", "Section");
+  assert.match(h2Style, /background: #92617E/);
+  assert.match(h2Style, /box-shadow: 0 4px 6px rgba\(0, 0, 0, 0\.1\)/);
+
+  const pMatch = html.match(/<p[^>]*style="([^"]*)"[^>]*>/);
+  assert.ok(pMatch, "Expected inline style on <p> tag");
+  assert.match(pMatch![1]!, /color:/);
+
+  const strongPattern = /<strong[^>]*style="([^"]*)"[^>]*>bold<\/strong>/;
+  const strongMatch = html.match(strongPattern);
+  assert.ok(strongMatch, "Expected inline style for <strong>bold</strong>");
+  assert.match(strongMatch![1]!, /font-weight:/);
+});
@@ -59,6 +59,17 @@ test("normalizeCssText and normalizeInlineCss replace variables and strip declar
  assert.doesNotMatch(normalizedHtml, /var\(--md-primary-color\)/);
 });

+test("normalizeInlineCss removes quoted custom property values without leaving fragments behind", () => {
+  const normalizedHtml = normalizeInlineCss(
+    `<html style="--md-font-family: Menlo, Monaco, 'Courier New', monospace; color: var(--md-primary-color)"></html>`,
+    DEFAULT_STYLE,
+  );
+
+  assert.match(normalizedHtml, /style=" color: #0F4C81"/);
+  assert.doesNotMatch(normalizedHtml, /Courier New/);
+  assert.doesNotMatch(normalizedHtml, /--md-font-family/);
+});
+
 test("HTML structure helpers hoist nested lists and remove the first heading", () => {
  const nestedList = `<ul><li>Parent<ul><li>Child</li></ul></li></ul>`;
  assert.equal(
@@ -100,13 +100,13 @@ export function normalizeCssText(cssText: string, style: StyleConfig = DEFAULT_S
    .replace(/var\(--md-accent-color\)/g, style.accentColor)
    .replace(/var\(--md-container-bg\)/g, style.containerBg)
    .replace(/hsl\(var\(--foreground\)\)/g, "#3f3f3f")
-    .replace(/--md-primary-color:\s*[^;"']+;?/g, "")
-    .replace(/--md-font-family:\s*[^;"']+;?/g, "")
-    .replace(/--md-font-size:\s*[^;"']+;?/g, "")
-    .replace(/--blockquote-background:\s*[^;"']+;?/g, "")
-    .replace(/--md-accent-color:\s*[^;"']+;?/g, "")
-    .replace(/--md-container-bg:\s*[^;"']+;?/g, "")
-    .replace(/--foreground:\s*[^;"']+;?/g, "");
+    .replace(/--md-primary-color:\s*[^;]+;?/g, "")
+    .replace(/--md-font-family:\s*[^;]+;?/g, "")
+    .replace(/--md-font-size:\s*[^;]+;?/g, "")
+    .replace(/--blockquote-background:\s*[^;]+;?/g, "")
+    .replace(/--md-accent-color:\s*[^;]+;?/g, "")
+    .replace(/--md-container-bg:\s*[^;]+;?/g, "")
+    .replace(/--foreground:\s*[^;]+;?/g, "");
 }

 export function normalizeInlineCss(html: string, style: StyleConfig = DEFAULT_STYLE): string {
@@ -6,6 +6,7 @@ import type { ThemeName } from "./types.js";
 const SCRIPT_DIR = path.dirname(fileURLToPath(import.meta.url));
 export const THEME_DIR = path.resolve(SCRIPT_DIR, "themes");
 const FALLBACK_THEMES: ThemeName[] = ["default", "grace", "simple"];
+const THEMES_EXTENDING_DEFAULT = new Set<ThemeName>(["grace", "simple"]);

 function stripOutputScope(cssContent: string): string {
  let css = cssContent;
@@ -41,6 +42,7 @@ export function loadThemeCss(theme: ThemeName): {
  themeCss: string;
 } {
  const basePath = path.join(THEME_DIR, "base.css");
+  const defaultThemePath = path.join(THEME_DIR, "default.css");
  const themePath = path.join(THEME_DIR, `${theme}.css`);

  if (!fs.existsSync(basePath)) {
@@ -51,9 +53,18 @@ export function loadThemeCss(theme: ThemeName): {
    throw new Error(`Missing theme CSS for "${theme}": ${themePath}`);
  }

+  const layeredThemeCss: string[] = [];
+  if (theme !== "default" && THEMES_EXTENDING_DEFAULT.has(theme)) {
+    if (!fs.existsSync(defaultThemePath)) {
+      throw new Error(`Missing default theme CSS: ${defaultThemePath}`);
+    }
+    layeredThemeCss.push(fs.readFileSync(defaultThemePath, "utf-8"));
+  }
+  layeredThemeCss.push(fs.readFileSync(themePath, "utf-8"));
+
  return {
    baseCss: fs.readFileSync(basePath, "utf-8"),
-    themeCss: fs.readFileSync(themePath, "utf-8"),
+    themeCss: layeredThemeCss.join("\n"),
  };
 }

@@ -151,6 +151,9 @@ async function main() {
    .map((tag) => tag.trim())
    .filter(Boolean);

+  let succeeded = 0;
+  const failed = [];
+
  for (const candidate of actionable) {
    const version =
      candidate.status === "new"
@@ -158,20 +161,30 @@ async function main() {
        : bumpSemver(candidate.latestVersion, options.bump);

    console.log(`Publishing ${candidate.slug}@${version}`);
-    const files = await listTextFiles(candidate.folder);
-    await publishSkill({
-      registry,
-      token: config.token,
-      skill: candidate,
-      files,
-      version,
-      changelog: options.changelog,
-      tags,
-    });
+    try {
+      const files = await listTextFiles(candidate.folder);
+      await publishSkill({
+        registry,
+        token: config.token,
+        skill: candidate,
+        files,
+        version,
+        changelog: options.changelog,
+        tags,
+      });
+      succeeded++;
+    } catch (err) {
+      const msg = err instanceof Error ? err.message : String(err);
+      console.error(`SKIPPED ${candidate.slug}: ${msg}`);
+      failed.push(candidate.slug);
+    }
  }

  console.log("");
-  console.log(`Uploaded ${actionable.length} skill(s).`);
+  console.log(`Uploaded ${succeeded}/${actionable.length} skill(s).`);
+  if (failed.length > 0) {
+    console.log(`Failed (${failed.length}): ${failed.join(", ")}`);
+  }
 }

 function parseArgs(argv) {
@@ -118,7 +118,7 @@ Full template: [references/workflow.md](references/workflow.md#step-4-generate-o

 ⛔ **BLOCKING: Prompt files MUST be saved before ANY image generation.**

-**Execution strategy**: When multiple illustrations have saved prompt files and the task is now plain generation, prefer `baoyu-image-gen` batch mode (`build-batch.ts` → `--batchfile`) over spawning subagents. Use subagents only when each image still needs separate prompt iteration or creative exploration.
+**Execution strategy**: When multiple illustrations have saved prompt files and the task is now plain generation, prefer `baoyu-imagine` batch mode (`build-batch.ts` → `--batchfile`) over spawning subagents. Use subagents only when each image still needs separate prompt iteration or creative exploration.

 1. For each illustration, create a prompt file per [references/prompt-construction.md](references/prompt-construction.md)
 2. Save to `prompts/NN-{type}-{slug}.md` with YAML frontmatter
@@ -280,5 +280,5 @@ TEXTURE: Halftone transitions between sides
 If watermark enabled in preferences, append:

 ```
-Include a subtle watermark "[content]" positioned at [position] with approximately [opacity*100]% visibility.
+Include a subtle watermark "[content]" positioned at [position].
 ```
@@ -316,7 +316,7 @@ Prompt Files:
 **DO NOT** pass ad-hoc inline text to `--prompt` without first saving prompt files. The generation command should either use `--promptfiles prompts/NN-{type}-{slug}.md` or read the saved file content for `--prompt`.

 **Execution choice**:
- If multiple illustrations already have saved prompt files and the task is now plain generation, prefer `baoyu-image-gen` batch mode (`build-batch.ts` -> `main.ts --batchfile`)
+- If multiple illustrations already have saved prompt files and the task is now plain generation, prefer `baoyu-imagine` batch mode (`build-batch.ts` -> `main.ts --batchfile`)
 - Use subagents only when each illustration still needs separate prompt rewriting, style exploration, or other per-image reasoning before generation

 **CRITICAL - References in Frontmatter**:
@@ -352,7 +352,7 @@ Check available skills. If multiple, ask user.

 | Skill Supports `--ref` | Action |
 |------------------------|--------|
-| Yes (e.g., baoyu-image-gen with Google) | Pass reference images via `--ref` |
+| Yes (e.g., baoyu-imagine with Google) | Pass reference images via `--ref` |
 | No | Convert to text description, append to prompt |

 **Verification**: Before generating, confirm reference processing:
@@ -29,8 +29,8 @@ Options:
  --prompts <path>     Path to prompts directory
  --output <path>      Path to output batch.json
  --images-dir <path>  Directory for generated images
-  --provider <name>    Provider for baoyu-image-gen batch tasks (default: replicate)
-  --model <id>         Model for baoyu-image-gen batch tasks (default: google/nano-banana-pro)
+  --provider <name>    Provider for baoyu-imagine batch tasks (default: replicate)
+  --model <id>         Model for baoyu-imagine batch tasks (default: google/nano-banana-pro)
  --ar <ratio>         Aspect ratio for all tasks (default: 16:9)
  --quality <level>    Quality for all tasks (default: 2k)
  --jobs <count>       Recommended worker count metadata (optional)
@@ -216,7 +216,7 @@ Analyze → [Check Existing?] → [Confirm: Style + Reviews] → Storyboard →

 **7.1 Generate character sheet first**:
 - **Backup rule**: If `characters/characters.png` exists, rename to `characters/characters-backup-YYYYMMDD-HHMMSS.png`
- Invoke an installed image generation skill such as `baoyu-image-gen`
+- Invoke an installed image generation skill such as `baoyu-imagine`
 - Read that skill's `SKILL.md` and follow its documented interface rather than calling its scripts directly
 - Use `characters/characters.md` as the prompt-file input
 - Save output to `characters/characters.png`
@@ -278,7 +278,7 @@ Create storyboard and character definitions using the confirmed style from Step
 | Role | Character | Visual Description |
 |------|-----------|-------------------|
 | Student | 大雄 (Nobita) | Japanese boy, 10yo, round glasses, black hair parted in middle, yellow shirt, navy shorts |
-| Mentor | 哆啦A梦 (Doraemon) | Round blue robot cat, big white eyes, red nose, whiskers, white belly with 4D pocket, golden bell, no ears |
+| Mentor | 哆啦 A 梦 (Doraemon) | Round blue robot cat, big white eyes, red nose, whiskers, white belly with 4D pocket, golden bell, no ears |
 | Challenge | 胖虎 (Gian) | Stocky boy, rough features, small eyes, orange shirt |
 | Support | 静香 (Shizuka) | Cute girl, black short hair, pink dress, gentle expression |

@@ -359,8 +359,7 @@ Art: [art style] | Tone: [tone] | Layout: [layout type]
 **Watermark Application** (if enabled in preferences):
 Add to each prompt:
 ```
-Include a subtle watermark "[content]" positioned at [position]
-with approximately [opacity*100]% visibility. The watermark should
+Include a subtle watermark "[content]" positioned at [position]. The watermark should
 be legible but not distracting from the comic panels and storytelling.
 Ensure watermark does not overlap speech bubbles or key action.
 ```
@@ -434,7 +433,7 @@ With confirmed prompts from Step 5/6:
 | Supports `--ref` | **Strategy A** | Pass `characters/characters.png` with EVERY page |
 | Does NOT support `--ref` | **Strategy B** | Prepend character descriptions to EVERY prompt |

-**Strategy A: Using `--ref` parameter** (e.g., baoyu-image-gen)
+**Strategy A: Using `--ref` parameter** (e.g., baoyu-imagine)

 - Read the chosen image generation skill's `SKILL.md`
 - Invoke that installed skill via its documented interface, not by calling its scripts directly
@@ -452,8 +451,8 @@ When skill does NOT support reference images, create combined prompt files:

 ## Character Reference (maintain consistency)
 [Copy relevant sections from characters/characters.md here]
- 大雄: Japanese boy, round glasses, yellow shirt, navy shorts...
- 哆啦A梦: Round blue robot cat, white belly, red nose, golden bell...
+- 大雄：Japanese boy, round glasses, yellow shirt, navy shorts...
+- 哆啦 A 梦：Round blue robot cat, white belly, red nose, golden bell...

 ## Page Content
 [Original page prompt here]
@@ -162,15 +162,14 @@ if (Test-Path "$HOME/.baoyu-skills/baoyu-cover-image/EXTEND.md") { "user" }
 5. **Detect language**: Compare source, user input, EXTEND.md preference
 6. **Determine output directory**: Per File Structure rules

-**⚠️ People in Reference Images — MUST follow all 3 rules:**
+**⚠️ People in Reference Images:**

 If reference images contain **people** who should appear in the cover:

-1. **`usage: direct`** — MUST set in refs description file. NEVER use `style` or `palette` when people need to appear
-2. **Per-character description** — MUST describe each person's distinctive features (hair, glasses, skin tone, clothing) in `refs/ref-NN-{slug}.md`. Vague descriptions like "a man" will fail
-3. **`--ref` flag** — MUST pass reference image via `--ref` in Step 4 so the model sees actual faces
+- **Model supports `--ref`** (default): Copy image to `refs/`, pass via `--ref` at generation. No description file needed — the model sees the face directly.
+- **Model does NOT support `--ref`** (Jimeng, Seedream 3.0): Create `refs/ref-NN-{slug}.md` with per-character description (hair, glasses, skin tone, clothing). Embed as MUST/REQUIRED instructions in prompt text.

-See [reference-images.md § Character Analysis](references/workflow/reference-images.md) for description format.
+See [reference-images.md](references/workflow/reference-images.md) for full decision table.

 ### Step 2: Confirm Options ⚠️

@@ -16,17 +16,24 @@ Guide for processing user-provided reference images in cover generation.

 **If user provides file path**:
 1. Copy to `refs/ref-NN-{slug}.{ext}` (NN = 01, 02, ...)
-2. Create description: `refs/ref-NN-{slug}.md`
-3. Verify files exist before proceeding
+2. **Only** create description file `refs/ref-NN-{slug}.md` when model does NOT support `--ref` (see below)
+3. Verify image file exists before proceeding

-**Description File Format**:
+**When to create description file**:
+
+| Situation | Action |
+|-----------|--------|
+| Model supports `--ref` (Google, OpenAI, OpenRouter, Replicate, Seedream 4.0+) | Copy image only. **No description file needed.** Pass via `--ref` at generation. |
+| Model does NOT support `--ref` (Jimeng, Seedream 3.0) | Copy image + create description file. Embed description in prompt text. |
+
+**Description File Format** (only when needed):
 ```yaml
 ---
 ref_id: NN
 filename: ref-NN-{slug}.{ext}
 usage: direct | style | palette
 ---
-[User's description or auto-generated description]
+[Character or style description to embed in prompt]
 ```

 | Usage | When to Use |
@@ -1,4 +1,5 @@
-import { expect, test } from "bun:test";
+import assert from "node:assert/strict";
+import test from "node:test";

 import { formatArticleMarkdown } from "./markdown.js";

@@ -40,10 +41,10 @@ test("formatArticleMarkdown renders MARKDOWN entities from atomic blocks", () =>

  const { markdown } = formatArticleMarkdown(article);

-  expect(markdown).toContain("Before the snippet.");
-  expect(markdown).toContain("```python\nprint('hello from x article')\n```");
-  expect(markdown).toContain("After the snippet.");
-  expect(markdown).toBe(`# Atomic Markdown Example
+  assert.ok(markdown.includes("Before the snippet."));
+  assert.ok(markdown.includes("```python\nprint('hello from x article')\n```"));
+  assert.ok(markdown.includes("After the snippet."));
+  assert.strictEqual(markdown, `# Atomic Markdown Example

 Before the snippet.

@@ -108,11 +109,11 @@ test("formatArticleMarkdown renders article video media as poster plus video lin

  const { markdown } = formatArticleMarkdown(article);

-  expect(markdown).toContain("Intro text.");
-  expect(markdown).toContain(`![Demo reel](${posterUrl})`);
-  expect(markdown).toContain(`[video](${videoUrl})`);
-  expect(markdown).not.toContain(`![Demo reel](${videoUrl})`);
-  expect(markdown).not.toContain("## Media");
+  assert.ok(markdown.includes("Intro text."));
+  assert.ok(markdown.includes(`![Demo reel](${posterUrl})`));
+  assert.ok(markdown.includes(`[video](${videoUrl})`));
+  assert.ok(!markdown.includes(`![Demo reel](${videoUrl})`));
+  assert.ok(!markdown.includes("## Media"));
 });

 test("formatArticleMarkdown renders unused article videos in trailing media section", () => {
@@ -143,10 +144,10 @@ test("formatArticleMarkdown renders unused article videos in trailing media sect

  const { markdown, coverUrl } = formatArticleMarkdown(article);

-  expect(coverUrl).toBeNull();
-  expect(markdown).toContain("## Media");
-  expect(markdown).toContain(`![video](${posterUrl})`);
-  expect(markdown).toContain(`[video](${videoUrl})`);
+  assert.strictEqual(coverUrl, null);
+  assert.ok(markdown.includes("## Media"));
+  assert.ok(markdown.includes(`![video](${posterUrl})`));
+  assert.ok(markdown.includes(`[video](${videoUrl})`));
 });

 test("formatArticleMarkdown keeps coverUrl as preview image for video cover media", () => {
@@ -174,5 +175,5 @@ test("formatArticleMarkdown keeps coverUrl as preview image for video cover medi

  const { coverUrl } = formatArticleMarkdown(article);

-  expect(coverUrl).toBe(posterUrl);
+  assert.strictEqual(coverUrl, posterUrl);
 });
@@ -1,7 +1,7 @@
 ---
 name: baoyu-image-gen
-description: AI image generation with OpenAI, Google, OpenRouter, DashScope, Jimeng, Seedream and Replicate APIs. Supports text-to-image, reference images, aspect ratios, and batch generation from saved prompt files. Sequential by default; use batch parallel generation when the user already has multiple prompts or wants stable multi-image throughput. Use when user asks to generate, create, or draw images.
-version: 1.56.3
+description: AI image generation with OpenAI, Azure OpenAI, Google, OpenRouter, DashScope, MiniMax, Jimeng, Seedream and Replicate APIs. Supports text-to-image, reference images, aspect ratios, and batch generation from saved prompt files. Sequential by default; use batch parallel generation when the user already has multiple prompts or wants stable multi-image throughput. Use when user asks to generate, create, or draw images.
+version: 1.56.4
 metadata:
  openclaw:
    homepage: https://github.com/JimLiu/baoyu-skills#baoyu-image-gen
@@ -11,9 +11,11 @@ metadata:
        - npx
 ---

+> **⚠️ DEPRECATED**: This skill has been migrated to `baoyu-imagine`. Print a bold red warning to the user **in the user's language** (use ANSI escape `\033[1;31m`) stating that `baoyu-image-gen` has been migrated to `baoyu-imagine` and they should use `baoyu-imagine` instead. Then STOP — do NOT continue executing this skill.
+
 # Image Generation (AI SDK)

-Official API-based image generation. Supports OpenAI, Google, OpenRouter, DashScope (阿里通义万象), Jimeng (即梦), Seedream (豆包) and Replicate providers.
+Official API-based image generation. Supports OpenAI, Azure OpenAI, Google, OpenRouter, DashScope (阿里通义万象), MiniMax, Jimeng (即梦), Seedream (豆包) and Replicate providers.

 ## Script Directory

@@ -74,12 +76,15 @@ ${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --quality 2k
 # From prompt files
 ${BUN_X} {baseDir}/scripts/main.ts --promptfiles system.md content.md --image out.png

-# With reference images (Google, OpenAI, OpenRouter, Replicate, or Seedream 4.0/4.5/5.0)
+# With reference images (Google, OpenAI, Azure OpenAI, OpenRouter, Replicate, MiniMax, or Seedream 4.0/4.5/5.0)
 ${BUN_X} {baseDir}/scripts/main.ts --prompt "Make blue" --image out.png --ref source.png

 # With reference images (explicit provider/model)
 ${BUN_X} {baseDir}/scripts/main.ts --prompt "Make blue" --image out.png --provider google --model gemini-3-pro-image-preview --ref source.png

+# Azure OpenAI (model means deployment name)
+${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider azure --model gpt-image-1.5
+
 # OpenRouter (recommended default model)
 ${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider openrouter

@@ -98,6 +103,15 @@ ${BUN_X} {baseDir}/scripts/main.ts --prompt "为咖啡品牌设计一张 21:9
 # DashScope legacy Qwen fixed-size model
 ${BUN_X} {baseDir}/scripts/main.ts --prompt "一张电影感海报" --image out.png --provider dashscope --model qwen-image-max --size 1664x928

+# MiniMax
+${BUN_X} {baseDir}/scripts/main.ts --prompt "A fashion editorial portrait by a bright studio window" --image out.jpg --provider minimax
+
+# MiniMax with subject reference (best for character/portrait consistency)
+${BUN_X} {baseDir}/scripts/main.ts --prompt "A girl stands by the library window, cinematic lighting" --image out.jpg --provider minimax --model image-01 --ref portrait.png --ar 16:9
+
+# MiniMax with custom size (documented for image-01)
+${BUN_X} {baseDir}/scripts/main.ts --prompt "A cinematic poster" --image out.jpg --provider minimax --model image-01 --size 1536x1024
+
 # Replicate (google/nano-banana-pro)
 ${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider replicate

@@ -147,13 +161,13 @@ Paths in `promptFiles`, `image`, and `ref` are resolved relative to the batch fi
 | `--image <path>` | Output image path (required in single-image mode) |
 | `--batchfile <path>` | JSON batch file for multi-image generation |
 | `--jobs <count>` | Worker count for batch mode (default: auto, max from config, built-in default 10) |
-| `--provider google\|openai\|openrouter\|dashscope\|jimeng\|seedream\|replicate` | Force provider (default: auto-detect) |
-| `--model <id>`, `-m` | Model ID (Google: `gemini-3-pro-image-preview`; OpenAI: `gpt-image-1.5`; OpenRouter: `google/gemini-3.1-flash-image-preview`; DashScope: `qwen-image-2.0-pro`) |
+| `--provider google\|openai\|azure\|openrouter\|dashscope\|minimax\|jimeng\|seedream\|replicate` | Force provider (default: auto-detect) |
+| `--model <id>`, `-m` | Model ID (Google: `gemini-3-pro-image-preview`; OpenAI: `gpt-image-1.5`; Azure: deployment name such as `gpt-image-1.5` or `image-prod`; OpenRouter: `google/gemini-3.1-flash-image-preview`; DashScope: `qwen-image-2.0-pro`; MiniMax: `image-01`) |
 | `--ar <ratio>` | Aspect ratio (e.g., `16:9`, `1:1`, `4:3`) |
 | `--size <WxH>` | Size (e.g., `1024x1024`) |
 | `--quality normal\|2k` | Quality preset (default: `2k`) |
 | `--imageSize 1K\|2K\|4K` | Image size for Google/OpenRouter (default: from quality) |
-| `--ref <files...>` | Reference images. Supported by Google multimodal, OpenAI GPT Image edits, OpenRouter multimodal models, Replicate, and Seedream 5.0/4.5/4.0. Not supported by Jimeng, Seedream 3.0, or removed SeedEdit 3.0 |
+| `--ref <files...>` | Reference images. Supported by Google multimodal, OpenAI GPT Image edits, Azure OpenAI edits (PNG/JPG only), OpenRouter multimodal models, Replicate, MiniMax subject-reference, and Seedream 5.0/4.5/4.0. Not supported by Jimeng, Seedream 3.0, or removed SeedEdit 3.0 |
 | `--n <count>` | Number of images |
 | `--json` | JSON output |

@@ -162,26 +176,34 @@ Paths in `promptFiles`, `image`, and `ref` are resolved relative to the batch fi
 | Variable | Description |
 |----------|-------------|
 | `OPENAI_API_KEY` | OpenAI API key |
+| `AZURE_OPENAI_API_KEY` | Azure OpenAI API key |
 | `OPENROUTER_API_KEY` | OpenRouter API key |
 | `GOOGLE_API_KEY` | Google API key |
 | `DASHSCOPE_API_KEY` | DashScope API key (阿里云) |
+| `MINIMAX_API_KEY` | MiniMax API key |
 | `REPLICATE_API_TOKEN` | Replicate API token |
 | `JIMENG_ACCESS_KEY_ID` | Jimeng (即梦) Volcengine access key |
 | `JIMENG_SECRET_ACCESS_KEY` | Jimeng (即梦) Volcengine secret key |
 | `ARK_API_KEY` | Seedream (豆包) Volcengine ARK API key |
 | `OPENAI_IMAGE_MODEL` | OpenAI model override |
+| `AZURE_OPENAI_DEPLOYMENT` | Azure default deployment name |
+| `AZURE_OPENAI_IMAGE_MODEL` | Backward-compatible alias for Azure default deployment/model name |
 | `OPENROUTER_IMAGE_MODEL` | OpenRouter model override (default: `google/gemini-3.1-flash-image-preview`) |
 | `GOOGLE_IMAGE_MODEL` | Google model override |
 | `DASHSCOPE_IMAGE_MODEL` | DashScope model override (default: `qwen-image-2.0-pro`) |
+| `MINIMAX_IMAGE_MODEL` | MiniMax model override (default: `image-01`) |
 | `REPLICATE_IMAGE_MODEL` | Replicate model override (default: google/nano-banana-pro) |
 | `JIMENG_IMAGE_MODEL` | Jimeng model override (default: jimeng_t2i_v40) |
 | `SEEDREAM_IMAGE_MODEL` | Seedream model override (default: doubao-seedream-5-0-260128) |
 | `OPENAI_BASE_URL` | Custom OpenAI endpoint |
+| `AZURE_OPENAI_BASE_URL` | Azure resource endpoint or deployment endpoint |
+| `AZURE_API_VERSION` | Azure image API version (default: `2025-04-01-preview`) |
 | `OPENROUTER_BASE_URL` | Custom OpenRouter endpoint (default: `https://openrouter.ai/api/v1`) |
 | `OPENROUTER_HTTP_REFERER` | Optional app/site URL for OpenRouter attribution |
 | `OPENROUTER_TITLE` | Optional app name for OpenRouter attribution |
 | `GOOGLE_BASE_URL` | Custom Google endpoint |
 | `DASHSCOPE_BASE_URL` | Custom DashScope endpoint |
+| `MINIMAX_BASE_URL` | Custom MiniMax endpoint (default: `https://api.minimax.io`) |
 | `REPLICATE_BASE_URL` | Custom Replicate endpoint |
 | `JIMENG_BASE_URL` | Custom Jimeng endpoint (default: `https://visual.volcengineapi.com`) |
 | `JIMENG_REGION` | Jimeng region (default: `cn-north-1`) |
@@ -201,6 +223,8 @@ Model priority (highest → lowest), applies to all providers:
 3. Env var: `<PROVIDER>_IMAGE_MODEL` (e.g., `GOOGLE_IMAGE_MODEL`)
 4. Built-in default

+For Azure, `--model` / `default_model.azure` should be the Azure deployment name. `AZURE_OPENAI_DEPLOYMENT` is the preferred env var, and `AZURE_OPENAI_IMAGE_MODEL` remains as a backward-compatible alias.
+
 **EXTEND.md overrides env vars**. If both EXTEND.md `default_model.google: "gemini-3-pro-image-preview"` and env var `GOOGLE_IMAGE_MODEL=gemini-3.1-flash-image-preview` exist, EXTEND.md wins.

 **Agent MUST display model info** before each generation:
@@ -253,6 +277,34 @@ Official references:
 - [Text-to-image guide](https://help.aliyun.com/zh/model-studio/text-to-image)
 - [Qwen-Image Edit API](https://help.aliyun.com/zh/model-studio/qwen-image-edit-api)

+### MiniMax Models
+
+Use `--model image-01` or set `default_model.minimax` / `MINIMAX_IMAGE_MODEL` when the user wants MiniMax image generation.
+
+Official MiniMax image model options currently documented in the API reference:
+
+- `image-01` (recommended default)
+  - Supports text-to-image and subject-reference image generation
+  - Supports official `aspect_ratio` values: `1:1`, `16:9`, `4:3`, `3:2`, `2:3`, `3:4`, `9:16`, `21:9`
+  - Supports documented custom `width` / `height` output sizes when using `--size <WxH>`
+  - `width` and `height` must both be between `512` and `2048`, and both must be divisible by `8`
+- `image-01-live`
+  - Lower-latency variant
+  - Use `--ar` for sizing; MiniMax documents custom `width` / `height` as only effective for `image-01`
+
+MiniMax subject reference notes:
+
+- `--ref` files are sent as MiniMax `subject_reference`
+- MiniMax docs currently describe `subject_reference[].type` as `character`
+- Official docs say `image_file` supports public URLs or Base64 Data URLs; `baoyu-image-gen` sends local refs as Data URLs
+- Official docs recommend front-facing portrait references in JPG/JPEG/PNG under 10MB
+
+Official references:
+
+- [MiniMax Image Generation Guide](https://platform.minimax.io/docs/guides/image-generation)
+- [MiniMax Text-to-Image API](https://platform.minimax.io/docs/api-reference/image-generation-t2i)
+- [MiniMax Image-to-Image API](https://platform.minimax.io/docs/api-reference/image-generation-i2i)
+
 ### OpenRouter Models

 Use full OpenRouter model IDs, e.g.:
@@ -287,8 +339,8 @@ ${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider r

 ## Provider Selection

-1. `--ref` provided + no `--provider` → auto-select Google first, then OpenAI, then OpenRouter, then Replicate (Jimeng and Seedream do not support reference images)
-2. `--provider` specified → use it (if `--ref`, must be `google`, `openai`, `openrouter`, or `replicate`)
+1. `--ref` provided + no `--provider` → auto-select Google first, then OpenAI, then Azure, then OpenRouter, then Replicate, then Seedream, then MiniMax (MiniMax subject reference is more specialized toward character/portrait consistency)
+2. `--provider` specified → use it (if `--ref`, must be `google`, `openai`, `azure`, `openrouter`, `replicate`, `seedream`, or `minimax`)
 3. Only one API key available → use that provider
 4. Multiple available → default to Google

@@ -309,6 +361,7 @@ Supported: `1:1`, `16:9`, `9:16`, `4:3`, `3:4`, `2.35:1`
 - OpenAI: maps to closest supported size
 - OpenRouter: sends `imageGenerationOptions.aspect_ratio`; if only `--size <WxH>` is given, aspect ratio is inferred automatically
 - Replicate: passes `aspect_ratio` to model; when `--ref` is provided without `--ar`, defaults to `match_input_image`
+- MiniMax: sends official `aspect_ratio` values directly; if `--size <WxH>` is given without `--ar`, `width` / `height` are sent for `image-01`

 ## Generation Mode

@@ -47,10 +47,14 @@ options:
    description: "Gemini multimodal - high quality, reference images, flexible sizes"
  - label: "OpenAI"
    description: "GPT Image - consistent quality, reliable output"
+  - label: "Azure OpenAI"
+    description: "Azure-hosted GPT Image deployments with resource-specific routing"
  - label: "OpenRouter"
    description: "Router for Gemini/FLUX/OpenAI-compatible image models"
  - label: "DashScope"
    description: "Alibaba Cloud - Qwen-Image, strong Chinese/English text rendering"
+  - label: "MiniMax"
+    description: "MiniMax image generation with subject-reference character workflows"
  - label: "Replicate"
    description: "Community models - nano-banana-pro, flexible model selection"
 ```
@@ -87,6 +91,34 @@ options:
    description: "Strong text-to-image quality through OpenRouter"
 ```

+### Question 2c: Default Azure Deployment
+
+Only show if user selected Azure OpenAI.
+
+```yaml
+header: "Azure Deploy"
+question: "Default Azure image deployment name?"
+options:
+  - label: "gpt-image-1.5 (Recommended)"
+    description: "Best default if your Azure deployment uses the same name"
+  - label: "gpt-image-1"
+    description: "Previous GPT Image deployment name"
+```
+
+### Question 2d: Default MiniMax Model
+
+Only show if user selected MiniMax.
+
+```yaml
+header: "MiniMax Model"
+question: "Default MiniMax image generation model?"
+options:
+  - label: "image-01 (Recommended)"
+    description: "Best default, supports aspect ratios and custom width/height"
+  - label: "image-01-live"
+    description: "Faster variant, use aspect ratio instead of custom size"
+```
+
 ### Question 3: Default Quality

 ```yaml
@@ -130,8 +162,10 @@ default_image_size: null
 default_model:
  google: [selected google model or null]
  openai: null
+  azure: [selected azure deployment or null]
  openrouter: [selected openrouter model or null]
  dashscope: null
+  minimax: [selected minimax model or null]
  replicate: null
 ---
 ```
@@ -166,6 +200,23 @@ options:
    description: "Previous generation GPT Image model"
 ```

+### Azure Deployment Selection
+
+```yaml
+header: "Azure Deploy"
+question: "Choose a default Azure image deployment name?"
+options:
+  - label: "gpt-image-1.5 (Recommended)"
+    description: "Use when your Azure deployment name matches the GPT-image-1.5 model"
+  - label: "gpt-image-1"
+    description: "Use when your Azure deployment name matches GPT-image-1"
+```
+
+Notes for Azure setup:
+
+- In `baoyu-image-gen`, Azure `--model` / `default_model.azure` should be the Azure deployment name, not just the underlying model family.
+- If the deployment name is custom, save that exact deployment name in `default_model.azure`.
+
 ### OpenRouter Model Selection

 ```yaml
@@ -218,6 +269,24 @@ options:
    description: "Google's base image model on Replicate"
 ```

+### MiniMax Model Selection
+
+```yaml
+header: "MiniMax Model"
+question: "Choose a default MiniMax image generation model?"
+options:
+  - label: "image-01 (Recommended)"
+    description: "Best general-purpose MiniMax image model with custom width/height support"
+  - label: "image-01-live"
+    description: "Lower-latency MiniMax image model using aspect ratios"
+```
+
+Notes for MiniMax setup:
+
+- `image-01` is the safest default. It supports official `aspect_ratio` values and documented custom `width` / `height` output sizes.
+- `image-01-live` is useful when the user prefers faster generation and can work with aspect-ratio-based sizing.
+- MiniMax subject reference currently uses `subject_reference[].type = character`; docs recommend front-facing portrait references in JPG/JPEG/PNG under 10MB.
+
 ### Update EXTEND.md

 After user selects a model:
@@ -230,8 +299,10 @@ After user selects a model:
 default_model:
  google: [value or null]
  openai: [value or null]
+  azure: [value or null]
  openrouter: [value or null]
  dashscope: [value or null]
+  minimax: [value or null]
  replicate: [value or null]
 ```

@@ -11,7 +11,7 @@ description: EXTEND.md YAML schema for baoyu-image-gen user preferences
 ---
 version: 1

-default_provider: null      # google|openai|openrouter|dashscope|replicate|null (null = auto-detect)
+default_provider: null      # google|openai|azure|openrouter|dashscope|minimax|replicate|null (null = auto-detect)

 default_quality: null       # normal|2k|null (null = use default: 2k)

@@ -22,8 +22,10 @@ default_image_size: null    # 1K|2K|4K|null (Google/OpenRouter, overrides qualit
 default_model:
  google: null              # e.g., "gemini-3-pro-image-preview", "gemini-3.1-flash-image-preview"
  openai: null              # e.g., "gpt-image-1.5", "gpt-image-1"
+  azure: null               # Azure deployment name, e.g., "gpt-image-1.5" or "image-prod"
  openrouter: null          # e.g., "google/gemini-3.1-flash-image-preview"
  dashscope: null           # e.g., "qwen-image-2.0-pro"
+  minimax: null             # e.g., "image-01"
  replicate: null           # e.g., "google/nano-banana-pro"

 batch:
@@ -38,12 +40,18 @@ batch:
    openai:
      concurrency: 3
      start_interval_ms: 1100
+    azure:
+      concurrency: 3
+      start_interval_ms: 1100
    openrouter:
      concurrency: 3
      start_interval_ms: 1100
    dashscope:
      concurrency: 3
      start_interval_ms: 1100
+    minimax:
+      concurrency: 3
+      start_interval_ms: 1100
 ---
 ```

@@ -58,8 +66,10 @@ batch:
 | `default_image_size` | string\|null | null | Google/OpenRouter image size (overrides quality) |
 | `default_model.google` | string\|null | null | Google default model |
 | `default_model.openai` | string\|null | null | OpenAI default model |
+| `default_model.azure` | string\|null | null | Azure default deployment name |
 | `default_model.openrouter` | string\|null | null | OpenRouter default model |
 | `default_model.dashscope` | string\|null | null | DashScope default model |
+| `default_model.minimax` | string\|null | null | MiniMax default model |
 | `default_model.replicate` | string\|null | null | Replicate default model |
 | `batch.max_workers` | int\|null | 10 | Batch worker cap |
 | `batch.provider_limits.<provider>.concurrency` | int\|null | provider default | Max simultaneous requests per provider |
@@ -87,8 +97,10 @@ default_image_size: 2K
 default_model:
  google: "gemini-3-pro-image-preview"
  openai: "gpt-image-1.5"
+  azure: "gpt-image-1.5"
  openrouter: "google/gemini-3.1-flash-image-preview"
  dashscope: "qwen-image-2.0-pro"
+  minimax: "image-01"
  replicate: "google/nano-banana-pro"
 batch:
  max_workers: 10
@@ -96,8 +108,14 @@ batch:
    replicate:
      concurrency: 5
      start_interval_ms: 700
+    azure:
+      concurrency: 3
+      start_interval_ms: 1100
    openrouter:
      concurrency: 3
      start_interval_ms: 1100
+    minimax:
+      concurrency: 3
+      start_interval_ms: 1100
 ---
 ```
@@ -123,6 +123,8 @@ default_image_size: 2K
 default_model:
  google: gemini-3-pro-image-preview
  openai: gpt-image-1.5
+  azure: image-prod
+  minimax: image-01
 batch:
  max_workers: 8
  provider_limits:
@@ -131,6 +133,12 @@ batch:
      start_interval_ms: 900
    openai:
      concurrency: 4
+    minimax:
+      concurrency: 2
+      start_interval_ms: 1400
+    azure:
+      concurrency: 1
+      start_interval_ms: 1500
 `;

  const config = parseSimpleYaml(yaml);
@@ -142,6 +150,8 @@ batch:
  assert.equal(config.default_image_size, "2K");
  assert.equal(config.default_model?.google, "gemini-3-pro-image-preview");
  assert.equal(config.default_model?.openai, "gpt-image-1.5");
+  assert.equal(config.default_model?.azure, "image-prod");
+  assert.equal(config.default_model?.minimax, "image-01");
  assert.equal(config.batch?.max_workers, 8);
  assert.deepEqual(config.batch?.provider_limits?.google, {
    concurrency: 2,
@@ -150,6 +160,14 @@ batch:
  assert.deepEqual(config.batch?.provider_limits?.openai, {
    concurrency: 4,
  });
+  assert.deepEqual(config.batch?.provider_limits?.minimax, {
+    concurrency: 2,
+    start_interval_ms: 1400,
+  });
+  assert.deepEqual(config.batch?.provider_limits?.azure, {
+    concurrency: 1,
+    start_interval_ms: 1500,
+  });
 });

 test("mergeConfig only fills values missing from CLI args", () => {
@@ -191,6 +209,7 @@ test("detectProvider rejects non-ref-capable providers and prefers Google first
    OPENAI_API_KEY: "openai-key",
    OPENROUTER_API_KEY: null,
    DASHSCOPE_API_KEY: null,
+    MINIMAX_API_KEY: null,
    REPLICATE_API_TOKEN: null,
    JIMENG_ACCESS_KEY_ID: null,
    JIMENG_SECRET_ACCESS_KEY: null,
@@ -203,8 +222,11 @@ test("detectProvider selects an available ref-capable provider for reference-ima
  useEnv(t, {
    GOOGLE_API_KEY: null,
    OPENAI_API_KEY: "openai-key",
+    AZURE_OPENAI_API_KEY: null,
+    AZURE_OPENAI_BASE_URL: null,
    OPENROUTER_API_KEY: null,
    DASHSCOPE_API_KEY: null,
+    MINIMAX_API_KEY: null,
    REPLICATE_API_TOKEN: null,
    JIMENG_ACCESS_KEY_ID: null,
    JIMENG_SECRET_ACCESS_KEY: null,
@@ -216,12 +238,35 @@ test("detectProvider selects an available ref-capable provider for reference-ima
  );
 });

+test("detectProvider selects Azure when only Azure credentials are configured", (t) => {
+  useEnv(t, {
+    GOOGLE_API_KEY: null,
+    OPENAI_API_KEY: null,
+    AZURE_OPENAI_API_KEY: "azure-key",
+    AZURE_OPENAI_BASE_URL: "https://example.openai.azure.com",
+    OPENROUTER_API_KEY: null,
+    DASHSCOPE_API_KEY: null,
+    MINIMAX_API_KEY: null,
+    REPLICATE_API_TOKEN: null,
+    JIMENG_ACCESS_KEY_ID: null,
+    JIMENG_SECRET_ACCESS_KEY: null,
+    ARK_API_KEY: null,
+  });
+
+  assert.equal(detectProvider(makeArgs()), "azure");
+  assert.equal(
+    detectProvider(makeArgs({ referenceImages: ["ref.png"] })),
+    "azure",
+  );
+});
+
 test("detectProvider infers Seedream from model id and allows Seedream reference-image workflows", (t) => {
  useEnv(t, {
    GOOGLE_API_KEY: null,
    OPENAI_API_KEY: null,
    OPENROUTER_API_KEY: null,
    DASHSCOPE_API_KEY: null,
+    MINIMAX_API_KEY: null,
    REPLICATE_API_TOKEN: null,
    JIMENG_ACCESS_KEY_ID: null,
    JIMENG_SECRET_ACCESS_KEY: null,
@@ -249,6 +294,26 @@ test("detectProvider infers Seedream from model id and allows Seedream reference
  );
 });

+test("detectProvider selects MiniMax when only MiniMax credentials are configured or the model id matches", (t) => {
+  useEnv(t, {
+    GOOGLE_API_KEY: null,
+    OPENAI_API_KEY: null,
+    AZURE_OPENAI_API_KEY: null,
+    AZURE_OPENAI_BASE_URL: null,
+    OPENROUTER_API_KEY: null,
+    DASHSCOPE_API_KEY: null,
+    MINIMAX_API_KEY: "minimax-key",
+    REPLICATE_API_TOKEN: null,
+    JIMENG_ACCESS_KEY_ID: null,
+    JIMENG_SECRET_ACCESS_KEY: null,
+    ARK_API_KEY: null,
+  });
+
+  assert.equal(detectProvider(makeArgs()), "minimax");
+  assert.equal(detectProvider(makeArgs({ referenceImages: ["ref.png"] })), "minimax");
+  assert.equal(detectProvider(makeArgs({ model: "image-01-live" })), "minimax");
+});
+
 test("batch worker and provider-rate-limit configuration prefer env over EXTEND config", (t) => {
  useEnv(t, {
    BAOYU_IMAGE_GEN_MAX_WORKERS: "12",
@@ -264,6 +329,10 @@ test("batch worker and provider-rate-limit configuration prefer env over EXTEND
          concurrency: 2,
          start_interval_ms: 900,
        },
+        minimax: {
+          concurrency: 1,
+          start_interval_ms: 1500,
+        },
      },
    },
  };
@@ -273,6 +342,10 @@ test("batch worker and provider-rate-limit configuration prefer env over EXTEND
    concurrency: 5,
    startIntervalMs: 450,
  });
+  assert.deepEqual(getConfiguredProviderRateLimits(extendConfig).minimax, {
+    concurrency: 1,
+    startIntervalMs: 1500,
+  });
 });

 test("loadBatchTasks and createTaskArgs resolve batch-relative paths", async (t) => {
@@ -58,8 +58,10 @@ const DEFAULT_PROVIDER_RATE_LIMITS: Record<Provider, ProviderRateLimit> = {
  openai: { concurrency: 3, startIntervalMs: 1100 },
  openrouter: { concurrency: 3, startIntervalMs: 1100 },
  dashscope: { concurrency: 3, startIntervalMs: 1100 },
+  minimax: { concurrency: 3, startIntervalMs: 1100 },
  jimeng: { concurrency: 3, startIntervalMs: 1100 },
  seedream: { concurrency: 3, startIntervalMs: 1100 },
+  azure: { concurrency: 3, startIntervalMs: 1100 },
 };

 function printUsage(): void {
@@ -74,13 +76,13 @@ Options:
  --image <path>            Output image path (required in single-image mode)
  --batchfile <path>        JSON batch file for multi-image generation
  --jobs <count>            Worker count for batch mode (default: auto, max from config, built-in default 10)
-  --provider google|openai|openrouter|dashscope|replicate|jimeng|seedream  Force provider (auto-detect by default)
+  --provider google|openai|openrouter|dashscope|minimax|replicate|jimeng|seedream|azure  Force provider (auto-detect by default)
  -m, --model <id>          Model ID
  --ar <ratio>              Aspect ratio (e.g., 16:9, 1:1, 4:3)
  --size <WxH>              Size (e.g., 1024x1024)
  --quality normal|2k       Quality preset (default: 2k)
  --imageSize 1K|2K|4K      Image size for Google/OpenRouter (default: from quality)
-  --ref <files...>          Reference images (Google, OpenAI, OpenRouter, Replicate, or Seedream 4.0/4.5/5.0)
+  --ref <files...>          Reference images (Google, OpenAI, Azure, OpenRouter, Replicate, MiniMax, or Seedream 4.0/4.5/5.0)
  --n <count>               Number of images for the current task (default: 1)
  --json                    JSON output
  -h, --help                Show help
@@ -111,6 +113,7 @@ Environment variables:
  GOOGLE_API_KEY            Google API key
  GEMINI_API_KEY            Gemini API key (alias for GOOGLE_API_KEY)
  DASHSCOPE_API_KEY         DashScope API key
+  MINIMAX_API_KEY           MiniMax API key
  REPLICATE_API_TOKEN       Replicate API token
  JIMENG_ACCESS_KEY_ID      Jimeng Access Key ID
  JIMENG_SECRET_ACCESS_KEY  Jimeng Secret Access Key
@@ -119,6 +122,7 @@ Environment variables:
  OPENROUTER_IMAGE_MODEL    Default OpenRouter model (google/gemini-3.1-flash-image-preview)
  GOOGLE_IMAGE_MODEL        Default Google model (gemini-3-pro-image-preview)
  DASHSCOPE_IMAGE_MODEL     Default DashScope model (qwen-image-2.0-pro)
+  MINIMAX_IMAGE_MODEL       Default MiniMax model (image-01)
  REPLICATE_IMAGE_MODEL     Default Replicate model (google/nano-banana-pro)
  JIMENG_IMAGE_MODEL        Default Jimeng model (jimeng_t2i_v40)
  SEEDREAM_IMAGE_MODEL      Default Seedream model (doubao-seedream-5-0-260128)
@@ -129,8 +133,14 @@ Environment variables:
  OPENROUTER_TITLE          Optional app name for OpenRouter attribution
  GOOGLE_BASE_URL           Custom Google endpoint
  DASHSCOPE_BASE_URL        Custom DashScope endpoint
+  MINIMAX_BASE_URL          Custom MiniMax endpoint
  REPLICATE_BASE_URL        Custom Replicate endpoint
  JIMENG_BASE_URL           Custom Jimeng endpoint
+  AZURE_OPENAI_API_KEY      Azure OpenAI API key
+  AZURE_OPENAI_BASE_URL     Azure OpenAI resource or deployment endpoint
+  AZURE_OPENAI_DEPLOYMENT   Default Azure deployment name
+  AZURE_API_VERSION         Azure API version (default: 2025-04-01-preview)
+  AZURE_OPENAI_IMAGE_MODEL  Backward-compatible Azure deployment/model alias (defaults to gpt-image-1.5)
  SEEDREAM_BASE_URL         Custom Seedream endpoint
  BAOYU_IMAGE_GEN_MAX_WORKERS  Override batch worker cap
  BAOYU_IMAGE_GEN_<PROVIDER>_CONCURRENCY  Override provider concurrency
@@ -229,9 +239,11 @@ export function parseArgs(argv: string[]): CliArgs {
        v !== "openai" &&
        v !== "openrouter" &&
        v !== "dashscope" &&
+        v !== "minimax" &&
        v !== "replicate" &&
        v !== "jimeng" &&
-        v !== "seedream"
+        v !== "seedream" &&
+        v !== "azure"
      ) {
        throw new Error(`Invalid provider: ${v}`);
      }
@@ -383,9 +395,11 @@ export function parseSimpleYaml(yaml: string): Partial<ExtendConfig> {
          openai: null,
          openrouter: null,
          dashscope: null,
+          minimax: null,
          replicate: null,
          jimeng: null,
          seedream: null,
+          azure: null,
        };
        currentKey = "default_model";
        currentProvider = null;
@@ -409,9 +423,11 @@ export function parseSimpleYaml(yaml: string): Partial<ExtendConfig> {
          key === "openai" ||
          key === "openrouter" ||
          key === "dashscope" ||
+          key === "minimax" ||
          key === "replicate" ||
          key === "jimeng" ||
-          key === "seedream"
+          key === "seedream" ||
+          key === "azure"
        )
      ) {
        config.batch ??= {};
@@ -425,9 +441,11 @@ export function parseSimpleYaml(yaml: string): Partial<ExtendConfig> {
          key === "openai" ||
          key === "openrouter" ||
          key === "dashscope" ||
+          key === "minimax" ||
          key === "replicate" ||
          key === "jimeng" ||
-          key === "seedream"
+          key === "seedream" ||
+          key === "azure"
        )
      ) {
        const cleaned = value.replace(/['"]/g, "");
@@ -518,11 +536,13 @@ export function getConfiguredProviderRateLimits(
    openai: { ...DEFAULT_PROVIDER_RATE_LIMITS.openai },
    openrouter: { ...DEFAULT_PROVIDER_RATE_LIMITS.openrouter },
    dashscope: { ...DEFAULT_PROVIDER_RATE_LIMITS.dashscope },
+    minimax: { ...DEFAULT_PROVIDER_RATE_LIMITS.minimax },
    jimeng: { ...DEFAULT_PROVIDER_RATE_LIMITS.jimeng },
    seedream: { ...DEFAULT_PROVIDER_RATE_LIMITS.seedream },
+    azure: { ...DEFAULT_PROVIDER_RATE_LIMITS.azure },
  };

-  for (const provider of ["replicate", "google", "openai", "openrouter", "dashscope", "jimeng", "seedream"] as Provider[]) {
+  for (const provider of ["replicate", "google", "openai", "openrouter", "dashscope", "minimax", "jimeng", "seedream", "azure"] as Provider[]) {
    const envPrefix = `BAOYU_IMAGE_GEN_${provider.toUpperCase()}`;
    const extendLimit = extendConfig.batch?.provider_limits?.[provider];
    configured[provider] = {
@@ -571,7 +591,9 @@ export function normalizeOutputImagePath(p: string, defaultExtension = ".png"):

 function inferProviderFromModel(model: string | null): Provider | null {
  if (!model) return null;
-  if (model.includes("seedream") || model.includes("seededit")) return "seedream";
+  const normalized = model.trim();
+  if (normalized.includes("seedream") || normalized.includes("seededit")) return "seedream";
+  if (normalized === "image-01" || normalized === "image-01-live") return "minimax";
  return null;
 }

@@ -581,21 +603,25 @@ export function detectProvider(args: CliArgs): Provider {
    args.provider &&
    args.provider !== "google" &&
    args.provider !== "openai" &&
+    args.provider !== "azure" &&
    args.provider !== "openrouter" &&
    args.provider !== "replicate" &&
-    args.provider !== "seedream"
+    args.provider !== "seedream" &&
+    args.provider !== "minimax"
  ) {
    throw new Error(
-      "Reference images require a ref-capable provider. Use --provider google (Gemini multimodal), --provider openai (GPT Image edits), --provider openrouter (OpenRouter multimodal), --provider replicate, or --provider seedream for supported Seedream models."
+      "Reference images require a ref-capable provider. Use --provider google (Gemini multimodal), --provider openai (GPT Image edits), --provider azure (Azure OpenAI), --provider openrouter (OpenRouter multimodal), --provider replicate, --provider seedream for supported Seedream models, or --provider minimax for MiniMax subject-reference workflows."
    );
  }

  if (args.provider) return args.provider;

  const hasGoogle = !!(process.env.GOOGLE_API_KEY || process.env.GEMINI_API_KEY);
+  const hasAzure = !!(process.env.AZURE_OPENAI_API_KEY && process.env.AZURE_OPENAI_BASE_URL);
  const hasOpenai = !!process.env.OPENAI_API_KEY;
  const hasOpenrouter = !!process.env.OPENROUTER_API_KEY;
  const hasDashscope = !!process.env.DASHSCOPE_API_KEY;
+  const hasMinimax = !!process.env.MINIMAX_API_KEY;
  const hasReplicate = !!process.env.REPLICATE_API_TOKEN;
  const hasJimeng = !!(process.env.JIMENG_ACCESS_KEY_ID && process.env.JIMENG_SECRET_ACCESS_KEY);
  const hasSeedream = !!process.env.ARK_API_KEY;
@@ -608,22 +634,33 @@ export function detectProvider(args: CliArgs): Provider {
    return "seedream";
  }

+  if (modelProvider === "minimax") {
+    if (!hasMinimax) {
+      throw new Error("Model looks like a MiniMax image model, but MINIMAX_API_KEY is not set.");
+    }
+    return "minimax";
+  }
+
  if (args.referenceImages.length > 0) {
    if (hasGoogle) return "google";
    if (hasOpenai) return "openai";
+    if (hasAzure) return "azure";
    if (hasOpenrouter) return "openrouter";
    if (hasReplicate) return "replicate";
    if (hasSeedream) return "seedream";
+    if (hasMinimax) return "minimax";
    throw new Error(
-      "Reference images require Google, OpenAI, OpenRouter, Replicate, or supported Seedream models. Set GOOGLE_API_KEY/GEMINI_API_KEY, OPENAI_API_KEY, OPENROUTER_API_KEY, REPLICATE_API_TOKEN, or ARK_API_KEY, or remove --ref."
+      "Reference images require Google, OpenAI, Azure, OpenRouter, Replicate, supported Seedream models, or MiniMax. Set GOOGLE_API_KEY/GEMINI_API_KEY, OPENAI_API_KEY, AZURE_OPENAI_API_KEY+AZURE_OPENAI_BASE_URL, OPENROUTER_API_KEY, REPLICATE_API_TOKEN, ARK_API_KEY, or MINIMAX_API_KEY, or remove --ref."
    );
  }

  const available = [
    hasGoogle && "google",
    hasOpenai && "openai",
+    hasAzure && "azure",
    hasOpenrouter && "openrouter",
    hasDashscope && "dashscope",
+    hasMinimax && "minimax",
    hasReplicate && "replicate",
    hasJimeng && "jimeng",
    hasSeedream && "seedream",
@@ -633,7 +670,7 @@ export function detectProvider(args: CliArgs): Provider {
  if (available.length > 1) return available[0]!;

  throw new Error(
-    "No API key found. Set GOOGLE_API_KEY, GEMINI_API_KEY, OPENAI_API_KEY, OPENROUTER_API_KEY, DASHSCOPE_API_KEY, REPLICATE_API_TOKEN, JIMENG keys, or ARK_API_KEY.\n" +
+    "No API key found. Set GOOGLE_API_KEY, GEMINI_API_KEY, OPENAI_API_KEY, AZURE_OPENAI_API_KEY+AZURE_OPENAI_BASE_URL, OPENROUTER_API_KEY, DASHSCOPE_API_KEY, MINIMAX_API_KEY, REPLICATE_API_TOKEN, JIMENG keys, or ARK_API_KEY.\n" +
      "Create ~/.baoyu-skills/.env or <cwd>/.baoyu-skills/.env with your keys."
  );
 }
@@ -672,10 +709,12 @@ export function isRetryableGenerationError(error: unknown): boolean {
 async function loadProviderModule(provider: Provider): Promise<ProviderModule> {
  if (provider === "google") return (await import("./providers/google")) as ProviderModule;
  if (provider === "dashscope") return (await import("./providers/dashscope")) as ProviderModule;
+  if (provider === "minimax") return (await import("./providers/minimax")) as ProviderModule;
  if (provider === "replicate") return (await import("./providers/replicate")) as ProviderModule;
  if (provider === "openrouter") return (await import("./providers/openrouter")) as ProviderModule;
  if (provider === "jimeng") return (await import("./providers/jimeng")) as ProviderModule;
  if (provider === "seedream") return (await import("./providers/seedream")) as ProviderModule;
+  if (provider === "azure") return (await import("./providers/azure")) as ProviderModule;
  return (await import("./providers/openai")) as ProviderModule;
 }

@@ -701,9 +740,11 @@ function getModelForProvider(
      return extendConfig.default_model.openrouter;
    }
    if (provider === "dashscope" && extendConfig.default_model.dashscope) return extendConfig.default_model.dashscope;
+    if (provider === "minimax" && extendConfig.default_model.minimax) return extendConfig.default_model.minimax;
    if (provider === "replicate" && extendConfig.default_model.replicate) return extendConfig.default_model.replicate;
    if (provider === "jimeng" && extendConfig.default_model.jimeng) return extendConfig.default_model.jimeng;
    if (provider === "seedream" && extendConfig.default_model.seedream) return extendConfig.default_model.seedream;
+    if (provider === "azure" && extendConfig.default_model.azure) return extendConfig.default_model.azure;
  }
  return providerModule.getDefaultModel();
 }
@@ -923,7 +964,7 @@ async function runBatchTasks(
  const acquireProvider = createProviderGate(providerRateLimits);
  const workerCount = getWorkerCount(tasks.length, jobs, maxWorkers);
  console.error(`Batch mode: ${tasks.length} tasks, ${workerCount} workers, parallel mode enabled.`);
-  for (const provider of ["replicate", "google", "openai", "openrouter", "dashscope", "jimeng", "seedream"] as Provider[]) {
+  for (const provider of ["replicate", "google", "openai", "openrouter", "dashscope", "jimeng", "seedream", "azure"] as Provider[]) {
    const limit = providerRateLimits[provider];
    console.error(`- ${provider}: concurrency=${limit.concurrency}, startIntervalMs=${limit.startIntervalMs}`);
  }
@@ -0,0 +1,188 @@
+import assert from "node:assert/strict";
+import fs from "node:fs/promises";
+import os from "node:os";
+import path from "node:path";
+import test, { type TestContext } from "node:test";
+
+import type { CliArgs } from "../types.ts";
+import {
+  generateImage,
+  getDefaultModel,
+  parseAzureBaseURL,
+  validateArgs,
+} from "./azure.ts";
+
+function useEnv(
+  t: TestContext,
+  values: Record<string, string | null>,
+): void {
+  const previous = new Map<string, string | undefined>();
+  for (const [key, value] of Object.entries(values)) {
+    previous.set(key, process.env[key]);
+    if (value == null) {
+      delete process.env[key];
+    } else {
+      process.env[key] = value;
+    }
+  }
+
+  t.after(() => {
+    for (const [key, value] of previous.entries()) {
+      if (value == null) {
+        delete process.env[key];
+      } else {
+        process.env[key] = value;
+      }
+    }
+  });
+}
+
+function makeArgs(overrides: Partial<CliArgs> = {}): CliArgs {
+  return {
+    prompt: null,
+    promptFiles: [],
+    imagePath: null,
+    provider: null,
+    model: null,
+    aspectRatio: null,
+    size: null,
+    quality: null,
+    imageSize: null,
+    referenceImages: [],
+    n: 1,
+    batchFile: null,
+    jobs: null,
+    json: false,
+    help: false,
+    ...overrides,
+  };
+}
+
+async function makeTempDir(prefix: string): Promise<string> {
+  return fs.mkdtemp(path.join(os.tmpdir(), prefix));
+}
+
+test("Azure endpoint parsing and default deployment selection follow env precedence", (t) => {
+  assert.deepEqual(parseAzureBaseURL("https://example.openai.azure.com"), {
+    resourceBaseURL: "https://example.openai.azure.com/openai",
+    deployment: null,
+  });
+  assert.deepEqual(
+    parseAzureBaseURL("https://example.openai.azure.com/openai/deployments/from-url"),
+    {
+      resourceBaseURL: "https://example.openai.azure.com/openai",
+      deployment: "from-url",
+    },
+  );
+
+  useEnv(t, {
+    AZURE_OPENAI_BASE_URL: "https://example.openai.azure.com/openai/deployments/from-url",
+    AZURE_OPENAI_DEPLOYMENT: "explicit-deploy",
+    AZURE_OPENAI_IMAGE_MODEL: "env-fallback",
+  });
+  assert.equal(getDefaultModel(), "explicit-deploy");
+});
+
+test("Azure validateArgs rejects unsupported edit input formats before the API call", () => {
+  assert.doesNotThrow(() =>
+    validateArgs("demo-deployment", makeArgs({ referenceImages: ["hero.png", "photo.jpeg"] })),
+  );
+  assert.throws(
+    () => validateArgs("demo-deployment", makeArgs({ referenceImages: ["hero.webp"] })),
+    /PNG or JPG\/JPEG/,
+  );
+});
+
+test("Azure image generation routes model to deployment and sends mapped quality", async (t) => {
+  useEnv(t, {
+    AZURE_OPENAI_API_KEY: "azure-key",
+    AZURE_OPENAI_BASE_URL: "https://example.openai.azure.com/openai/deployments/default-deploy",
+    AZURE_API_VERSION: null,
+    AZURE_OPENAI_DEPLOYMENT: null,
+    AZURE_OPENAI_IMAGE_MODEL: null,
+  });
+
+  const originalFetch = globalThis.fetch;
+  t.after(() => {
+    globalThis.fetch = originalFetch;
+  });
+
+  const calls: Array<{ url: string; body: string }> = [];
+  globalThis.fetch = async (input, init) => {
+    calls.push({
+      url: String(input),
+      body: String(init?.body ?? ""),
+    });
+    return Response.json({
+      data: [{ b64_json: Buffer.from("azure-image").toString("base64") }],
+    });
+  };
+
+  const bytes = await generateImage(
+    "A calm lake at sunset",
+    "custom-deploy",
+    makeArgs({ quality: "normal" }),
+  );
+
+  assert.equal(Buffer.from(bytes).toString("utf8"), "azure-image");
+  assert.equal(
+    calls[0]?.url,
+    "https://example.openai.azure.com/openai/deployments/custom-deploy/images/generations?api-version=2025-04-01-preview",
+  );
+
+  const body = JSON.parse(calls[0]!.body) as Record<string, string>;
+  assert.equal(body.quality, "medium");
+  assert.equal(body.size, "1024x1024");
+});
+
+test("Azure image edits include quality in multipart requests", async (t) => {
+  const root = await makeTempDir("baoyu-image-gen-azure-");
+  t.after(() => fs.rm(root, { recursive: true, force: true }));
+
+  const pngPath = path.join(root, "ref.png");
+  const jpgPath = path.join(root, "ref.jpg");
+  await fs.writeFile(pngPath, "png-bytes");
+  await fs.writeFile(jpgPath, "jpg-bytes");
+
+  useEnv(t, {
+    AZURE_OPENAI_API_KEY: "azure-key",
+    AZURE_OPENAI_BASE_URL: "https://example.openai.azure.com",
+    AZURE_API_VERSION: "2025-04-01-preview",
+    AZURE_OPENAI_DEPLOYMENT: null,
+    AZURE_OPENAI_IMAGE_MODEL: null,
+  });
+
+  const originalFetch = globalThis.fetch;
+  t.after(() => {
+    globalThis.fetch = originalFetch;
+  });
+
+  const calls: Array<{ url: string; form: FormData }> = [];
+  globalThis.fetch = async (input, init) => {
+    calls.push({
+      url: String(input),
+      form: init?.body as FormData,
+    });
+    return Response.json({
+      data: [{ b64_json: Buffer.from("edited-image").toString("base64") }],
+    });
+  };
+
+  const bytes = await generateImage(
+    "Add warm lighting",
+    "edit-deploy",
+    makeArgs({
+      quality: "2k",
+      referenceImages: [pngPath, jpgPath],
+    }),
+  );
+
+  assert.equal(Buffer.from(bytes).toString("utf8"), "edited-image");
+  assert.equal(
+    calls[0]?.url,
+    "https://example.openai.azure.com/openai/deployments/edit-deploy/images/edits?api-version=2025-04-01-preview",
+  );
+  assert.equal(calls[0]?.form.get("quality"), "high");
+  assert.equal(calls[0]?.form.get("size"), "1024x1024");
+  assert.equal(calls[0]?.form.getAll("image[]").length, 2);
+});
@@ -0,0 +1,192 @@
+import path from "node:path";
+import { readFile } from "node:fs/promises";
+import type { CliArgs } from "../types";
+import { getOpenAISize, extractImageFromResponse } from "./openai.ts";
+
+type OpenAIImageResponse = { data: Array<{ url?: string; b64_json?: string }> };
+type AzureEndpoint = {
+  resourceBaseURL: string;
+  deployment: string | null;
+};
+
+const DEFAULT_AZURE_API_VERSION = "2025-04-01-preview";
+const AZURE_EDIT_IMAGE_EXTENSIONS = new Set([".png", ".jpg", ".jpeg"]);
+
+export function parseAzureBaseURL(url: string): AzureEndpoint {
+  const parsed = new URL(url);
+  const trimmedPath = parsed.pathname.replace(/\/+$/, "");
+  const deploymentMatch = trimmedPath.match(/^(.*?)(?:\/openai)?\/deployments\/([^/]+)$/);
+
+  if (deploymentMatch) {
+    parsed.pathname = `${deploymentMatch[1] || ""}/openai`;
+    return {
+      resourceBaseURL: parsed.toString().replace(/\/+$/, ""),
+      deployment: decodeURIComponent(deploymentMatch[2]!),
+    };
+  }
+
+  parsed.pathname = trimmedPath.endsWith("/openai") ? trimmedPath : `${trimmedPath}/openai`;
+  return {
+    resourceBaseURL: parsed.toString().replace(/\/+$/, ""),
+    deployment: null,
+  };
+}
+
+export function getDefaultModel(): string {
+  const explicitDeployment = process.env.AZURE_OPENAI_DEPLOYMENT?.trim();
+  if (explicitDeployment) return explicitDeployment;
+
+  const baseURL = process.env.AZURE_OPENAI_BASE_URL;
+  if (baseURL) {
+    try {
+      const { deployment } = parseAzureBaseURL(baseURL);
+      if (deployment) return deployment;
+    } catch {
+      // Ignore invalid URLs here so the required-env check can raise the user-facing error later.
+    }
+  }
+
+  return process.env.AZURE_OPENAI_IMAGE_MODEL || "gpt-image-1.5";
+}
+
+function getEndpoint(): AzureEndpoint {
+  const url = process.env.AZURE_OPENAI_BASE_URL;
+  if (!url) {
+    throw new Error(
+      "AZURE_OPENAI_BASE_URL is required. Set it to your Azure resource or deployment endpoint, e.g.: https://your-resource.openai.azure.com or https://your-resource.openai.azure.com/openai/deployments/your-deployment"
+    );
+  }
+  return parseAzureBaseURL(url);
+}
+
+function getApiKey(): string {
+  const key = process.env.AZURE_OPENAI_API_KEY;
+  if (!key) {
+    throw new Error(
+      "AZURE_OPENAI_API_KEY is required. Get it from Azure Portal → your OpenAI resource → Keys and Endpoint."
+    );
+  }
+  return key;
+}
+
+function getApiVersion(): string {
+  return process.env.AZURE_API_VERSION || DEFAULT_AZURE_API_VERSION;
+}
+
+function getDeployment(model: string): string {
+  const deployment = model.trim();
+  if (!deployment) {
+    throw new Error(
+      "Azure deployment name is required. Use --model <deployment>, AZURE_OPENAI_DEPLOYMENT, AZURE_OPENAI_IMAGE_MODEL, or embed the deployment in AZURE_OPENAI_BASE_URL."
+    );
+  }
+  return deployment;
+}
+
+function buildURL(deployment: string, pathSuffix: string): string {
+  const { resourceBaseURL } = getEndpoint();
+  return `${resourceBaseURL}/deployments/${encodeURIComponent(deployment)}${pathSuffix}?api-version=${getApiVersion()}`;
+}
+
+function authHeaders(): Record<string, string> {
+  return { "api-key": getApiKey() };
+}
+
+function getAzureQuality(quality: CliArgs["quality"]): "medium" | "high" {
+  return quality === "2k" ? "high" : "medium";
+}
+
+export function validateArgs(_model: string, args: CliArgs): void {
+  for (const refPath of args.referenceImages) {
+    const ext = path.extname(refPath).toLowerCase();
+    if (!AZURE_EDIT_IMAGE_EXTENSIONS.has(ext)) {
+      throw new Error(
+        `Azure OpenAI reference images must be PNG or JPG/JPEG. Unsupported file: ${refPath}`
+      );
+    }
+  }
+}
+
+export async function generateImage(
+  prompt: string,
+  model: string,
+  args: CliArgs
+): Promise<Uint8Array> {
+  const deployment = getDeployment(model);
+  const size = args.size || getOpenAISize(model, args.aspectRatio, args.quality);
+
+  if (args.referenceImages.length > 0) {
+    return generateWithAzureEdits(prompt, deployment, size, args.referenceImages, args.quality);
+  }
+
+  return generateWithAzureGenerations(prompt, deployment, size, args.quality);
+}
+
+async function generateWithAzureGenerations(
+  prompt: string,
+  deployment: string,
+  size: string,
+  quality: CliArgs["quality"]
+): Promise<Uint8Array> {
+  const body: Record<string, any> = {
+    prompt,
+    size,
+    n: 1,
+    quality: getAzureQuality(quality),
+  };
+
+  const res = await fetch(buildURL(deployment, "/images/generations"), {
+    method: "POST",
+    headers: {
+      "Content-Type": "application/json",
+      ...authHeaders(),
+    },
+    body: JSON.stringify(body),
+  });
+
+  if (!res.ok) {
+    const err = await res.text();
+    throw new Error(`Azure OpenAI API error: ${err}`);
+  }
+
+  const result = (await res.json()) as OpenAIImageResponse;
+  return extractImageFromResponse(result);
+}
+
+async function generateWithAzureEdits(
+  prompt: string,
+  deployment: string,
+  size: string,
+  referenceImages: string[],
+  quality: CliArgs["quality"]
+): Promise<Uint8Array> {
+  const form = new FormData();
+  form.append("prompt", prompt);
+  form.append("size", size);
+  form.append("n", "1");
+  form.append("quality", getAzureQuality(quality));
+
+  for (const refPath of referenceImages) {
+    const bytes = await readFile(refPath);
+    const filename = path.basename(refPath);
+    const mimeType = path.extname(filename).toLowerCase() === ".png" ? "image/png" : "image/jpeg";
+    const blob = new Blob([bytes], { type: mimeType });
+    form.append("image[]", blob, filename);
+  }
+
+  const res = await fetch(buildURL(deployment, "/images/edits"), {
+    method: "POST",
+    headers: {
+      ...authHeaders(),
+    },
+    body: form,
+  });
+
+  if (!res.ok) {
+    const err = await res.text();
+    throw new Error(`Azure OpenAI edits API error: ${err}`);
+  }
+
+  const result = (await res.json()) as OpenAIImageResponse;
+  return extractImageFromResponse(result);
+}
@@ -0,0 +1,114 @@
+import assert from "node:assert/strict";
+import test, { type TestContext } from "node:test";
+
+import type { CliArgs } from "../types.ts";
+import { generateImage } from "./jimeng.ts";
+
+function makeArgs(overrides: Partial<CliArgs> = {}): CliArgs {
+  return {
+    prompt: null,
+    promptFiles: [],
+    imagePath: null,
+    provider: null,
+    model: null,
+    aspectRatio: null,
+    size: null,
+    quality: null,
+    imageSize: null,
+    referenceImages: [],
+    n: 1,
+    batchFile: null,
+    jobs: null,
+    json: false,
+    help: false,
+    ...overrides,
+  };
+}
+
+function useEnv(
+  t: TestContext,
+  values: Record<string, string | null>,
+): void {
+  const previous = new Map<string, string | undefined>();
+  for (const [key, value] of Object.entries(values)) {
+    previous.set(key, process.env[key]);
+    if (value == null) {
+      delete process.env[key];
+    } else {
+      process.env[key] = value;
+    }
+  }
+
+  t.after(() => {
+    for (const [key, value] of previous.entries()) {
+      if (value == null) {
+        delete process.env[key];
+      } else {
+        process.env[key] = value;
+      }
+    }
+  });
+}
+
+test("Jimeng submit request uses prompt field expected by current API", async (t) => {
+  useEnv(t, {
+    JIMENG_ACCESS_KEY_ID: "test-access-key",
+    JIMENG_SECRET_ACCESS_KEY: "test-secret-key",
+    JIMENG_BASE_URL: null,
+    JIMENG_REGION: null,
+  });
+
+  const originalFetch = globalThis.fetch;
+  t.after(() => {
+    globalThis.fetch = originalFetch;
+  });
+
+  const calls: Array<{
+    input: string;
+    init?: RequestInit;
+  }> = [];
+
+  globalThis.fetch = async (input, init) => {
+    calls.push({
+      input: String(input),
+      init,
+    });
+
+    if (calls.length === 1) {
+      return Response.json({
+        code: 10000,
+        data: {
+          task_id: "task-123",
+        },
+      });
+    }
+
+    return Response.json({
+      code: 10000,
+      data: {
+        status: "done",
+        binary_data_base64: [Buffer.from("jimeng-image").toString("base64")],
+      },
+    });
+  };
+
+  const image = await generateImage(
+    "A quiet bamboo forest",
+    "jimeng_t2i_v40",
+    makeArgs({ quality: "normal" }),
+  );
+
+  assert.equal(Buffer.from(image).toString("utf8"), "jimeng-image");
+  assert.equal(calls.length, 2);
+  assert.equal(
+    calls[0]?.input,
+    "https://visual.volcengineapi.com/?Action=CVSync2AsyncSubmitTask&Version=2022-08-31",
+  );
+
+  const submitBody = JSON.parse(String(calls[0]?.init?.body)) as Record<string, unknown>;
+  assert.equal(submitBody.req_key, "jimeng_t2i_v40");
+  assert.equal(submitBody.prompt, "A quiet bamboo forest");
+  assert.ok(!("prompt_text" in submitBody));
+  assert.equal(submitBody.width, 1024);
+  assert.equal(submitBody.height, 1024);
+});
@@ -246,7 +246,7 @@ async function submitTask(
  const [width, height] = size.split("x").map(Number);
  const bodyObj = {
    req_key: model,
-    prompt_text: prompt,
+    prompt,
    // Use separate width and height parameters instead of size string
    width: width,
    height: height,
@@ -0,0 +1,171 @@
+import assert from "node:assert/strict";
+import fs from "node:fs/promises";
+import os from "node:os";
+import path from "node:path";
+import test, { type TestContext } from "node:test";
+
+import type { CliArgs } from "../types.ts";
+import {
+  buildMinimaxUrl,
+  buildRequestBody,
+  buildSubjectReference,
+  extractImageFromResponse,
+  parsePixelSize,
+  validateArgs,
+} from "./minimax.ts";
+
+function useEnv(
+  t: TestContext,
+  values: Record<string, string | null>,
+): void {
+  const previous = new Map<string, string | undefined>();
+  for (const [key, value] of Object.entries(values)) {
+    previous.set(key, process.env[key]);
+    if (value == null) {
+      delete process.env[key];
+    } else {
+      process.env[key] = value;
+    }
+  }
+
+  t.after(() => {
+    for (const [key, value] of previous.entries()) {
+      if (value == null) {
+        delete process.env[key];
+      } else {
+        process.env[key] = value;
+      }
+    }
+  });
+}
+
+function makeArgs(overrides: Partial<CliArgs> = {}): CliArgs {
+  return {
+    prompt: null,
+    promptFiles: [],
+    imagePath: null,
+    provider: null,
+    model: null,
+    aspectRatio: null,
+    size: null,
+    quality: null,
+    imageSize: null,
+    referenceImages: [],
+    n: 1,
+    batchFile: null,
+    jobs: null,
+    json: false,
+    help: false,
+    ...overrides,
+  };
+}
+
+test("MiniMax URL builder normalizes /v1 suffixes", (t) => {
+  useEnv(t, { MINIMAX_BASE_URL: "https://api.minimax.io" });
+  assert.equal(buildMinimaxUrl(), "https://api.minimax.io/v1/image_generation");
+
+  process.env.MINIMAX_BASE_URL = "https://proxy.example.com/custom/v1/";
+  assert.equal(buildMinimaxUrl(), "https://proxy.example.com/custom/v1/image_generation");
+});
+
+test("MiniMax size parsing and validation follow documented constraints", () => {
+  assert.deepEqual(parsePixelSize("1536x1024"), { width: 1536, height: 1024 });
+  assert.deepEqual(parsePixelSize("1536*1024"), { width: 1536, height: 1024 });
+  assert.equal(parsePixelSize("wide"), null);
+
+  validateArgs("image-01", makeArgs({ size: "1536x1024", n: 9 }));
+
+  assert.throws(
+    () => validateArgs("image-01-live", makeArgs({ size: "1536x1024" })),
+    /only supported with model image-01/,
+  );
+  assert.throws(
+    () => validateArgs("image-01", makeArgs({ size: "1537x1024" })),
+    /divisible by 8/,
+  );
+  assert.throws(
+    () => validateArgs("image-01", makeArgs({ aspectRatio: "2.35:1" })),
+    /aspect_ratio must be one of/,
+  );
+  assert.throws(
+    () => validateArgs("image-01", makeArgs({ n: 10 })),
+    /at most 9 images/,
+  );
+});
+
+test("MiniMax request body maps aspect ratio, size, n, and subject references", async (t) => {
+  const dir = await fs.mkdtemp(path.join(os.tmpdir(), "minimax-test-"));
+  t.after(() => fs.rm(dir, { recursive: true, force: true }));
+
+  const refPath = path.join(dir, "portrait.png");
+  await fs.writeFile(refPath, Buffer.from("portrait"));
+
+  const ratioBody = await buildRequestBody(
+    "A portrait by the window",
+    "image-01",
+    makeArgs({ aspectRatio: "16:9", n: 2, referenceImages: [refPath] }),
+  );
+  assert.equal(ratioBody.aspect_ratio, "16:9");
+  assert.equal(ratioBody.n, 2);
+  assert.equal(ratioBody.response_format, "base64");
+  assert.match(ratioBody.subject_reference?.[0]?.image_file || "", /^data:image\/png;base64,/);
+
+  const sizeBody = await buildRequestBody(
+    "A portrait by the window",
+    "image-01",
+    makeArgs({ size: "1536x1024" }),
+  );
+  assert.equal(sizeBody.width, 1536);
+  assert.equal(sizeBody.height, 1024);
+  assert.equal(sizeBody.aspect_ratio, undefined);
+});
+
+test("MiniMax subject references require supported file types", async (t) => {
+  const dir = await fs.mkdtemp(path.join(os.tmpdir(), "minimax-ref-"));
+  t.after(() => fs.rm(dir, { recursive: true, force: true }));
+
+  const good = path.join(dir, "portrait.jpg");
+  const bad = path.join(dir, "portrait.webp");
+  await fs.writeFile(good, Buffer.from("portrait"));
+  await fs.writeFile(bad, Buffer.from("portrait"));
+
+  const subjectReference = await buildSubjectReference([good]);
+  assert.equal(subjectReference?.[0]?.type, "character");
+
+  await assert.rejects(
+    () => buildSubjectReference([bad]),
+    /only supports JPG, JPEG, or PNG/,
+  );
+});
+
+test("MiniMax response extraction supports base64 and URL payloads", async (t) => {
+  const originalFetch = globalThis.fetch;
+  t.after(() => {
+    globalThis.fetch = originalFetch;
+  });
+
+  const fromBase64 = await extractImageFromResponse({
+    data: {
+      image_base64: [Buffer.from("hello").toString("base64")],
+    },
+  });
+  assert.equal(Buffer.from(fromBase64).toString("utf8"), "hello");
+
+  globalThis.fetch = async () =>
+    new Response(Uint8Array.from([1, 2, 3]), {
+      status: 200,
+      headers: { "Content-Type": "image/jpeg" },
+    });
+
+  const fromUrl = await extractImageFromResponse({
+    data: {
+      image_urls: ["https://example.com/output.jpg"],
+    },
+  });
+  assert.deepEqual([...fromUrl], [1, 2, 3]);
+
+  await assert.rejects(
+    () => extractImageFromResponse({ base_resp: { status_code: 1001, status_msg: "blocked" } }),
+    /blocked/,
+  );
+});
@@ -0,0 +1,220 @@
+import path from "node:path";
+import { readFile } from "node:fs/promises";
+
+import type { CliArgs } from "../types";
+
+const DEFAULT_MODEL = "image-01";
+const MAX_REFERENCE_IMAGE_BYTES = 10 * 1024 * 1024;
+const SUPPORTED_ASPECT_RATIOS = new Set(["1:1", "16:9", "4:3", "3:2", "2:3", "3:4", "9:16", "21:9"]);
+
+type MinimaxSubjectReference = {
+  type: "character";
+  image_file: string;
+};
+
+type MinimaxRequestBody = {
+  model: string;
+  prompt: string;
+  response_format: "base64";
+  aspect_ratio?: string;
+  width?: number;
+  height?: number;
+  n?: number;
+  subject_reference?: MinimaxSubjectReference[];
+};
+
+type MinimaxResponse = {
+  id?: string;
+  data?: {
+    image_urls?: string[];
+    image_base64?: string[];
+  };
+  base_resp?: {
+    status_code?: number;
+    status_msg?: string;
+  };
+};
+
+export function getDefaultModel(): string {
+  return process.env.MINIMAX_IMAGE_MODEL || DEFAULT_MODEL;
+}
+
+function getApiKey(): string | null {
+  return process.env.MINIMAX_API_KEY || null;
+}
+
+export function buildMinimaxUrl(): string {
+  const base = (process.env.MINIMAX_BASE_URL || "https://api.minimax.io").replace(/\/+$/g, "");
+  return base.endsWith("/v1") ? `${base}/image_generation` : `${base}/v1/image_generation`;
+}
+
+function getMimeType(filename: string): "image/jpeg" | "image/png" {
+  const ext = path.extname(filename).toLowerCase();
+  if (ext === ".jpg" || ext === ".jpeg") return "image/jpeg";
+  if (ext === ".png") return "image/png";
+  throw new Error(
+    `MiniMax subject_reference only supports JPG, JPEG, or PNG files: ${filename}`
+  );
+}
+
+export function parsePixelSize(size: string): { width: number; height: number } | null {
+  const match = size.trim().match(/^(\d+)\s*[xX*]\s*(\d+)$/);
+  if (!match) return null;
+
+  const width = parseInt(match[1]!, 10);
+  const height = parseInt(match[2]!, 10);
+  if (!Number.isFinite(width) || !Number.isFinite(height) || width <= 0 || height <= 0) {
+    return null;
+  }
+
+  return { width, height };
+}
+
+function validatePixelSize(width: number, height: number): void {
+  if (width < 512 || width > 2048 || height < 512 || height > 2048) {
+    throw new Error("MiniMax custom size must keep width and height between 512 and 2048.");
+  }
+  if (width % 8 !== 0 || height % 8 !== 0) {
+    throw new Error("MiniMax custom size requires width and height divisible by 8.");
+  }
+}
+
+export function validateArgs(model: string, args: CliArgs): void {
+  if (args.n > 9) {
+    throw new Error("MiniMax supports at most 9 images per request.");
+  }
+
+  if (args.aspectRatio && !SUPPORTED_ASPECT_RATIOS.has(args.aspectRatio)) {
+    throw new Error(
+      `MiniMax aspect_ratio must be one of: ${Array.from(SUPPORTED_ASPECT_RATIOS).join(", ")}.`
+    );
+  }
+
+  if (args.size && !args.aspectRatio) {
+    if (model !== "image-01") {
+      throw new Error("MiniMax custom --size is only supported with model image-01. Use --model image-01 or pass --ar instead.");
+    }
+    const parsed = parsePixelSize(args.size);
+    if (!parsed) {
+      throw new Error("MiniMax --size must be in WxH format, for example 1536x1024.");
+    }
+    validatePixelSize(parsed.width, parsed.height);
+  }
+}
+
+export async function buildSubjectReference(
+  referenceImages: string[],
+): Promise<MinimaxSubjectReference[] | undefined> {
+  if (referenceImages.length === 0) return undefined;
+
+  const subjectReference: MinimaxSubjectReference[] = [];
+  for (const refPath of referenceImages) {
+    const bytes = await readFile(refPath);
+    if (bytes.length > MAX_REFERENCE_IMAGE_BYTES) {
+      throw new Error(`MiniMax subject_reference images must be smaller than 10MB: ${refPath}`);
+    }
+
+    subjectReference.push({
+      type: "character",
+      image_file: `data:${getMimeType(refPath)};base64,${bytes.toString("base64")}`,
+    });
+  }
+
+  return subjectReference;
+}
+
+export async function buildRequestBody(
+  prompt: string,
+  model: string,
+  args: CliArgs,
+): Promise<MinimaxRequestBody> {
+  validateArgs(model, args);
+
+  const body: MinimaxRequestBody = {
+    model,
+    prompt,
+    response_format: "base64",
+  };
+
+  if (args.aspectRatio) {
+    body.aspect_ratio = args.aspectRatio;
+  } else if (args.size) {
+    const parsed = parsePixelSize(args.size);
+    if (!parsed) {
+      throw new Error("MiniMax --size must be in WxH format, for example 1536x1024.");
+    }
+    body.width = parsed.width;
+    body.height = parsed.height;
+  }
+
+  if (args.n > 1) {
+    body.n = args.n;
+  }
+
+  const subjectReference = await buildSubjectReference(args.referenceImages);
+  if (subjectReference) {
+    body.subject_reference = subjectReference;
+  }
+
+  return body;
+}
+
+async function downloadImage(url: string): Promise<Uint8Array> {
+  const response = await fetch(url);
+  if (!response.ok) {
+    throw new Error(`Failed to download image from MiniMax: ${response.status}`);
+  }
+  return new Uint8Array(await response.arrayBuffer());
+}
+
+export async function extractImageFromResponse(result: MinimaxResponse): Promise<Uint8Array> {
+  const baseResp = result.base_resp;
+  if (baseResp && baseResp.status_code !== undefined && baseResp.status_code !== 0) {
+    throw new Error(baseResp.status_msg || `MiniMax API returned status_code=${baseResp.status_code}`);
+  }
+
+  const base64Image = result.data?.image_base64?.[0];
+  if (base64Image) {
+    return Uint8Array.from(Buffer.from(base64Image, "base64"));
+  }
+
+  const url = result.data?.image_urls?.[0];
+  if (url) {
+    return downloadImage(url);
+  }
+
+  throw new Error("No image data in MiniMax response");
+}
+
+export function getDefaultOutputExtension(): ".jpg" {
+  return ".jpg";
+}
+
+export async function generateImage(
+  prompt: string,
+  model: string,
+  args: CliArgs
+): Promise<Uint8Array> {
+  const apiKey = getApiKey();
+  if (!apiKey) {
+    throw new Error("MINIMAX_API_KEY is required. Get one from https://platform.minimax.io/");
+  }
+
+  const body = await buildRequestBody(prompt, model, args);
+  const response = await fetch(buildMinimaxUrl(), {
+    method: "POST",
+    headers: {
+      "Content-Type": "application/json",
+      Authorization: `Bearer ${apiKey}`,
+    },
+    body: JSON.stringify(body),
+  });
+
+  if (!response.ok) {
+    const err = await response.text();
+    throw new Error(`MiniMax API error (${response.status}): ${err}`);
+  }
+
+  const result = (await response.json()) as MinimaxResponse;
+  return extractImageFromResponse(result);
+}
@@ -0,0 +1,168 @@
+import assert from "node:assert/strict";
+import test from "node:test";
+
+import type { CliArgs } from "../types.ts";
+import {
+  buildContent,
+  buildRequestBody,
+  extractImageFromResponse,
+  getAspectRatio,
+  getImageSize,
+  validateArgs,
+} from "./openrouter.ts";
+
+const GEMINI_MODEL = "google/gemini-3.1-flash-image-preview";
+const GEMINI_25_MODEL = "google/gemini-2.5-flash-image";
+const GPT_5_IMAGE_MODEL = "openai/gpt-5-image";
+const OPENROUTER_AUTO_MODEL = "openrouter/auto";
+const FLUX_MODEL = "black-forest-labs/flux.2-pro";
+
+function makeArgs(overrides: Partial<CliArgs> = {}): CliArgs {
+  return {
+    prompt: null,
+    promptFiles: [],
+    imagePath: null,
+    provider: null,
+    model: null,
+    aspectRatio: null,
+    size: null,
+    quality: null,
+    imageSize: null,
+    referenceImages: [],
+    n: 1,
+    batchFile: null,
+    jobs: null,
+    json: false,
+    help: false,
+    ...overrides,
+  };
+}
+
+test("OpenRouter request body uses image_config and string content for text-only prompts", () => {
+  const args = makeArgs({ aspectRatio: "16:9", quality: "2k" });
+  const body = buildRequestBody("hello", GEMINI_MODEL, args, []);
+
+  assert.deepEqual(body.image_config, {
+    image_size: "2K",
+    aspect_ratio: "16:9",
+  });
+  assert.deepEqual(body.provider, {
+    require_parameters: true,
+  });
+  assert.deepEqual(body.modalities, ["image", "text"]);
+  assert.equal(body.stream, false);
+  assert.equal(body.messages[0].content, "hello");
+});
+
+test("OpenRouter request body keeps text+image modalities for current text+image models", () => {
+  for (const model of [GEMINI_MODEL, GEMINI_25_MODEL, GPT_5_IMAGE_MODEL, OPENROUTER_AUTO_MODEL]) {
+    const body = buildRequestBody("hello", model, makeArgs({ quality: "2k" }), []);
+
+    assert.deepEqual(body.image_config, {
+      image_size: "2K",
+    });
+    assert.deepEqual(body.provider, {
+      require_parameters: true,
+    });
+    assert.deepEqual(body.modalities, ["image", "text"]);
+    assert.equal(body.messages[0].content, "hello");
+  }
+});
+
+test("OpenRouter request body uses image-only modalities for image-only models under CLI defaults", () => {
+  const body = buildRequestBody("hello", FLUX_MODEL, makeArgs({ quality: "2k" }), []);
+
+  assert.deepEqual(body.image_config, {
+    image_size: "2K",
+  });
+  assert.deepEqual(body.provider, {
+    require_parameters: true,
+  });
+  assert.deepEqual(body.modalities, ["image"]);
+  assert.equal(body.stream, false);
+  assert.equal(body.messages[0].content, "hello");
+});
+
+test("OpenRouter helper omits image_config when no size or quality is passed", () => {
+  const body = buildRequestBody("hello", FLUX_MODEL, makeArgs(), []);
+
+  assert.equal(body.image_config, undefined);
+  assert.equal(body.provider, undefined);
+  assert.deepEqual(body.modalities, ["image"]);
+  assert.equal(body.stream, false);
+  assert.equal(body.messages[0].content, "hello");
+});
+
+test("OpenRouter request body keeps multimodal array content when references are provided", () => {
+  const content = buildContent("hello", ["data:image/png;base64,abc"]);
+  assert.ok(Array.isArray(content));
+  assert.deepEqual(content[0], { type: "text", text: "hello" });
+  assert.deepEqual(content[1], {
+    type: "image_url",
+    image_url: { url: "data:image/png;base64,abc" },
+  });
+});
+
+test("OpenRouter size and aspect helpers infer supported values", () => {
+  assert.equal(getImageSize(makeArgs()), null);
+  assert.equal(getImageSize(makeArgs({ quality: "normal" })), "1K");
+  assert.equal(getImageSize(makeArgs({ size: "2048x1024" })), "2K");
+  assert.equal(getAspectRatio(GEMINI_MODEL, makeArgs({ size: "1600x900" })), "16:9");
+  assert.equal(getAspectRatio(GEMINI_MODEL, makeArgs({ size: "1024x4096" })), "1:4");
+  assert.equal(getAspectRatio(GEMINI_25_MODEL, makeArgs({ size: "1600x900" })), "16:9");
+  assert.equal(getAspectRatio(FLUX_MODEL, makeArgs({ size: "1024x4096" })), null);
+});
+
+test("OpenRouter validates explicit aspect ratios and inferred size ratios against model support", () => {
+  assert.doesNotThrow(() =>
+    validateArgs(GEMINI_MODEL, makeArgs({ aspectRatio: "1:4" })),
+  );
+  assert.doesNotThrow(() =>
+    validateArgs(GEMINI_MODEL, makeArgs({ size: "1024x4096" })),
+  );
+  assert.throws(
+    () => validateArgs(GEMINI_25_MODEL, makeArgs({ aspectRatio: "1:4" })),
+    /does not support aspect ratio 1:4/,
+  );
+  assert.throws(
+    () => validateArgs(FLUX_MODEL, makeArgs({ aspectRatio: "1:4" })),
+    /does not support aspect ratio 1:4/,
+  );
+  assert.throws(
+    () => validateArgs(GEMINI_MODEL, makeArgs({ size: "2048x1024" })),
+    /does not support size 2048x1024 \(aspect ratio 2:1\)/,
+  );
+});
+
+test("OpenRouter response extraction supports inline image data and finish_reason errors", async () => {
+  const bytes = await extractImageFromResponse({
+    choices: [
+      {
+        message: {
+          images: [
+            {
+              image_url: {
+                url: `data:image/png;base64,${Buffer.from("hello").toString("base64")}`,
+              },
+            },
+          ],
+        },
+      },
+    ],
+  });
+  assert.equal(Buffer.from(bytes).toString("utf8"), "hello");
+
+  await assert.rejects(
+    () =>
+      extractImageFromResponse({
+        choices: [
+          {
+            finish_reason: "error",
+            native_finish_reason: "MALFORMED_FUNCTION_CALL",
+            message: { content: null },
+          },
+        ],
+      }),
+    /finish_reason=MALFORMED_FUNCTION_CALL/,
+  );
+});
@@ -3,6 +3,19 @@ import { readFile } from "node:fs/promises";
 import type { CliArgs } from "../types";

 const DEFAULT_MODEL = "google/gemini-3.1-flash-image-preview";
+const COMMON_ASPECT_RATIOS = [
+  "1:1",
+  "2:3",
+  "3:2",
+  "3:4",
+  "4:3",
+  "4:5",
+  "5:4",
+  "9:16",
+  "16:9",
+  "21:9",
+];
+const GEMINI_EXTENDED_ASPECT_RATIOS = ["1:4", "4:1", "1:8", "8:1"];

 type OpenRouterImageEntry = {
  image_url?: string | { url?: string | null } | null;
@@ -18,9 +31,11 @@ type OpenRouterMessagePart = {

 type OpenRouterResponse = {
  choices?: Array<{
+    finish_reason?: string | null;
+    native_finish_reason?: string | null;
    message?: {
      images?: OpenRouterImageEntry[];
-      content?: string | OpenRouterMessagePart[];
+      content?: string | OpenRouterMessagePart[] | null;
    };
  }>;
 };
@@ -29,6 +44,36 @@ export function getDefaultModel(): string {
  return process.env.OPENROUTER_IMAGE_MODEL || DEFAULT_MODEL;
 }

+function normalizeModelId(model: string): string {
+  return model.trim().toLowerCase().split(":")[0]!;
+}
+
+function isTextAndImageModel(model: string): boolean {
+  const normalized = normalizeModelId(model);
+  if (normalized === "openrouter/auto") {
+    return true;
+  }
+
+  if (normalized.startsWith("google/gemini-") && normalized.includes("image")) {
+    return true;
+  }
+
+  if (normalized.startsWith("openai/gpt-") && normalized.includes("image")) {
+    return true;
+  }
+
+  return false;
+}
+
+function getSupportedAspectRatios(model: string): Set<string> {
+  const normalized = normalizeModelId(model);
+  if (normalized !== "google/gemini-3.1-flash-image-preview") {
+    return new Set(COMMON_ASPECT_RATIOS);
+  }
+
+  return new Set([...COMMON_ASPECT_RATIOS, ...GEMINI_EXTENDED_ASPECT_RATIOS]);
+}
+
 function getApiKey(): string | null {
  return process.env.OPENROUTER_API_KEY || null;
 }
@@ -103,17 +148,50 @@ function inferImageSize(size: string | null): "1K" | "2K" | "4K" | null {
  return "4K";
 }

-function getImageSize(args: CliArgs): "1K" | "2K" | "4K" {
+export function getImageSize(args: CliArgs): "1K" | "2K" | "4K" | null {
  if (args.imageSize) return args.imageSize as "1K" | "2K" | "4K";

  const inferredFromSize = inferImageSize(args.size);
  if (inferredFromSize) return inferredFromSize;

-  return args.quality === "normal" ? "1K" : "2K";
+  if (args.quality === "normal") return "1K";
+  if (args.quality === "2k") return "2K";
+  return null;
 }

-function getAspectRatio(args: CliArgs): string | null {
-  return args.aspectRatio || inferAspectRatio(args.size);
+export function getAspectRatio(model: string, args: CliArgs): string | null {
+  if (args.aspectRatio) return args.aspectRatio;
+
+  const inferred = inferAspectRatio(args.size);
+  if (!inferred || !getSupportedAspectRatios(model).has(inferred)) {
+    return null;
+  }
+
+  return inferred;
+}
+
+function getModalities(model: string): string[] {
+  return isTextAndImageModel(model) ? ["image", "text"] : ["image"];
+}
+
+export function validateArgs(model: string, args: CliArgs): void {
+  const requestedAspectRatio = args.aspectRatio || inferAspectRatio(args.size);
+  if (!requestedAspectRatio) {
+    return;
+  }
+
+  const supported = getSupportedAspectRatios(model);
+  if (supported.has(requestedAspectRatio)) {
+    return;
+  }
+
+  const requestedValue = args.aspectRatio
+    ? `aspect ratio ${requestedAspectRatio}`
+    : `size ${args.size} (aspect ratio ${requestedAspectRatio})`;
+
+  throw new Error(
+    `OpenRouter model ${model} does not support ${requestedValue}. Supported values: ${Array.from(supported).join(", ")}`
+  );
 }

 function getMimeType(filename: string): string {
@@ -129,7 +207,14 @@ async function readImageAsDataUrl(filePath: string): Promise<string> {
  return `data:${getMimeType(filePath)};base64,${bytes.toString("base64")}`;
 }

-function buildContent(prompt: string, referenceImages: string[]): Array<Record<string, unknown>> {
+export function buildContent(
+  prompt: string,
+  referenceImages: string[],
+): string | Array<Record<string, unknown>> {
+  if (referenceImages.length === 0) {
+    return prompt;
+  }
+
  const content: Array<Record<string, unknown>> = [{ type: "text", text: prompt }];

  for (const imageUrl of referenceImages) {
@@ -171,8 +256,9 @@ async function downloadImage(value: string): Promise<Uint8Array> {
  return Uint8Array.from(Buffer.from(value, "base64"));
 }

-async function extractImageFromResponse(result: OpenRouterResponse): Promise<Uint8Array> {
-  const message = result.choices?.[0]?.message;
+export async function extractImageFromResponse(result: OpenRouterResponse): Promise<Uint8Array> {
+  const choice = result.choices?.[0];
+  const message = choice?.message;

  for (const image of message?.images ?? []) {
    const imageUrl = extractImageUrl(image);
@@ -194,7 +280,52 @@ async function extractImageFromResponse(result: OpenRouterResponse): Promise<Uin
    if (inline) return inline;
  }

-  throw new Error("No image in OpenRouter response");
+  const finishReason =
+    choice?.native_finish_reason || choice?.finish_reason || "unknown";
+  throw new Error(
+    `No image in OpenRouter response (finish_reason=${finishReason})`,
+  );
+}
+
+export function buildRequestBody(
+  prompt: string,
+  model: string,
+  args: CliArgs,
+  referenceImages: string[],
+): Record<string, unknown> {
+  validateArgs(model, args);
+
+  const imageConfig: Record<string, string> = {};
+
+  const imageSize = getImageSize(args);
+  if (imageSize) {
+    imageConfig.image_size = imageSize;
+  }
+
+  const aspectRatio = getAspectRatio(model, args);
+  if (aspectRatio) {
+    imageConfig.aspect_ratio = aspectRatio;
+  }
+
+  const body: Record<string, unknown> = {
+    messages: [
+      {
+        role: "user",
+        content: buildContent(prompt, referenceImages),
+      },
+    ],
+    modalities: getModalities(model),
+    stream: false,
+  };
+
+  if (Object.keys(imageConfig).length > 0) {
+    body.image_config = imageConfig;
+    body.provider = {
+      require_parameters: true,
+    };
+  }
+
+  return body;
 }

 export async function generateImage(
@@ -212,32 +343,15 @@ export async function generateImage(
    referenceImages.push(await readImageAsDataUrl(refPath));
  }

-  const imageGenerationOptions: Record<string, string> = {
-    size: getImageSize(args),
-  };
-
-  const aspectRatio = getAspectRatio(args);
-  if (aspectRatio) {
-    imageGenerationOptions.aspect_ratio = aspectRatio;
-  }
-
  const body = {
    model,
-    messages: [
-      {
-        role: "user",
-        content: buildContent(prompt, referenceImages),
-      },
-    ],
-    modalities: ["image", "text"],
-    max_tokens: 256,
-    imageGenerationOptions,
-    providerPreferences: {
-      require_parameters: true,
-    },
+    ...buildRequestBody(prompt, model, args, referenceImages),
  };

-  console.log(`Generating image with OpenRouter (${model})...`, imageGenerationOptions);
+  console.log(
+    `Generating image with OpenRouter (${model})...`,
+    (body.image_config as Record<string, string>),
+  );

  const response = await fetch(`${getBaseUrl()}/chat/completions`, {
    method: "POST",
@@ -1,4 +1,13 @@
-export type Provider = "google" | "openai" | "openrouter" | "dashscope" | "replicate" | "jimeng" | "seedream";
+export type Provider =
+  | "google"
+  | "openai"
+  | "openrouter"
+  | "dashscope"
+  | "minimax"
+  | "replicate"
+  | "jimeng"
+  | "seedream"
+  | "azure";
 export type Quality = "normal" | "2k";

 export type CliArgs = {
@@ -52,9 +61,11 @@ export type ExtendConfig = {
    openai: string | null;
    openrouter: string | null;
    dashscope: string | null;
+    minimax: string | null;
    replicate: string | null;
    jimeng: string | null;
    seedream: string | null;
+    azure: string | null;
  };
  batch?: {
    max_workers?: number | null;
@@ -0,0 +1,408 @@
+---
+name: baoyu-imagine
+description: AI image generation with OpenAI, Azure OpenAI, Google, OpenRouter, DashScope, MiniMax, Jimeng, Seedream and Replicate APIs. Supports text-to-image, reference images, aspect ratios, and batch generation from saved prompt files. Sequential by default; use batch parallel generation when the user already has multiple prompts or wants stable multi-image throughput. Use when user asks to generate, create, or draw images.
+version: 1.56.4
+metadata:
+  openclaw:
+    homepage: https://github.com/JimLiu/baoyu-skills#baoyu-imagine
+    requires:
+      anyBins:
+        - bun
+        - npx
+---
+
+# Image Generation (AI SDK)
+
+Official API-based image generation. Supports OpenAI, Azure OpenAI, Google, OpenRouter, DashScope (阿里通义万象), MiniMax, Jimeng (即梦), Seedream (豆包) and Replicate providers.
+
+## Script Directory
+
+**Agent Execution**:
+1. `{baseDir}` = this SKILL.md file's directory
+2. Script path = `{baseDir}/scripts/main.ts`
+3. Resolve `${BUN_X}` runtime: if `bun` installed → `bun`; if `npx` available → `npx -y bun`; else suggest installing bun
+
+## Step 0: Load Preferences ⛔ BLOCKING
+
+**CRITICAL**: This step MUST complete BEFORE any image generation. Do NOT skip or defer.
+
+Check EXTEND.md existence (priority: project → user):
+
+```bash
+# macOS, Linux, WSL, Git Bash
+test -f .baoyu-skills/baoyu-imagine/EXTEND.md && echo "project"
+test -f "${XDG_CONFIG_HOME:-$HOME/.config}/baoyu-skills/baoyu-imagine/EXTEND.md" && echo "xdg"
+test -f "$HOME/.baoyu-skills/baoyu-imagine/EXTEND.md" && echo "user"
+```
+
+```powershell
+# PowerShell (Windows)
+if (Test-Path .baoyu-skills/baoyu-imagine/EXTEND.md) { "project" }
+$xdg = if ($env:XDG_CONFIG_HOME) { $env:XDG_CONFIG_HOME } else { "$HOME/.config" }
+if (Test-Path "$xdg/baoyu-skills/baoyu-imagine/EXTEND.md") { "xdg" }
+if (Test-Path "$HOME/.baoyu-skills/baoyu-imagine/EXTEND.md") { "user" }
+```
+
+| Result | Action |
+|--------|--------|
+| Found | Load, parse, apply settings. If `default_model.[provider]` is null → ask model only (Flow 2) |
+| Not found | ⛔ Run first-time setup ([references/config/first-time-setup.md](references/config/first-time-setup.md)) → Save EXTEND.md → Then continue |
+
+**CRITICAL**: If not found, complete the full setup (provider + model + quality + save location) using AskUserQuestion BEFORE generating any images. Generation is BLOCKED until EXTEND.md is created.
+
+| Path | Location |
+|------|----------|
+| `.baoyu-skills/baoyu-imagine/EXTEND.md` | Project directory |
+| `$HOME/.baoyu-skills/baoyu-imagine/EXTEND.md` | User home |
+
+Legacy compatibility: if `.baoyu-skills/baoyu-image-gen/EXTEND.md` exists and the new path does not, runtime renames it to `baoyu-imagine`. If both files exist, runtime leaves them unchanged and uses the new path.
+
+**EXTEND.md Supports**: Default provider | Default quality | Default aspect ratio | Default image size | Default models | Batch worker cap | Provider-specific batch limits
+
+Schema: `references/config/preferences-schema.md`
+
+## Usage
+
+```bash
+# Basic
+${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image cat.png
+
+# With aspect ratio
+${BUN_X} {baseDir}/scripts/main.ts --prompt "A landscape" --image out.png --ar 16:9
+
+# High quality
+${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --quality 2k
+
+# From prompt files
+${BUN_X} {baseDir}/scripts/main.ts --promptfiles system.md content.md --image out.png
+
+# With reference images (Google, OpenAI, Azure OpenAI, OpenRouter, Replicate, MiniMax, or Seedream 4.0/4.5/5.0)
+${BUN_X} {baseDir}/scripts/main.ts --prompt "Make blue" --image out.png --ref source.png
+
+# With reference images (explicit provider/model)
+${BUN_X} {baseDir}/scripts/main.ts --prompt "Make blue" --image out.png --provider google --model gemini-3-pro-image-preview --ref source.png
+
+# Azure OpenAI (model means deployment name)
+${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider azure --model gpt-image-1.5
+
+# OpenRouter (recommended default model)
+${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider openrouter
+
+# OpenRouter with reference images
+${BUN_X} {baseDir}/scripts/main.ts --prompt "Make blue" --image out.png --provider openrouter --model google/gemini-3.1-flash-image-preview --ref source.png
+
+# Specific provider
+${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider openai
+
+# DashScope (阿里通义万象)
+${BUN_X} {baseDir}/scripts/main.ts --prompt "一只可爱的猫" --image out.png --provider dashscope
+
+# DashScope Qwen-Image 2.0 Pro (recommended for custom sizes and text rendering)
+${BUN_X} {baseDir}/scripts/main.ts --prompt "为咖啡品牌设计一张 21:9 横幅海报，包含清晰中文标题" --image out.png --provider dashscope --model qwen-image-2.0-pro --size 2048x872
+
+# DashScope legacy Qwen fixed-size model
+${BUN_X} {baseDir}/scripts/main.ts --prompt "一张电影感海报" --image out.png --provider dashscope --model qwen-image-max --size 1664x928
+
+# MiniMax
+${BUN_X} {baseDir}/scripts/main.ts --prompt "A fashion editorial portrait by a bright studio window" --image out.jpg --provider minimax
+
+# MiniMax with subject reference (best for character/portrait consistency)
+${BUN_X} {baseDir}/scripts/main.ts --prompt "A girl stands by the library window, cinematic lighting" --image out.jpg --provider minimax --model image-01 --ref portrait.png --ar 16:9
+
+# MiniMax with custom size (documented for image-01)
+${BUN_X} {baseDir}/scripts/main.ts --prompt "A cinematic poster" --image out.jpg --provider minimax --model image-01 --size 1536x1024
+
+# Replicate (google/nano-banana-pro)
+${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider replicate
+
+# Replicate with specific model
+${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider replicate --model google/nano-banana
+
+# Batch mode with saved prompt files
+${BUN_X} {baseDir}/scripts/main.ts --batchfile batch.json
+
+# Batch mode with explicit worker count
+${BUN_X} {baseDir}/scripts/main.ts --batchfile batch.json --jobs 4 --json
+```
+
+### Batch File Format
+
+```json
+{
+  "jobs": 4,
+  "tasks": [
+    {
+      "id": "hero",
+      "promptFiles": ["prompts/hero.md"],
+      "image": "out/hero.png",
+      "provider": "replicate",
+      "model": "google/nano-banana-pro",
+      "ar": "16:9",
+      "quality": "2k"
+    },
+    {
+      "id": "diagram",
+      "promptFiles": ["prompts/diagram.md"],
+      "image": "out/diagram.png",
+      "ref": ["references/original.png"]
+    }
+  ]
+}
+```
+
+Paths in `promptFiles`, `image`, and `ref` are resolved relative to the batch file's directory. `jobs` is optional (overridden by CLI `--jobs`). Top-level array format (without `jobs` wrapper) is also accepted.
+
+## Options
+
+| Option | Description |
+|--------|-------------|
+| `--prompt <text>`, `-p` | Prompt text |
+| `--promptfiles <files...>` | Read prompt from files (concatenated) |
+| `--image <path>` | Output image path (required in single-image mode) |
+| `--batchfile <path>` | JSON batch file for multi-image generation |
+| `--jobs <count>` | Worker count for batch mode (default: auto, max from config, built-in default 10) |
+| `--provider google\|openai\|azure\|openrouter\|dashscope\|minimax\|jimeng\|seedream\|replicate` | Force provider (default: auto-detect) |
+| `--model <id>`, `-m` | Model ID (Google: `gemini-3-pro-image-preview`; OpenAI: `gpt-image-1.5`; Azure: deployment name such as `gpt-image-1.5` or `image-prod`; OpenRouter: `google/gemini-3.1-flash-image-preview`; DashScope: `qwen-image-2.0-pro`; MiniMax: `image-01`) |
+| `--ar <ratio>` | Aspect ratio (e.g., `16:9`, `1:1`, `4:3`) |
+| `--size <WxH>` | Size (e.g., `1024x1024`) |
+| `--quality normal\|2k` | Quality preset (default: `2k`) |
+| `--imageSize 1K\|2K\|4K` | Image size for Google/OpenRouter (default: from quality) |
+| `--ref <files...>` | Reference images. Supported by Google multimodal, OpenAI GPT Image edits, Azure OpenAI edits (PNG/JPG only), OpenRouter multimodal models, Replicate, MiniMax subject-reference, and Seedream 5.0/4.5/4.0. Not supported by Jimeng, Seedream 3.0, or removed SeedEdit 3.0 |
+| `--n <count>` | Number of images |
+| `--json` | JSON output |
+
+## Environment Variables
+
+| Variable | Description |
+|----------|-------------|
+| `OPENAI_API_KEY` | OpenAI API key |
+| `AZURE_OPENAI_API_KEY` | Azure OpenAI API key |
+| `OPENROUTER_API_KEY` | OpenRouter API key |
+| `GOOGLE_API_KEY` | Google API key |
+| `DASHSCOPE_API_KEY` | DashScope API key (阿里云) |
+| `MINIMAX_API_KEY` | MiniMax API key |
+| `REPLICATE_API_TOKEN` | Replicate API token |
+| `JIMENG_ACCESS_KEY_ID` | Jimeng (即梦) Volcengine access key |
+| `JIMENG_SECRET_ACCESS_KEY` | Jimeng (即梦) Volcengine secret key |
+| `ARK_API_KEY` | Seedream (豆包) Volcengine ARK API key |
+| `OPENAI_IMAGE_MODEL` | OpenAI model override |
+| `AZURE_OPENAI_DEPLOYMENT` | Azure default deployment name |
+| `AZURE_OPENAI_IMAGE_MODEL` | Backward-compatible alias for Azure default deployment/model name |
+| `OPENROUTER_IMAGE_MODEL` | OpenRouter model override (default: `google/gemini-3.1-flash-image-preview`) |
+| `GOOGLE_IMAGE_MODEL` | Google model override |
+| `DASHSCOPE_IMAGE_MODEL` | DashScope model override (default: `qwen-image-2.0-pro`) |
+| `MINIMAX_IMAGE_MODEL` | MiniMax model override (default: `image-01`) |
+| `REPLICATE_IMAGE_MODEL` | Replicate model override (default: google/nano-banana-pro) |
+| `JIMENG_IMAGE_MODEL` | Jimeng model override (default: jimeng_t2i_v40) |
+| `SEEDREAM_IMAGE_MODEL` | Seedream model override (default: doubao-seedream-5-0-260128) |
+| `OPENAI_BASE_URL` | Custom OpenAI endpoint |
+| `AZURE_OPENAI_BASE_URL` | Azure resource endpoint or deployment endpoint |
+| `AZURE_API_VERSION` | Azure image API version (default: `2025-04-01-preview`) |
+| `OPENROUTER_BASE_URL` | Custom OpenRouter endpoint (default: `https://openrouter.ai/api/v1`) |
+| `OPENROUTER_HTTP_REFERER` | Optional app/site URL for OpenRouter attribution |
+| `OPENROUTER_TITLE` | Optional app name for OpenRouter attribution |
+| `GOOGLE_BASE_URL` | Custom Google endpoint |
+| `DASHSCOPE_BASE_URL` | Custom DashScope endpoint |
+| `MINIMAX_BASE_URL` | Custom MiniMax endpoint (default: `https://api.minimax.io`) |
+| `REPLICATE_BASE_URL` | Custom Replicate endpoint |
+| `JIMENG_BASE_URL` | Custom Jimeng endpoint (default: `https://visual.volcengineapi.com`) |
+| `JIMENG_REGION` | Jimeng region (default: `cn-north-1`) |
+| `SEEDREAM_BASE_URL` | Custom Seedream endpoint (default: `https://ark.cn-beijing.volces.com/api/v3`) |
+| `BAOYU_IMAGE_GEN_MAX_WORKERS` | Override batch worker cap |
+| `BAOYU_IMAGE_GEN_<PROVIDER>_CONCURRENCY` | Override provider concurrency, e.g. `BAOYU_IMAGE_GEN_REPLICATE_CONCURRENCY` |
+| `BAOYU_IMAGE_GEN_<PROVIDER>_START_INTERVAL_MS` | Override provider start gap, e.g. `BAOYU_IMAGE_GEN_REPLICATE_START_INTERVAL_MS` |
+
+**Load Priority**: CLI args > EXTEND.md > env vars > `<cwd>/.baoyu-skills/.env` > `~/.baoyu-skills/.env`
+
+## Model Resolution
+
+Model priority (highest → lowest), applies to all providers:
+
+1. CLI flag: `--model <id>`
+2. EXTEND.md: `default_model.[provider]`
+3. Env var: `<PROVIDER>_IMAGE_MODEL` (e.g., `GOOGLE_IMAGE_MODEL`)
+4. Built-in default
+
+For Azure, `--model` / `default_model.azure` should be the Azure deployment name. `AZURE_OPENAI_DEPLOYMENT` is the preferred env var, and `AZURE_OPENAI_IMAGE_MODEL` remains as a backward-compatible alias.
+
+**EXTEND.md overrides env vars**. If both EXTEND.md `default_model.google: "gemini-3-pro-image-preview"` and env var `GOOGLE_IMAGE_MODEL=gemini-3.1-flash-image-preview` exist, EXTEND.md wins.
+
+**Agent MUST display model info** before each generation:
+- Show: `Using [provider] / [model]`
+- Show switch hint: `Switch model: --model <id> | EXTEND.md default_model.[provider] | env <PROVIDER>_IMAGE_MODEL`
+
+### DashScope Models
+
+Use `--model qwen-image-2.0-pro` or set `default_model.dashscope` / `DASHSCOPE_IMAGE_MODEL` when the user wants official Qwen-Image behavior.
+
+Official DashScope model families:
+
+- `qwen-image-2.0-pro`, `qwen-image-2.0-pro-2026-03-03`, `qwen-image-2.0`, `qwen-image-2.0-2026-03-03`
+  - Free-form `size` in `宽*高` format
+  - Total pixels must stay between `512*512` and `2048*2048`
+  - Default size is approximately `1024*1024`
+  - Best choice for custom ratios such as `21:9` and text-heavy Chinese/English layouts
+- `qwen-image-max`, `qwen-image-max-2025-12-30`, `qwen-image-plus`, `qwen-image-plus-2026-01-09`, `qwen-image`
+  - Fixed sizes only: `1664*928`, `1472*1104`, `1328*1328`, `1104*1472`, `928*1664`
+  - Default size is `1664*928`
+  - `qwen-image` currently has the same capability as `qwen-image-plus`
+- Legacy DashScope models such as `z-image-turbo`, `z-image-ultra`, `wanx-v1`
+  - Keep using them only when the user explicitly asks for legacy behavior or compatibility
+
+When translating CLI args into DashScope behavior:
+
+- `--size` wins over `--ar`
+- For `qwen-image-2.0*`, prefer explicit `--size`; otherwise infer from `--ar` and use the official recommended resolutions below
+- For `qwen-image-max/plus/image`, only use the five official fixed sizes; if the requested ratio is not covered, switch to `qwen-image-2.0-pro`
+- `--quality` is a baoyu-imagine compatibility preset, not a native DashScope API field. Mapping `normal` / `2k` onto the `qwen-image-2.0*` table below is an implementation inference, not an official API guarantee
+
+Recommended `qwen-image-2.0*` sizes for common aspect ratios:
+
+| Ratio | `normal` | `2k` |
+|-------|----------|------|
+| `1:1` | `1024*1024` | `1536*1536` |
+| `2:3` | `768*1152` | `1024*1536` |
+| `3:2` | `1152*768` | `1536*1024` |
+| `3:4` | `960*1280` | `1080*1440` |
+| `4:3` | `1280*960` | `1440*1080` |
+| `9:16` | `720*1280` | `1080*1920` |
+| `16:9` | `1280*720` | `1920*1080` |
+| `21:9` | `1344*576` | `2048*872` |
+
+DashScope official APIs also expose `negative_prompt`, `prompt_extend`, and `watermark`, but `baoyu-imagine` does not expose them as dedicated CLI flags today.
+
+Official references:
+
+- [Qwen-Image API](https://help.aliyun.com/zh/model-studio/qwen-image-api)
+- [Text-to-image guide](https://help.aliyun.com/zh/model-studio/text-to-image)
+- [Qwen-Image Edit API](https://help.aliyun.com/zh/model-studio/qwen-image-edit-api)
+
+### MiniMax Models
+
+Use `--model image-01` or set `default_model.minimax` / `MINIMAX_IMAGE_MODEL` when the user wants MiniMax image generation.
+
+Official MiniMax image model options currently documented in the API reference:
+
+- `image-01` (recommended default)
+  - Supports text-to-image and subject-reference image generation
+  - Supports official `aspect_ratio` values: `1:1`, `16:9`, `4:3`, `3:2`, `2:3`, `3:4`, `9:16`, `21:9`
+  - Supports documented custom `width` / `height` output sizes when using `--size <WxH>`
+  - `width` and `height` must both be between `512` and `2048`, and both must be divisible by `8`
+- `image-01-live`
+  - Lower-latency variant
+  - Use `--ar` for sizing; MiniMax documents custom `width` / `height` as only effective for `image-01`
+
+MiniMax subject reference notes:
+
+- `--ref` files are sent as MiniMax `subject_reference`
+- MiniMax docs currently describe `subject_reference[].type` as `character`
+- Official docs say `image_file` supports public URLs or Base64 Data URLs; `baoyu-imagine` sends local refs as Data URLs
+- Official docs recommend front-facing portrait references in JPG/JPEG/PNG under 10MB
+
+Official references:
+
+- [MiniMax Image Generation Guide](https://platform.minimax.io/docs/guides/image-generation)
+- [MiniMax Text-to-Image API](https://platform.minimax.io/docs/api-reference/image-generation-t2i)
+- [MiniMax Image-to-Image API](https://platform.minimax.io/docs/api-reference/image-generation-i2i)
+
+### OpenRouter Models
+
+Use full OpenRouter model IDs, e.g.:
+
+- `google/gemini-3.1-flash-image-preview` (recommended, supports image output and reference-image workflows)
+- `google/gemini-2.5-flash-image-preview`
+- `black-forest-labs/flux.2-pro`
+- Other OpenRouter image-capable model IDs
+
+Notes:
+
+- OpenRouter image generation uses `/chat/completions`, not the OpenAI `/images` endpoints
+- If `--ref` is used, choose a multimodal model that supports image input and image output
+- `--imageSize` maps to OpenRouter `imageGenerationOptions.size`; `--size <WxH>` is converted to the nearest OpenRouter size and inferred aspect ratio when possible
+
+### Replicate Models
+
+Supported model formats:
+
+- `owner/name` (recommended for official models), e.g. `google/nano-banana-pro`
+- `owner/name:version` (community models by version), e.g. `stability-ai/sdxl:<version>`
+
+Examples:
+
+```bash
+# Use Replicate default model
+${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider replicate
+
+# Override model explicitly
+${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider replicate --model google/nano-banana
+```
+
+## Provider Selection
+
+1. `--ref` provided + no `--provider` → auto-select Google first, then OpenAI, then Azure, then OpenRouter, then Replicate, then Seedream, then MiniMax (MiniMax subject reference is more specialized toward character/portrait consistency)
+2. `--provider` specified → use it (if `--ref`, must be `google`, `openai`, `azure`, `openrouter`, `replicate`, `seedream`, or `minimax`)
+3. Only one API key available → use that provider
+4. Multiple available → default to Google
+
+## Quality Presets
+
+| Preset | Google imageSize | OpenAI Size | OpenRouter size | Replicate resolution | Use Case |
+|--------|------------------|-------------|-----------------|----------------------|----------|
+| `normal` | 1K | 1024px | 1K | 1K | Quick previews |
+| `2k` (default) | 2K | 2048px | 2K | 2K | Covers, illustrations, infographics |
+
+**Google/OpenRouter imageSize**: Can be overridden with `--imageSize 1K|2K|4K`
+
+## Aspect Ratios
+
+Supported: `1:1`, `16:9`, `9:16`, `4:3`, `3:4`, `2.35:1`
+
+- Google multimodal: uses `imageConfig.aspectRatio`
+- OpenAI: maps to closest supported size
+- OpenRouter: sends `imageGenerationOptions.aspect_ratio`; if only `--size <WxH>` is given, aspect ratio is inferred automatically
+- Replicate: passes `aspect_ratio` to model; when `--ref` is provided without `--ar`, defaults to `match_input_image`
+- MiniMax: sends official `aspect_ratio` values directly; if `--size <WxH>` is given without `--ar`, `width` / `height` are sent for `image-01`
+
+## Generation Mode
+
+**Default**: Sequential generation.
+
+**Batch Parallel Generation**: When `--batchfile` contains 2 or more pending tasks, the script automatically enables parallel generation.
+
+| Mode | When to Use |
+|------|-------------|
+| Sequential (default) | Normal usage, single images, small batches |
+| Parallel batch | Batch mode with 2+ tasks |
+
+Execution choice:
+
+| Situation | Preferred approach | Why |
+|-----------|--------------------|-----|
+| One image, or 1-2 simple images | Sequential | Lower coordination overhead and easier debugging |
+| Multiple images already have saved prompt files | Batch (`--batchfile`) | Reuses finalized prompts, applies shared throttling/retries, and gives predictable throughput |
+| Each image still needs separate reasoning, prompt writing, or style exploration | Subagents | The work is still exploratory, so each image may need independent analysis before generation |
+| Output comes from `baoyu-article-illustrator` with `outline.md` + `prompts/` | Batch (`build-batch.ts` -> `--batchfile`) | That workflow already produces prompt files, so direct batch execution is the intended path |
+
+Rule of thumb:
+
+- Prefer batch over subagents once prompt files are already saved and the task is "generate all of these"
+- Use subagents only when generation is coupled with per-image thinking, rewriting, or divergent creative exploration
+
+Parallel behavior:
+
+- Default worker count is automatic, capped by config, built-in default 10
+- Provider-specific throttling is applied only in batch mode, and the built-in defaults are tuned for faster throughput while still avoiding obvious RPM bursts
+- You can override worker count with `--jobs <count>`
+- Each image retries automatically up to 3 attempts
+- Final output includes success count, failure count, and per-image failure reasons
+
+## Error Handling
+
+- Missing API key → error with setup instructions
+- Generation failure → auto-retry up to 3 attempts per image
+- Invalid aspect ratio → warning, proceed with default
+- Reference images with unsupported provider/model → error with fix hint
+
+## Extension Support
+
+Custom configurations via EXTEND.md. See **Preferences** section for paths and supported options.
@@ -0,0 +1,316 @@
+---
+name: first-time-setup
+description: First-time setup and default model selection flow for baoyu-imagine
+---
+
+# First-Time Setup
+
+## Overview
+
+Triggered when:
+1. No EXTEND.md found → full setup (provider + model + preferences)
+2. EXTEND.md found but `default_model.[provider]` is null → model selection only
+
+## Setup Flow
+
+```
+No EXTEND.md found          EXTEND.md found, model null
+        │                            │
+        ▼                            ▼
+┌─────────────────────┐    ┌──────────────────────┐
+│ AskUserQuestion     │    │ AskUserQuestion      │
+│ (full setup)        │    │ (model only)         │
+└─────────────────────┘    └──────────────────────┘
+        │                            │
+        ▼                            ▼
+┌─────────────────────┐    ┌──────────────────────┐
+│ Create EXTEND.md    │    │ Update EXTEND.md     │
+└─────────────────────┘    └──────────────────────┘
+        │                            │
+        ▼                            ▼
+    Continue                     Continue
+```
+
+## Flow 1: No EXTEND.md (Full Setup)
+
+**Language**: Use user's input language or saved language preference.
+
+Use AskUserQuestion with ALL questions in ONE call:
+
+### Question 1: Default Provider
+
+```yaml
+header: "Provider"
+question: "Default image generation provider?"
+options:
+  - label: "Google (Recommended)"
+    description: "Gemini multimodal - high quality, reference images, flexible sizes"
+  - label: "OpenAI"
+    description: "GPT Image - consistent quality, reliable output"
+  - label: "Azure OpenAI"
+    description: "Azure-hosted GPT Image deployments with resource-specific routing"
+  - label: "OpenRouter"
+    description: "Router for Gemini/FLUX/OpenAI-compatible image models"
+  - label: "DashScope"
+    description: "Alibaba Cloud - Qwen-Image, strong Chinese/English text rendering"
+  - label: "MiniMax"
+    description: "MiniMax image generation with subject-reference character workflows"
+  - label: "Replicate"
+    description: "Community models - nano-banana-pro, flexible model selection"
+```
+
+### Question 2: Default Google Model
+
+Only show if user selected Google or auto-detect (no explicit provider).
+
+```yaml
+header: "Google Model"
+question: "Default Google image generation model?"
+options:
+  - label: "gemini-3-pro-image-preview (Recommended)"
+    description: "Highest quality, best for production use"
+  - label: "gemini-3.1-flash-image-preview"
+    description: "Fast generation, good quality, lower cost"
+  - label: "gemini-3-flash-preview"
+    description: "Fast generation, balanced quality and speed"
+```
+
+### Question 2b: Default OpenRouter Model
+
+Only show if user selected OpenRouter.
+
+```yaml
+header: "OpenRouter Model"
+question: "Default OpenRouter image generation model?"
+options:
+  - label: "google/gemini-3.1-flash-image-preview (Recommended)"
+    description: "Best general-purpose OpenRouter image model with reference-image workflows"
+  - label: "google/gemini-2.5-flash-image-preview"
+    description: "Fast Gemini preview model on OpenRouter"
+  - label: "black-forest-labs/flux.2-pro"
+    description: "Strong text-to-image quality through OpenRouter"
+```
+
+### Question 2c: Default Azure Deployment
+
+Only show if user selected Azure OpenAI.
+
+```yaml
+header: "Azure Deploy"
+question: "Default Azure image deployment name?"
+options:
+  - label: "gpt-image-1.5 (Recommended)"
+    description: "Best default if your Azure deployment uses the same name"
+  - label: "gpt-image-1"
+    description: "Previous GPT Image deployment name"
+```
+
+### Question 2d: Default MiniMax Model
+
+Only show if user selected MiniMax.
+
+```yaml
+header: "MiniMax Model"
+question: "Default MiniMax image generation model?"
+options:
+  - label: "image-01 (Recommended)"
+    description: "Best default, supports aspect ratios and custom width/height"
+  - label: "image-01-live"
+    description: "Faster variant, use aspect ratio instead of custom size"
+```
+
+### Question 3: Default Quality
+
+```yaml
+header: "Quality"
+question: "Default image quality?"
+options:
+  - label: "2k (Recommended)"
+    description: "2048px - covers, illustrations, infographics"
+  - label: "normal"
+    description: "1024px - quick previews, drafts"
+```
+
+### Question 4: Save Location
+
+```yaml
+header: "Save"
+question: "Where to save preferences?"
+options:
+  - label: "Project (Recommended)"
+    description: ".baoyu-skills/ (this project only)"
+  - label: "User"
+    description: "~/.baoyu-skills/ (all projects)"
+```
+
+### Save Locations
+
+| Choice | Path | Scope |
+|--------|------|-------|
+| Project | `.baoyu-skills/baoyu-imagine/EXTEND.md` | Current project |
+| User | `$HOME/.baoyu-skills/baoyu-imagine/EXTEND.md` | All projects |
+
+### EXTEND.md Template
+
+```yaml
+---
+version: 1
+default_provider: [selected provider or null]
+default_quality: [selected quality]
+default_aspect_ratio: null
+default_image_size: null
+default_model:
+  google: [selected google model or null]
+  openai: null
+  azure: [selected azure deployment or null]
+  openrouter: [selected openrouter model or null]
+  dashscope: null
+  minimax: [selected minimax model or null]
+  replicate: null
+---
+```
+
+## Flow 2: EXTEND.md Exists, Model Null
+
+When EXTEND.md exists but `default_model.[current_provider]` is null, ask ONLY the model question for the current provider.
+
+### Google Model Selection
+
+```yaml
+header: "Google Model"
+question: "Choose a default Google image generation model?"
+options:
+  - label: "gemini-3-pro-image-preview (Recommended)"
+    description: "Highest quality, best for production use"
+  - label: "gemini-3.1-flash-image-preview"
+    description: "Fast generation, good quality, lower cost"
+  - label: "gemini-3-flash-preview"
+    description: "Fast generation, balanced quality and speed"
+```
+
+### OpenAI Model Selection
+
+```yaml
+header: "OpenAI Model"
+question: "Choose a default OpenAI image generation model?"
+options:
+  - label: "gpt-image-1.5 (Recommended)"
+    description: "Latest GPT Image model, high quality"
+  - label: "gpt-image-1"
+    description: "Previous generation GPT Image model"
+```
+
+### Azure Deployment Selection
+
+```yaml
+header: "Azure Deploy"
+question: "Choose a default Azure image deployment name?"
+options:
+  - label: "gpt-image-1.5 (Recommended)"
+    description: "Use when your Azure deployment name matches the GPT-image-1.5 model"
+  - label: "gpt-image-1"
+    description: "Use when your Azure deployment name matches GPT-image-1"
+```
+
+Notes for Azure setup:
+
+- In `baoyu-imagine`, Azure `--model` / `default_model.azure` should be the Azure deployment name, not just the underlying model family.
+- If the deployment name is custom, save that exact deployment name in `default_model.azure`.
+
+### OpenRouter Model Selection
+
+```yaml
+header: "OpenRouter Model"
+question: "Choose a default OpenRouter image generation model?"
+options:
+  - label: "google/gemini-3.1-flash-image-preview (Recommended)"
+    description: "Recommended for image output and reference-image edits"
+  - label: "google/gemini-2.5-flash-image-preview"
+    description: "Fast preview-oriented image generation"
+  - label: "black-forest-labs/flux.2-pro"
+    description: "High-quality text-to-image through OpenRouter"
+```
+
+### DashScope Model Selection
+
+```yaml
+header: "DashScope Model"
+question: "Choose a default DashScope image generation model?"
+options:
+  - label: "qwen-image-2.0-pro (Recommended)"
+    description: "Best DashScope model for text rendering and custom sizes"
+  - label: "qwen-image-2.0"
+    description: "Faster 2.0 variant with flexible output size"
+  - label: "qwen-image-max"
+    description: "Legacy Qwen model with five fixed output sizes"
+  - label: "qwen-image-plus"
+    description: "Legacy Qwen model, same current capability as qwen-image"
+  - label: "z-image-turbo"
+    description: "Legacy DashScope model for compatibility"
+  - label: "z-image-ultra"
+    description: "Legacy DashScope model, higher quality but slower"
+```
+
+Notes for DashScope setup:
+
+- Prefer `qwen-image-2.0-pro` when the user needs custom `--size`, uncommon ratios like `21:9`, or strong Chinese/English text rendering.
+- `qwen-image-max` / `qwen-image-plus` / `qwen-image` only support five fixed sizes: `1664*928`, `1472*1104`, `1328*1328`, `1104*1472`, `928*1664`.
+- In `baoyu-imagine`, `quality` is a compatibility preset. It is not a native DashScope parameter.
+
+### Replicate Model Selection
+
+```yaml
+header: "Replicate Model"
+question: "Choose a default Replicate image generation model?"
+options:
+  - label: "google/nano-banana-pro (Recommended)"
+    description: "Google's fast image model on Replicate"
+  - label: "google/nano-banana"
+    description: "Google's base image model on Replicate"
+```
+
+### MiniMax Model Selection
+
+```yaml
+header: "MiniMax Model"
+question: "Choose a default MiniMax image generation model?"
+options:
+  - label: "image-01 (Recommended)"
+    description: "Best general-purpose MiniMax image model with custom width/height support"
+  - label: "image-01-live"
+    description: "Lower-latency MiniMax image model using aspect ratios"
+```
+
+Notes for MiniMax setup:
+
+- `image-01` is the safest default. It supports official `aspect_ratio` values and documented custom `width` / `height` output sizes.
+- `image-01-live` is useful when the user prefers faster generation and can work with aspect-ratio-based sizing.
+- MiniMax subject reference currently uses `subject_reference[].type = character`; docs recommend front-facing portrait references in JPG/JPEG/PNG under 10MB.
+
+### Update EXTEND.md
+
+After user selects a model:
+
+1. Read existing EXTEND.md
+2. If `default_model:` section exists → update the provider-specific key
+3. If `default_model:` section missing → add the full section:
+
+```yaml
+default_model:
+  google: [value or null]
+  openai: [value or null]
+  azure: [value or null]
+  openrouter: [value or null]
+  dashscope: [value or null]
+  minimax: [value or null]
+  replicate: [value or null]
+```
+
+Only set the selected provider's model; leave others as their current value or null.
+
+## After Setup
+
+1. Create directory if needed
+2. Write/update EXTEND.md with frontmatter
+3. Confirm: "Preferences saved to [path]"
+4. Continue with image generation
@@ -0,0 +1,121 @@
+---
+name: preferences-schema
+description: EXTEND.md YAML schema for baoyu-imagine user preferences
+---
+
+# Preferences Schema
+
+## Full Schema
+
+```yaml
+---
+version: 1
+
+default_provider: null      # google|openai|azure|openrouter|dashscope|minimax|replicate|null (null = auto-detect)
+
+default_quality: null       # normal|2k|null (null = use default: 2k)
+
+default_aspect_ratio: null  # "16:9"|"1:1"|"4:3"|"3:4"|"2.35:1"|null
+
+default_image_size: null    # 1K|2K|4K|null (Google/OpenRouter, overrides quality)
+
+default_model:
+  google: null              # e.g., "gemini-3-pro-image-preview", "gemini-3.1-flash-image-preview"
+  openai: null              # e.g., "gpt-image-1.5", "gpt-image-1"
+  azure: null               # Azure deployment name, e.g., "gpt-image-1.5" or "image-prod"
+  openrouter: null          # e.g., "google/gemini-3.1-flash-image-preview"
+  dashscope: null           # e.g., "qwen-image-2.0-pro"
+  minimax: null             # e.g., "image-01"
+  replicate: null           # e.g., "google/nano-banana-pro"
+
+batch:
+  max_workers: 10
+  provider_limits:
+    replicate:
+      concurrency: 5
+      start_interval_ms: 700
+    google:
+      concurrency: 3
+      start_interval_ms: 1100
+    openai:
+      concurrency: 3
+      start_interval_ms: 1100
+    azure:
+      concurrency: 3
+      start_interval_ms: 1100
+    openrouter:
+      concurrency: 3
+      start_interval_ms: 1100
+    dashscope:
+      concurrency: 3
+      start_interval_ms: 1100
+    minimax:
+      concurrency: 3
+      start_interval_ms: 1100
+---
+```
+
+## Field Reference
+
+| Field | Type | Default | Description |
+|-------|------|---------|-------------|
+| `version` | int | 1 | Schema version |
+| `default_provider` | string\|null | null | Default provider (null = auto-detect) |
+| `default_quality` | string\|null | null | Default quality (null = 2k) |
+| `default_aspect_ratio` | string\|null | null | Default aspect ratio |
+| `default_image_size` | string\|null | null | Google/OpenRouter image size (overrides quality) |
+| `default_model.google` | string\|null | null | Google default model |
+| `default_model.openai` | string\|null | null | OpenAI default model |
+| `default_model.azure` | string\|null | null | Azure default deployment name |
+| `default_model.openrouter` | string\|null | null | OpenRouter default model |
+| `default_model.dashscope` | string\|null | null | DashScope default model |
+| `default_model.minimax` | string\|null | null | MiniMax default model |
+| `default_model.replicate` | string\|null | null | Replicate default model |
+| `batch.max_workers` | int\|null | 10 | Batch worker cap |
+| `batch.provider_limits.<provider>.concurrency` | int\|null | provider default | Max simultaneous requests per provider |
+| `batch.provider_limits.<provider>.start_interval_ms` | int\|null | provider default | Minimum gap between request starts per provider |
+
+## Examples
+
+**Minimal**:
+```yaml
+---
+version: 1
+default_provider: google
+default_quality: 2k
+---
+```
+
+**Full**:
+```yaml
+---
+version: 1
+default_provider: google
+default_quality: 2k
+default_aspect_ratio: "16:9"
+default_image_size: 2K
+default_model:
+  google: "gemini-3-pro-image-preview"
+  openai: "gpt-image-1.5"
+  azure: "gpt-image-1.5"
+  openrouter: "google/gemini-3.1-flash-image-preview"
+  dashscope: "qwen-image-2.0-pro"
+  minimax: "image-01"
+  replicate: "google/nano-banana-pro"
+batch:
+  max_workers: 10
+  provider_limits:
+    replicate:
+      concurrency: 5
+      start_interval_ms: 700
+    azure:
+      concurrency: 3
+      start_interval_ms: 1100
+    openrouter:
+      concurrency: 3
+      start_interval_ms: 1100
+    minimax:
+      concurrency: 3
+      start_interval_ms: 1100
+---
+```
@@ -0,0 +1,468 @@
+import assert from "node:assert/strict";
+import fs from "node:fs/promises";
+import os from "node:os";
+import path from "node:path";
+import test, { type TestContext } from "node:test";
+
+import type { CliArgs, ExtendConfig } from "./types.ts";
+import {
+  createTaskArgs,
+  detectProvider,
+  getConfiguredMaxWorkers,
+  getConfiguredProviderRateLimits,
+  getWorkerCount,
+  isRetryableGenerationError,
+  loadBatchTasks,
+  loadExtendConfig,
+  mergeConfig,
+  normalizeOutputImagePath,
+  parseArgs,
+  parseSimpleYaml,
+} from "./main.ts";
+
+function makeArgs(overrides: Partial<CliArgs> = {}): CliArgs {
+  return {
+    prompt: null,
+    promptFiles: [],
+    imagePath: null,
+    provider: null,
+    model: null,
+    aspectRatio: null,
+    size: null,
+    quality: null,
+    imageSize: null,
+    referenceImages: [],
+    n: 1,
+    batchFile: null,
+    jobs: null,
+    json: false,
+    help: false,
+    ...overrides,
+  };
+}
+
+function useEnv(
+  t: TestContext,
+  values: Record<string, string | null>,
+): void {
+  const previous = new Map<string, string | undefined>();
+  for (const [key, value] of Object.entries(values)) {
+    previous.set(key, process.env[key]);
+    if (value == null) {
+      delete process.env[key];
+    } else {
+      process.env[key] = value;
+    }
+  }
+
+  t.after(() => {
+    for (const [key, value] of previous.entries()) {
+      if (value == null) {
+        delete process.env[key];
+      } else {
+        process.env[key] = value;
+      }
+    }
+  });
+}
+
+async function makeTempDir(prefix: string): Promise<string> {
+  return fs.mkdtemp(path.join(os.tmpdir(), prefix));
+}
+
+test("parseArgs parses the main baoyu-imagine CLI flags", () => {
+  const args = parseArgs([
+    "--promptfiles",
+    "prompts/system.md",
+    "prompts/content.md",
+    "--image",
+    "out/hero",
+    "--provider",
+    "openai",
+    "--quality",
+    "2k",
+    "--imageSize",
+    "4k",
+    "--ref",
+    "ref/one.png",
+    "ref/two.jpg",
+    "--n",
+    "3",
+    "--jobs",
+    "5",
+    "--json",
+  ]);
+
+  assert.deepEqual(args.promptFiles, ["prompts/system.md", "prompts/content.md"]);
+  assert.equal(args.imagePath, "out/hero");
+  assert.equal(args.provider, "openai");
+  assert.equal(args.quality, "2k");
+  assert.equal(args.imageSize, "4K");
+  assert.deepEqual(args.referenceImages, ["ref/one.png", "ref/two.jpg"]);
+  assert.equal(args.n, 3);
+  assert.equal(args.jobs, 5);
+  assert.equal(args.json, true);
+});
+
+test("parseArgs falls back to positional prompt and rejects invalid provider", () => {
+  const positional = parseArgs(["draw", "a", "cat"]);
+  assert.equal(positional.prompt, "draw a cat");
+
+  assert.throws(
+    () => parseArgs(["--provider", "stability"]),
+    /Invalid provider/,
+  );
+});
+
+test("parseSimpleYaml parses nested defaults and provider limits", () => {
+  const yaml = `
+version: 2
+default_provider: openrouter
+default_quality: normal
+default_aspect_ratio: '16:9'
+default_image_size: 2K
+default_model:
+  google: gemini-3-pro-image-preview
+  openai: gpt-image-1.5
+  azure: image-prod
+  minimax: image-01
+batch:
+  max_workers: 8
+  provider_limits:
+    google:
+      concurrency: 2
+      start_interval_ms: 900
+    openai:
+      concurrency: 4
+    minimax:
+      concurrency: 2
+      start_interval_ms: 1400
+    azure:
+      concurrency: 1
+      start_interval_ms: 1500
+`;
+
+  const config = parseSimpleYaml(yaml);
+
+  assert.equal(config.version, 2);
+  assert.equal(config.default_provider, "openrouter");
+  assert.equal(config.default_quality, "normal");
+  assert.equal(config.default_aspect_ratio, "16:9");
+  assert.equal(config.default_image_size, "2K");
+  assert.equal(config.default_model?.google, "gemini-3-pro-image-preview");
+  assert.equal(config.default_model?.openai, "gpt-image-1.5");
+  assert.equal(config.default_model?.azure, "image-prod");
+  assert.equal(config.default_model?.minimax, "image-01");
+  assert.equal(config.batch?.max_workers, 8);
+  assert.deepEqual(config.batch?.provider_limits?.google, {
+    concurrency: 2,
+    start_interval_ms: 900,
+  });
+  assert.deepEqual(config.batch?.provider_limits?.openai, {
+    concurrency: 4,
+  });
+  assert.deepEqual(config.batch?.provider_limits?.minimax, {
+    concurrency: 2,
+    start_interval_ms: 1400,
+  });
+  assert.deepEqual(config.batch?.provider_limits?.azure, {
+    concurrency: 1,
+    start_interval_ms: 1500,
+  });
+});
+
+test("loadExtendConfig renames legacy EXTEND.md when the new path is missing", async () => {
+  const root = await makeTempDir("baoyu-imagine-extend-");
+  const cwd = path.join(root, "project");
+  const home = path.join(root, "home");
+  const legacyPath = path.join(cwd, ".baoyu-skills", "baoyu-image-gen", "EXTEND.md");
+  const currentPath = path.join(cwd, ".baoyu-skills", "baoyu-imagine", "EXTEND.md");
+
+  await fs.mkdir(path.dirname(legacyPath), { recursive: true });
+  await fs.mkdir(home, { recursive: true });
+  await fs.writeFile(legacyPath, `---
+default_provider: google
+default_quality: 2k
+---
+`);
+
+  const config = await loadExtendConfig(cwd, home);
+
+  assert.equal(config.default_provider, "google");
+  assert.equal(config.default_quality, "2k");
+  await fs.access(currentPath);
+  await assert.rejects(() => fs.access(legacyPath));
+});
+
+test("loadExtendConfig leaves legacy EXTEND.md untouched when both paths exist", async () => {
+  const root = await makeTempDir("baoyu-imagine-extend-dual-");
+  const cwd = path.join(root, "project");
+  const home = path.join(root, "home");
+  const legacyPath = path.join(cwd, ".baoyu-skills", "baoyu-image-gen", "EXTEND.md");
+  const currentPath = path.join(cwd, ".baoyu-skills", "baoyu-imagine", "EXTEND.md");
+
+  await fs.mkdir(path.dirname(legacyPath), { recursive: true });
+  await fs.mkdir(path.dirname(currentPath), { recursive: true });
+  await fs.mkdir(home, { recursive: true });
+  await fs.writeFile(legacyPath, `---
+default_provider: google
+---
+`);
+  await fs.writeFile(currentPath, `---
+default_provider: openai
+---
+`);
+
+  const config = await loadExtendConfig(cwd, home);
+
+  assert.equal(config.default_provider, "openai");
+  assert.equal(await fs.readFile(legacyPath, "utf8"), `---
+default_provider: google
+---
+`);
+  assert.equal(await fs.readFile(currentPath, "utf8"), `---
+default_provider: openai
+---
+`);
+});
+
+test("mergeConfig only fills values missing from CLI args", () => {
+  const merged = mergeConfig(
+    makeArgs({
+      provider: "openai",
+      quality: null,
+      aspectRatio: null,
+      imageSize: "4K",
+    }),
+    {
+      default_provider: "google",
+      default_quality: "2k",
+      default_aspect_ratio: "3:2",
+      default_image_size: "2K",
+    } satisfies Partial<ExtendConfig>,
+  );
+
+  assert.equal(merged.provider, "openai");
+  assert.equal(merged.quality, "2k");
+  assert.equal(merged.aspectRatio, "3:2");
+  assert.equal(merged.imageSize, "4K");
+});
+
+test("detectProvider rejects non-ref-capable providers and prefers Google first when multiple keys exist", (t) => {
+  assert.throws(
+    () =>
+      detectProvider(
+        makeArgs({
+          provider: "dashscope",
+          referenceImages: ["ref.png"],
+        }),
+      ),
+    /Reference images require a ref-capable provider/,
+  );
+
+  useEnv(t, {
+    GOOGLE_API_KEY: "google-key",
+    OPENAI_API_KEY: "openai-key",
+    OPENROUTER_API_KEY: null,
+    DASHSCOPE_API_KEY: null,
+    MINIMAX_API_KEY: null,
+    REPLICATE_API_TOKEN: null,
+    JIMENG_ACCESS_KEY_ID: null,
+    JIMENG_SECRET_ACCESS_KEY: null,
+    ARK_API_KEY: null,
+  });
+  assert.equal(detectProvider(makeArgs()), "google");
+});
+
+test("detectProvider selects an available ref-capable provider for reference-image tasks", (t) => {
+  useEnv(t, {
+    GOOGLE_API_KEY: null,
+    OPENAI_API_KEY: "openai-key",
+    AZURE_OPENAI_API_KEY: null,
+    AZURE_OPENAI_BASE_URL: null,
+    OPENROUTER_API_KEY: null,
+    DASHSCOPE_API_KEY: null,
+    MINIMAX_API_KEY: null,
+    REPLICATE_API_TOKEN: null,
+    JIMENG_ACCESS_KEY_ID: null,
+    JIMENG_SECRET_ACCESS_KEY: null,
+    ARK_API_KEY: null,
+  });
+  assert.equal(
+    detectProvider(makeArgs({ referenceImages: ["ref.png"] })),
+    "openai",
+  );
+});
+
+test("detectProvider selects Azure when only Azure credentials are configured", (t) => {
+  useEnv(t, {
+    GOOGLE_API_KEY: null,
+    OPENAI_API_KEY: null,
+    AZURE_OPENAI_API_KEY: "azure-key",
+    AZURE_OPENAI_BASE_URL: "https://example.openai.azure.com",
+    OPENROUTER_API_KEY: null,
+    DASHSCOPE_API_KEY: null,
+    MINIMAX_API_KEY: null,
+    REPLICATE_API_TOKEN: null,
+    JIMENG_ACCESS_KEY_ID: null,
+    JIMENG_SECRET_ACCESS_KEY: null,
+    ARK_API_KEY: null,
+  });
+
+  assert.equal(detectProvider(makeArgs()), "azure");
+  assert.equal(
+    detectProvider(makeArgs({ referenceImages: ["ref.png"] })),
+    "azure",
+  );
+});
+
+test("detectProvider infers Seedream from model id and allows Seedream reference-image workflows", (t) => {
+  useEnv(t, {
+    GOOGLE_API_KEY: null,
+    OPENAI_API_KEY: null,
+    OPENROUTER_API_KEY: null,
+    DASHSCOPE_API_KEY: null,
+    MINIMAX_API_KEY: null,
+    REPLICATE_API_TOKEN: null,
+    JIMENG_ACCESS_KEY_ID: null,
+    JIMENG_SECRET_ACCESS_KEY: null,
+    ARK_API_KEY: "ark-key",
+  });
+
+  assert.equal(
+    detectProvider(
+      makeArgs({
+        model: "doubao-seedream-4-5-251128",
+        referenceImages: ["ref.png"],
+      }),
+    ),
+    "seedream",
+  );
+
+  assert.equal(
+    detectProvider(
+      makeArgs({
+        provider: "seedream",
+        referenceImages: ["ref.png"],
+      }),
+    ),
+    "seedream",
+  );
+});
+
+test("detectProvider selects MiniMax when only MiniMax credentials are configured or the model id matches", (t) => {
+  useEnv(t, {
+    GOOGLE_API_KEY: null,
+    OPENAI_API_KEY: null,
+    AZURE_OPENAI_API_KEY: null,
+    AZURE_OPENAI_BASE_URL: null,
+    OPENROUTER_API_KEY: null,
+    DASHSCOPE_API_KEY: null,
+    MINIMAX_API_KEY: "minimax-key",
+    REPLICATE_API_TOKEN: null,
+    JIMENG_ACCESS_KEY_ID: null,
+    JIMENG_SECRET_ACCESS_KEY: null,
+    ARK_API_KEY: null,
+  });
+
+  assert.equal(detectProvider(makeArgs()), "minimax");
+  assert.equal(detectProvider(makeArgs({ referenceImages: ["ref.png"] })), "minimax");
+  assert.equal(detectProvider(makeArgs({ model: "image-01-live" })), "minimax");
+});
+
+test("batch worker and provider-rate-limit configuration prefer env over EXTEND config", (t) => {
+  useEnv(t, {
+    BAOYU_IMAGE_GEN_MAX_WORKERS: "12",
+    BAOYU_IMAGE_GEN_GOOGLE_CONCURRENCY: "5",
+    BAOYU_IMAGE_GEN_GOOGLE_START_INTERVAL_MS: "450",
+  });
+
+  const extendConfig: Partial<ExtendConfig> = {
+    batch: {
+      max_workers: 7,
+      provider_limits: {
+        google: {
+          concurrency: 2,
+          start_interval_ms: 900,
+        },
+        minimax: {
+          concurrency: 1,
+          start_interval_ms: 1500,
+        },
+      },
+    },
+  };
+
+  assert.equal(getConfiguredMaxWorkers(extendConfig), 12);
+  assert.deepEqual(getConfiguredProviderRateLimits(extendConfig).google, {
+    concurrency: 5,
+    startIntervalMs: 450,
+  });
+  assert.deepEqual(getConfiguredProviderRateLimits(extendConfig).minimax, {
+    concurrency: 1,
+    startIntervalMs: 1500,
+  });
+});
+
+test("loadBatchTasks and createTaskArgs resolve batch-relative paths", async (t) => {
+  const root = await makeTempDir("baoyu-imagine-batch-");
+  t.after(() => fs.rm(root, { recursive: true, force: true }));
+
+  const batchFile = path.join(root, "jobs", "batch.json");
+  await fs.mkdir(path.dirname(batchFile), { recursive: true });
+  await fs.writeFile(
+    batchFile,
+    JSON.stringify({
+      jobs: 2,
+      tasks: [
+        {
+          id: "hero",
+          promptFiles: ["prompts/hero.md"],
+          image: "out/hero",
+          ref: ["refs/hero.png"],
+          ar: "16:9",
+        },
+      ],
+    }),
+  );
+
+  const loaded = await loadBatchTasks(batchFile);
+  assert.equal(loaded.jobs, 2);
+  assert.equal(loaded.batchDir, path.dirname(batchFile));
+  assert.equal(loaded.tasks[0]?.id, "hero");
+
+  const taskArgs = createTaskArgs(
+    makeArgs({
+      provider: "replicate",
+      quality: "2k",
+      json: true,
+    }),
+    loaded.tasks[0]!,
+    loaded.batchDir,
+  );
+
+  assert.deepEqual(taskArgs.promptFiles, [
+    path.join(loaded.batchDir, "prompts/hero.md"),
+  ]);
+  assert.equal(taskArgs.imagePath, path.join(loaded.batchDir, "out/hero"));
+  assert.deepEqual(taskArgs.referenceImages, [
+    path.join(loaded.batchDir, "refs/hero.png"),
+  ]);
+  assert.equal(taskArgs.provider, "replicate");
+  assert.equal(taskArgs.aspectRatio, "16:9");
+  assert.equal(taskArgs.quality, "2k");
+  assert.equal(taskArgs.json, true);
+});
+
+test("path normalization, worker count, and retry classification follow expected rules", () => {
+  assert.match(normalizeOutputImagePath("out/sample"), /out[\\/]+sample\.png$/);
+  assert.match(normalizeOutputImagePath("out/sample", ".jpg"), /out[\\/]+sample\.jpg$/);
+  assert.match(normalizeOutputImagePath("out/sample.webp"), /out[\\/]+sample\.webp$/);
+
+  assert.equal(getWorkerCount(8, null, 3), 3);
+  assert.equal(getWorkerCount(2, 6, 5), 2);
+  assert.equal(getWorkerCount(5, 0, 4), 1);
+
+  assert.equal(isRetryableGenerationError(new Error("API error (401): denied")), false);
+  assert.equal(isRetryableGenerationError(new Error("socket hang up")), true);
+});
@@ -0,0 +1,188 @@
+import assert from "node:assert/strict";
+import fs from "node:fs/promises";
+import os from "node:os";
+import path from "node:path";
+import test, { type TestContext } from "node:test";
+
+import type { CliArgs } from "../types.ts";
+import {
+  generateImage,
+  getDefaultModel,
+  parseAzureBaseURL,
+  validateArgs,
+} from "./azure.ts";
+
+function useEnv(
+  t: TestContext,
+  values: Record<string, string | null>,
+): void {
+  const previous = new Map<string, string | undefined>();
+  for (const [key, value] of Object.entries(values)) {
+    previous.set(key, process.env[key]);
+    if (value == null) {
+      delete process.env[key];
+    } else {
+      process.env[key] = value;
+    }
+  }
+
+  t.after(() => {
+    for (const [key, value] of previous.entries()) {
+      if (value == null) {
+        delete process.env[key];
+      } else {
+        process.env[key] = value;
+      }
+    }
+  });
+}
+
+function makeArgs(overrides: Partial<CliArgs> = {}): CliArgs {
+  return {
+    prompt: null,
+    promptFiles: [],
+    imagePath: null,
+    provider: null,
+    model: null,
+    aspectRatio: null,
+    size: null,
+    quality: null,
+    imageSize: null,
+    referenceImages: [],
+    n: 1,
+    batchFile: null,
+    jobs: null,
+    json: false,
+    help: false,
+    ...overrides,
+  };
+}
+
+async function makeTempDir(prefix: string): Promise<string> {
+  return fs.mkdtemp(path.join(os.tmpdir(), prefix));
+}
+
+test("Azure endpoint parsing and default deployment selection follow env precedence", (t) => {
+  assert.deepEqual(parseAzureBaseURL("https://example.openai.azure.com"), {
+    resourceBaseURL: "https://example.openai.azure.com/openai",
+    deployment: null,
+  });
+  assert.deepEqual(
+    parseAzureBaseURL("https://example.openai.azure.com/openai/deployments/from-url"),
+    {
+      resourceBaseURL: "https://example.openai.azure.com/openai",
+      deployment: "from-url",
+    },
+  );
+
+  useEnv(t, {
+    AZURE_OPENAI_BASE_URL: "https://example.openai.azure.com/openai/deployments/from-url",
+    AZURE_OPENAI_DEPLOYMENT: "explicit-deploy",
+    AZURE_OPENAI_IMAGE_MODEL: "env-fallback",
+  });
+  assert.equal(getDefaultModel(), "explicit-deploy");
+});
+
+test("Azure validateArgs rejects unsupported edit input formats before the API call", () => {
+  assert.doesNotThrow(() =>
+    validateArgs("demo-deployment", makeArgs({ referenceImages: ["hero.png", "photo.jpeg"] })),
+  );
+  assert.throws(
+    () => validateArgs("demo-deployment", makeArgs({ referenceImages: ["hero.webp"] })),
+    /PNG or JPG\/JPEG/,
+  );
+});
+
+test("Azure image generation routes model to deployment and sends mapped quality", async (t) => {
+  useEnv(t, {
+    AZURE_OPENAI_API_KEY: "azure-key",
+    AZURE_OPENAI_BASE_URL: "https://example.openai.azure.com/openai/deployments/default-deploy",
+    AZURE_API_VERSION: null,
+    AZURE_OPENAI_DEPLOYMENT: null,
+    AZURE_OPENAI_IMAGE_MODEL: null,
+  });
+
+  const originalFetch = globalThis.fetch;
+  t.after(() => {
+    globalThis.fetch = originalFetch;
+  });
+
+  const calls: Array<{ url: string; body: string }> = [];
+  globalThis.fetch = async (input, init) => {
+    calls.push({
+      url: String(input),
+      body: String(init?.body ?? ""),
+    });
+    return Response.json({
+      data: [{ b64_json: Buffer.from("azure-image").toString("base64") }],
+    });
+  };
+
+  const bytes = await generateImage(
+    "A calm lake at sunset",
+    "custom-deploy",
+    makeArgs({ quality: "normal" }),
+  );
+
+  assert.equal(Buffer.from(bytes).toString("utf8"), "azure-image");
+  assert.equal(
+    calls[0]?.url,
+    "https://example.openai.azure.com/openai/deployments/custom-deploy/images/generations?api-version=2025-04-01-preview",
+  );
+
+  const body = JSON.parse(calls[0]!.body) as Record<string, string>;
+  assert.equal(body.quality, "medium");
+  assert.equal(body.size, "1024x1024");
+});
+
+test("Azure image edits include quality in multipart requests", async (t) => {
+  const root = await makeTempDir("baoyu-imagine-azure-");
+  t.after(() => fs.rm(root, { recursive: true, force: true }));
+
+  const pngPath = path.join(root, "ref.png");
+  const jpgPath = path.join(root, "ref.jpg");
+  await fs.writeFile(pngPath, "png-bytes");
+  await fs.writeFile(jpgPath, "jpg-bytes");
+
+  useEnv(t, {
+    AZURE_OPENAI_API_KEY: "azure-key",
+    AZURE_OPENAI_BASE_URL: "https://example.openai.azure.com",
+    AZURE_API_VERSION: "2025-04-01-preview",
+    AZURE_OPENAI_DEPLOYMENT: null,
+    AZURE_OPENAI_IMAGE_MODEL: null,
+  });
+
+  const originalFetch = globalThis.fetch;
+  t.after(() => {
+    globalThis.fetch = originalFetch;
+  });
+
+  const calls: Array<{ url: string; form: FormData }> = [];
+  globalThis.fetch = async (input, init) => {
+    calls.push({
+      url: String(input),
+      form: init?.body as FormData,
+    });
+    return Response.json({
+      data: [{ b64_json: Buffer.from("edited-image").toString("base64") }],
+    });
+  };
+
+  const bytes = await generateImage(
+    "Add warm lighting",
+    "edit-deploy",
+    makeArgs({
+      quality: "2k",
+      referenceImages: [pngPath, jpgPath],
+    }),
+  );
+
+  assert.equal(Buffer.from(bytes).toString("utf8"), "edited-image");
+  assert.equal(
+    calls[0]?.url,
+    "https://example.openai.azure.com/openai/deployments/edit-deploy/images/edits?api-version=2025-04-01-preview",
+  );
+  assert.equal(calls[0]?.form.get("quality"), "high");
+  assert.equal(calls[0]?.form.get("size"), "1024x1024");
+  assert.equal(calls[0]?.form.getAll("image[]").length, 2);
+});
@@ -0,0 +1,192 @@
+import path from "node:path";
+import { readFile } from "node:fs/promises";
+import type { CliArgs } from "../types";
+import { getOpenAISize, extractImageFromResponse } from "./openai.ts";
+
+type OpenAIImageResponse = { data: Array<{ url?: string; b64_json?: string }> };
+type AzureEndpoint = {
+  resourceBaseURL: string;
+  deployment: string | null;
+};
+
+const DEFAULT_AZURE_API_VERSION = "2025-04-01-preview";
+const AZURE_EDIT_IMAGE_EXTENSIONS = new Set([".png", ".jpg", ".jpeg"]);
+
+export function parseAzureBaseURL(url: string): AzureEndpoint {
+  const parsed = new URL(url);
+  const trimmedPath = parsed.pathname.replace(/\/+$/, "");
+  const deploymentMatch = trimmedPath.match(/^(.*?)(?:\/openai)?\/deployments\/([^/]+)$/);
+
+  if (deploymentMatch) {
+    parsed.pathname = `${deploymentMatch[1] || ""}/openai`;
+    return {
+      resourceBaseURL: parsed.toString().replace(/\/+$/, ""),
+      deployment: decodeURIComponent(deploymentMatch[2]!),
+    };
+  }
+
+  parsed.pathname = trimmedPath.endsWith("/openai") ? trimmedPath : `${trimmedPath}/openai`;
+  return {
+    resourceBaseURL: parsed.toString().replace(/\/+$/, ""),
+    deployment: null,
+  };
+}
+
+export function getDefaultModel(): string {
+  const explicitDeployment = process.env.AZURE_OPENAI_DEPLOYMENT?.trim();
+  if (explicitDeployment) return explicitDeployment;
+
+  const baseURL = process.env.AZURE_OPENAI_BASE_URL;
+  if (baseURL) {
+    try {
+      const { deployment } = parseAzureBaseURL(baseURL);
+      if (deployment) return deployment;
+    } catch {
+      // Ignore invalid URLs here so the required-env check can raise the user-facing error later.
+    }
+  }
+
+  return process.env.AZURE_OPENAI_IMAGE_MODEL || "gpt-image-1.5";
+}
+
+function getEndpoint(): AzureEndpoint {
+  const url = process.env.AZURE_OPENAI_BASE_URL;
+  if (!url) {
+    throw new Error(
+      "AZURE_OPENAI_BASE_URL is required. Set it to your Azure resource or deployment endpoint, e.g.: https://your-resource.openai.azure.com or https://your-resource.openai.azure.com/openai/deployments/your-deployment"
+    );
+  }
+  return parseAzureBaseURL(url);
+}
+
+function getApiKey(): string {
+  const key = process.env.AZURE_OPENAI_API_KEY;
+  if (!key) {
+    throw new Error(
+      "AZURE_OPENAI_API_KEY is required. Get it from Azure Portal → your OpenAI resource → Keys and Endpoint."
+    );
+  }
+  return key;
+}
+
+function getApiVersion(): string {
+  return process.env.AZURE_API_VERSION || DEFAULT_AZURE_API_VERSION;
+}
+
+function getDeployment(model: string): string {
+  const deployment = model.trim();
+  if (!deployment) {
+    throw new Error(
+      "Azure deployment name is required. Use --model <deployment>, AZURE_OPENAI_DEPLOYMENT, AZURE_OPENAI_IMAGE_MODEL, or embed the deployment in AZURE_OPENAI_BASE_URL."
+    );
+  }
+  return deployment;
+}
+
+function buildURL(deployment: string, pathSuffix: string): string {
+  const { resourceBaseURL } = getEndpoint();
+  return `${resourceBaseURL}/deployments/${encodeURIComponent(deployment)}${pathSuffix}?api-version=${getApiVersion()}`;
+}
+
+function authHeaders(): Record<string, string> {
+  return { "api-key": getApiKey() };
+}
+
+function getAzureQuality(quality: CliArgs["quality"]): "medium" | "high" {
+  return quality === "2k" ? "high" : "medium";
+}
+
+export function validateArgs(_model: string, args: CliArgs): void {
+  for (const refPath of args.referenceImages) {
+    const ext = path.extname(refPath).toLowerCase();
+    if (!AZURE_EDIT_IMAGE_EXTENSIONS.has(ext)) {
+      throw new Error(
+        `Azure OpenAI reference images must be PNG or JPG/JPEG. Unsupported file: ${refPath}`
+      );
+    }
+  }
+}
+
+export async function generateImage(
+  prompt: string,
+  model: string,
+  args: CliArgs
+): Promise<Uint8Array> {
+  const deployment = getDeployment(model);
+  const size = args.size || getOpenAISize(model, args.aspectRatio, args.quality);
+
+  if (args.referenceImages.length > 0) {
+    return generateWithAzureEdits(prompt, deployment, size, args.referenceImages, args.quality);
+  }
+
+  return generateWithAzureGenerations(prompt, deployment, size, args.quality);
+}
+
+async function generateWithAzureGenerations(
+  prompt: string,
+  deployment: string,
+  size: string,
+  quality: CliArgs["quality"]
+): Promise<Uint8Array> {
+  const body: Record<string, any> = {
+    prompt,
+    size,
+    n: 1,
+    quality: getAzureQuality(quality),
+  };
+
+  const res = await fetch(buildURL(deployment, "/images/generations"), {
+    method: "POST",
+    headers: {
+      "Content-Type": "application/json",
+      ...authHeaders(),
+    },
+    body: JSON.stringify(body),
+  });
+
+  if (!res.ok) {
+    const err = await res.text();
+    throw new Error(`Azure OpenAI API error: ${err}`);
+  }
+
+  const result = (await res.json()) as OpenAIImageResponse;
+  return extractImageFromResponse(result);
+}
+
+async function generateWithAzureEdits(
+  prompt: string,
+  deployment: string,
+  size: string,
+  referenceImages: string[],
+  quality: CliArgs["quality"]
+): Promise<Uint8Array> {
+  const form = new FormData();
+  form.append("prompt", prompt);
+  form.append("size", size);
+  form.append("n", "1");
+  form.append("quality", getAzureQuality(quality));
+
+  for (const refPath of referenceImages) {
+    const bytes = await readFile(refPath);
+    const filename = path.basename(refPath);
+    const mimeType = path.extname(filename).toLowerCase() === ".png" ? "image/png" : "image/jpeg";
+    const blob = new Blob([bytes], { type: mimeType });
+    form.append("image[]", blob, filename);
+  }
+
+  const res = await fetch(buildURL(deployment, "/images/edits"), {
+    method: "POST",
+    headers: {
+      ...authHeaders(),
+    },
+    body: form,
+  });
+
+  if (!res.ok) {
+    const err = await res.text();
+    throw new Error(`Azure OpenAI edits API error: ${err}`);
+  }
+
+  const result = (await res.json()) as OpenAIImageResponse;
+  return extractImageFromResponse(result);
+}
@@ -0,0 +1,148 @@
+import assert from "node:assert/strict";
+import test, { type TestContext } from "node:test";
+
+import {
+  getDefaultModel,
+  getModelFamily,
+  getQwen2SizeFromAspectRatio,
+  getSizeFromAspectRatio,
+  normalizeSize,
+  parseAspectRatio,
+  parseSize,
+  resolveSizeForModel,
+} from "./dashscope.ts";
+
+function useEnv(
+  t: TestContext,
+  values: Record<string, string | null>,
+): void {
+  const previous = new Map<string, string | undefined>();
+  for (const [key, value] of Object.entries(values)) {
+    previous.set(key, process.env[key]);
+    if (value == null) {
+      delete process.env[key];
+    } else {
+      process.env[key] = value;
+    }
+  }
+
+  t.after(() => {
+    for (const [key, value] of previous.entries()) {
+      if (value == null) {
+        delete process.env[key];
+      } else {
+        process.env[key] = value;
+      }
+    }
+  });
+}
+
+test("DashScope default model prefers env override and otherwise uses qwen-image-2.0-pro", (t) => {
+  useEnv(t, { DASHSCOPE_IMAGE_MODEL: null });
+  assert.equal(getDefaultModel(), "qwen-image-2.0-pro");
+
+  process.env.DASHSCOPE_IMAGE_MODEL = "qwen-image-max";
+  assert.equal(getDefaultModel(), "qwen-image-max");
+});
+
+test("DashScope aspect-ratio parsing accepts numeric ratios only", () => {
+  assert.deepEqual(parseAspectRatio("3:2"), { width: 3, height: 2 });
+  assert.equal(parseAspectRatio("square"), null);
+  assert.equal(parseAspectRatio("-1:2"), null);
+});
+
+test("DashScope model family routing distinguishes qwen-2.0, fixed-size qwen, and legacy models", () => {
+  assert.equal(getModelFamily("qwen-image-2.0-pro"), "qwen2");
+  assert.equal(getModelFamily("qwen-image"), "qwenFixed");
+  assert.equal(getModelFamily("z-image-turbo"), "legacy");
+  assert.equal(getModelFamily("wanx-v1"), "legacy");
+});
+
+test("Legacy DashScope size selection keeps the previous quality-based heuristic", () => {
+  assert.equal(getSizeFromAspectRatio(null, "normal"), "1024*1024");
+  assert.equal(getSizeFromAspectRatio("16:9", "normal"), "1280*720");
+  assert.equal(getSizeFromAspectRatio("16:9", "2k"), "2048*1152");
+  assert.equal(getSizeFromAspectRatio("invalid", "2k"), "1536*1536");
+});
+
+test("Qwen 2.0 recommended sizes follow the official common-ratio table", () => {
+  assert.equal(getQwen2SizeFromAspectRatio(null, "normal"), "1024*1024");
+  assert.equal(getQwen2SizeFromAspectRatio(null, "2k"), "1536*1536");
+  assert.equal(getQwen2SizeFromAspectRatio("16:9", "normal"), "1280*720");
+  assert.equal(getQwen2SizeFromAspectRatio("21:9", "2k"), "2048*872");
+});
+
+test("Qwen 2.0 derives free-form sizes within pixel budget for uncommon ratios", () => {
+  const size = getQwen2SizeFromAspectRatio("5:2", "normal");
+  const parsed = parseSize(size);
+  assert.ok(parsed);
+  assert.ok(parsed.width * parsed.height >= 512 * 512);
+  assert.ok(parsed.width * parsed.height <= 2048 * 2048);
+  assert.ok(Math.abs(parsed.width / parsed.height - 2.5) < 0.08);
+});
+
+test("resolveSizeForModel validates explicit qwen-image-2.0 sizes by total pixels", () => {
+  assert.equal(
+    resolveSizeForModel("qwen-image-2.0-pro", {
+      size: "2048x872",
+      aspectRatio: null,
+      quality: "2k",
+    }),
+    "2048*872",
+  );
+
+  assert.throws(
+    () =>
+      resolveSizeForModel("qwen-image-2.0-pro", {
+        size: "4096x4096",
+        aspectRatio: null,
+        quality: "2k",
+      }),
+    /total pixels between/,
+  );
+});
+
+test("resolveSizeForModel enforces fixed sizes for qwen-image-max/plus/image", () => {
+  assert.equal(
+    resolveSizeForModel("qwen-image-max", {
+      size: null,
+      aspectRatio: "1:1",
+      quality: "2k",
+    }),
+    "1328*1328",
+  );
+
+  assert.equal(
+    resolveSizeForModel("qwen-image", {
+      size: "1664x928",
+      aspectRatio: "9:16",
+      quality: "normal",
+    }),
+    "1664*928",
+  );
+
+  assert.throws(
+    () =>
+      resolveSizeForModel("qwen-image-max", {
+        size: null,
+        aspectRatio: "21:9",
+        quality: "2k",
+      }),
+    /supports only fixed ratios/,
+  );
+
+  assert.throws(
+    () =>
+      resolveSizeForModel("qwen-image-plus", {
+        size: "1024x1024",
+        aspectRatio: null,
+        quality: "2k",
+      }),
+    /support only these sizes/,
+  );
+});
+
+test("DashScope size normalization converts WxH into provider format", () => {
+  assert.equal(normalizeSize("1024x1024"), "1024*1024");
+  assert.equal(normalizeSize("2048*1152"), "2048*1152");
+});
@@ -0,0 +1,463 @@
+import type { CliArgs, Quality } from "../types";
+
+type DashScopeModelFamily = "qwen2" | "qwenFixed" | "legacy";
+
+type DashScopeModelSpec = {
+  family: DashScopeModelFamily;
+  defaultSize: string;
+};
+
+const DEFAULT_MODEL = "qwen-image-2.0-pro";
+const MIN_QWEN_2_TOTAL_PIXELS = 512 * 512;
+const MAX_QWEN_2_TOTAL_PIXELS = 2048 * 2048;
+const SIZE_STEP = 16;
+const QWEN_NEGATIVE_PROMPT =
+  "低分辨率，低画质，肢体畸形，手指畸形，画面过饱和，蜡像感，人脸无细节，过度光滑，画面具有AI感，构图混乱，文字模糊，扭曲";
+
+const QWEN_2_TARGET_PIXELS: Record<Quality, number> = {
+  normal: 1024 * 1024,
+  "2k": 1536 * 1536,
+};
+
+const QWEN_2_RECOMMENDED: Record<string, Record<Quality, string>> = {
+  "1:1": { normal: "1024*1024", "2k": "1536*1536" },
+  "2:3": { normal: "768*1152", "2k": "1024*1536" },
+  "3:2": { normal: "1152*768", "2k": "1536*1024" },
+  "3:4": { normal: "960*1280", "2k": "1080*1440" },
+  "4:3": { normal: "1280*960", "2k": "1440*1080" },
+  "9:16": { normal: "720*1280", "2k": "1080*1920" },
+  "16:9": { normal: "1280*720", "2k": "1920*1080" },
+  "21:9": { normal: "1344*576", "2k": "2048*872" },
+};
+
+const QWEN_FIXED_SIZES_BY_RATIO: Record<string, string> = {
+  "16:9": "1664*928",
+  "4:3": "1472*1104",
+  "1:1": "1328*1328",
+  "3:4": "1104*1472",
+  "9:16": "928*1664",
+};
+
+const QWEN_FIXED_SIZES = Object.values(QWEN_FIXED_SIZES_BY_RATIO);
+
+const LEGACY_STANDARD_SIZES: [number, number][] = [
+  [1024, 1024],
+  [1280, 720],
+  [720, 1280],
+  [1024, 768],
+  [768, 1024],
+  [1536, 1024],
+  [1024, 1536],
+  [1536, 864],
+  [864, 1536],
+];
+
+const LEGACY_STANDARD_SIZES_2K: [number, number][] = [
+  [1536, 1536],
+  [2048, 1152],
+  [1152, 2048],
+  [1536, 1024],
+  [1024, 1536],
+  [1536, 864],
+  [864, 1536],
+  [2048, 2048],
+];
+
+const QWEN_2_SPEC: DashScopeModelSpec = {
+  family: "qwen2",
+  defaultSize: "1024*1024",
+};
+
+const QWEN_FIXED_SPEC: DashScopeModelSpec = {
+  family: "qwenFixed",
+  defaultSize: QWEN_FIXED_SIZES_BY_RATIO["16:9"],
+};
+
+const LEGACY_SPEC: DashScopeModelSpec = {
+  family: "legacy",
+  defaultSize: "1536*1536",
+};
+
+const MODEL_SPEC_ALIASES: Record<string, DashScopeModelSpec> = {
+  "qwen-image-2.0-pro": QWEN_2_SPEC,
+  "qwen-image-2.0-pro-2026-03-03": QWEN_2_SPEC,
+  "qwen-image-2.0": QWEN_2_SPEC,
+  "qwen-image-2.0-2026-03-03": QWEN_2_SPEC,
+  "qwen-image-max": QWEN_FIXED_SPEC,
+  "qwen-image-max-2025-12-30": QWEN_FIXED_SPEC,
+  "qwen-image-plus": QWEN_FIXED_SPEC,
+  "qwen-image-plus-2026-01-09": QWEN_FIXED_SPEC,
+  "qwen-image": QWEN_FIXED_SPEC,
+};
+
+export function getDefaultModel(): string {
+  return process.env.DASHSCOPE_IMAGE_MODEL || DEFAULT_MODEL;
+}
+
+function getApiKey(): string | null {
+  return process.env.DASHSCOPE_API_KEY || null;
+}
+
+function getBaseUrl(): string {
+  const base = process.env.DASHSCOPE_BASE_URL || "https://dashscope.aliyuncs.com";
+  return base.replace(/\/+$/g, "");
+}
+
+function getModelSpec(model: string): DashScopeModelSpec {
+  return MODEL_SPEC_ALIASES[model.trim().toLowerCase()] || LEGACY_SPEC;
+}
+
+export function getModelFamily(model: string): DashScopeModelFamily {
+  return getModelSpec(model).family;
+}
+
+function normalizeQuality(quality: CliArgs["quality"]): Quality {
+  return quality === "normal" ? "normal" : "2k";
+}
+
+export function parseAspectRatio(ar: string): { width: number; height: number } | null {
+  const match = ar.match(/^(\d+(?:\.\d+)?):(\d+(?:\.\d+)?)$/);
+  if (!match) return null;
+  const w = parseFloat(match[1]!);
+  const h = parseFloat(match[2]!);
+  if (w <= 0 || h <= 0) return null;
+  return { width: w, height: h };
+}
+
+export function normalizeSize(size: string): string {
+  return size.replace("x", "*");
+}
+
+export function parseSize(size: string): { width: number; height: number } | null {
+  const match = normalizeSize(size).match(/^(\d+)\*(\d+)$/);
+  if (!match) return null;
+  const width = Number(match[1]);
+  const height = Number(match[2]);
+  if (!Number.isFinite(width) || !Number.isFinite(height) || width <= 0 || height <= 0) {
+    return null;
+  }
+  return { width, height };
+}
+
+function formatSize(width: number, height: number): string {
+  return `${width}*${height}`;
+}
+
+function getRatioValue(ar: string): number | null {
+  const parsed = parseAspectRatio(ar);
+  if (!parsed) return null;
+  return parsed.width / parsed.height;
+}
+
+function findKnownRatioKey(ar: string, candidates: string[], tolerance = 0.02): string | null {
+  const targetRatio = getRatioValue(ar);
+  if (targetRatio == null) return null;
+
+  let bestKey: string | null = null;
+  let bestDiff = Infinity;
+
+  for (const candidate of candidates) {
+    const candidateRatio = getRatioValue(candidate);
+    if (candidateRatio == null) continue;
+    const diff = Math.abs(candidateRatio - targetRatio);
+    if (diff < bestDiff) {
+      bestDiff = diff;
+      bestKey = candidate;
+    }
+  }
+
+  return bestDiff <= tolerance ? bestKey : null;
+}
+
+function roundToStep(value: number): number {
+  return Math.max(SIZE_STEP, Math.round(value / SIZE_STEP) * SIZE_STEP);
+}
+
+function fitToPixelBudget(
+  width: number,
+  height: number,
+  minPixels: number,
+  maxPixels: number,
+): { width: number; height: number } {
+  let nextWidth = width;
+  let nextHeight = height;
+  let pixels = nextWidth * nextHeight;
+
+  if (pixels > maxPixels) {
+    const scale = Math.sqrt(maxPixels / pixels);
+    nextWidth *= scale;
+    nextHeight *= scale;
+  } else if (pixels < minPixels) {
+    const scale = Math.sqrt(minPixels / pixels);
+    nextWidth *= scale;
+    nextHeight *= scale;
+  }
+
+  let roundedWidth = roundToStep(nextWidth);
+  let roundedHeight = roundToStep(nextHeight);
+  pixels = roundedWidth * roundedHeight;
+
+  while (pixels > maxPixels && (roundedWidth > SIZE_STEP || roundedHeight > SIZE_STEP)) {
+    if (roundedWidth >= roundedHeight && roundedWidth > SIZE_STEP) {
+      roundedWidth -= SIZE_STEP;
+    } else if (roundedHeight > SIZE_STEP) {
+      roundedHeight -= SIZE_STEP;
+    } else {
+      break;
+    }
+    pixels = roundedWidth * roundedHeight;
+  }
+
+  while (pixels < minPixels) {
+    if (roundedWidth <= roundedHeight) {
+      roundedWidth += SIZE_STEP;
+    } else {
+      roundedHeight += SIZE_STEP;
+    }
+    pixels = roundedWidth * roundedHeight;
+  }
+
+  return { width: roundedWidth, height: roundedHeight };
+}
+
+export function getSizeFromAspectRatio(ar: string | null, quality: CliArgs["quality"]): string {
+  const normalizedQuality = normalizeQuality(quality);
+  const sizes = normalizedQuality === "2k" ? LEGACY_STANDARD_SIZES_2K : LEGACY_STANDARD_SIZES;
+  const defaultSize = normalizedQuality === "2k" ? "1536*1536" : "1024*1024";
+
+  if (!ar) return defaultSize;
+
+  const parsed = parseAspectRatio(ar);
+  if (!parsed) return defaultSize;
+
+  const targetRatio = parsed.width / parsed.height;
+  let best = defaultSize;
+  let bestDiff = Infinity;
+
+  for (const [width, height] of sizes) {
+    const diff = Math.abs(width / height - targetRatio);
+    if (diff < bestDiff) {
+      bestDiff = diff;
+      best = formatSize(width, height);
+    }
+  }
+
+  return best;
+}
+
+export function getQwen2SizeFromAspectRatio(ar: string | null, quality: CliArgs["quality"]): string {
+  const normalizedQuality = normalizeQuality(quality);
+
+  if (!ar) {
+    return QWEN_2_RECOMMENDED["1:1"][normalizedQuality];
+  }
+
+  const recommendedRatio = findKnownRatioKey(ar, Object.keys(QWEN_2_RECOMMENDED));
+  if (recommendedRatio) {
+    return QWEN_2_RECOMMENDED[recommendedRatio][normalizedQuality];
+  }
+
+  const parsed = parseAspectRatio(ar);
+  if (!parsed) {
+    return QWEN_2_RECOMMENDED["1:1"][normalizedQuality];
+  }
+
+  const targetRatio = parsed.width / parsed.height;
+  const targetPixels = QWEN_2_TARGET_PIXELS[normalizedQuality];
+  const rawWidth = Math.sqrt(targetPixels * targetRatio);
+  const rawHeight = Math.sqrt(targetPixels / targetRatio);
+  const fitted = fitToPixelBudget(
+    rawWidth,
+    rawHeight,
+    MIN_QWEN_2_TOTAL_PIXELS,
+    MAX_QWEN_2_TOTAL_PIXELS,
+  );
+
+  return formatSize(fitted.width, fitted.height);
+}
+
+function getQwenFixedSizeFromAspectRatio(ar: string | null, quality: CliArgs["quality"]): string {
+  if (quality === "normal") {
+    console.warn(
+      "DashScope qwen-image-max/plus/image models use fixed output sizes; --quality normal does not change the generated resolution."
+    );
+  }
+
+  if (!ar) return QWEN_FIXED_SPEC.defaultSize;
+
+  const ratioKey = findKnownRatioKey(ar, Object.keys(QWEN_FIXED_SIZES_BY_RATIO));
+  if (!ratioKey) {
+    throw new Error(
+      `DashScope model supports only fixed ratios ${Object.keys(QWEN_FIXED_SIZES_BY_RATIO).join(", ")}. ` +
+      `For custom ratios like "${ar}", use --model qwen-image-2.0-pro.`
+    );
+  }
+
+  return QWEN_FIXED_SIZES_BY_RATIO[ratioKey]!;
+}
+
+function validateSizeFormat(size: string): { width: number; height: number } {
+  const parsed = parseSize(size);
+  if (!parsed) {
+    throw new Error(`Invalid DashScope size "${size}". Expected <width>x<height> or <width>*<height>.`);
+  }
+  return parsed;
+}
+
+function validateQwen2Size(size: string): string {
+  const normalized = normalizeSize(size);
+  const parsed = validateSizeFormat(normalized);
+  const totalPixels = parsed.width * parsed.height;
+  if (totalPixels < MIN_QWEN_2_TOTAL_PIXELS || totalPixels > MAX_QWEN_2_TOTAL_PIXELS) {
+    throw new Error(
+      `DashScope qwen-image-2.0* models require total pixels between ${MIN_QWEN_2_TOTAL_PIXELS} ` +
+      `and ${MAX_QWEN_2_TOTAL_PIXELS}. Received ${normalized} (${totalPixels} pixels).`
+    );
+  }
+  return normalized;
+}
+
+function validateQwenFixedSize(size: string): string {
+  const normalized = normalizeSize(size);
+  validateSizeFormat(normalized);
+  if (!QWEN_FIXED_SIZES.includes(normalized)) {
+    throw new Error(
+      `DashScope qwen-image-max/plus/image models support only these sizes: ${QWEN_FIXED_SIZES.join(", ")}. ` +
+      `Received ${normalized}.`
+    );
+  }
+  return normalized;
+}
+
+export function resolveSizeForModel(
+  model: string,
+  args: Pick<CliArgs, "size" | "aspectRatio" | "quality">,
+): string {
+  const spec = getModelSpec(model);
+
+  if (args.size) {
+    if (spec.family === "qwen2") return validateQwen2Size(args.size);
+    if (spec.family === "qwenFixed") return validateQwenFixedSize(args.size);
+    validateSizeFormat(args.size);
+    return normalizeSize(args.size);
+  }
+
+  if (spec.family === "qwen2") {
+    return getQwen2SizeFromAspectRatio(args.aspectRatio, args.quality);
+  }
+
+  if (spec.family === "qwenFixed") {
+    return getQwenFixedSizeFromAspectRatio(args.aspectRatio, args.quality);
+  }
+
+  return getSizeFromAspectRatio(args.aspectRatio, args.quality);
+}
+
+function buildParameters(
+  family: DashScopeModelFamily,
+  size: string,
+): Record<string, unknown> {
+  const parameters: Record<string, unknown> = {
+    prompt_extend: false,
+    size,
+  };
+
+  if (family === "qwen2" || family === "qwenFixed") {
+    parameters.watermark = false;
+    parameters.negative_prompt = QWEN_NEGATIVE_PROMPT;
+  }
+
+  return parameters;
+}
+
+type DashScopeResponse = {
+  output?: {
+    result_image?: string;
+    choices?: Array<{
+      message?: {
+        content?: Array<{ image?: string }>;
+      };
+    }>;
+  };
+};
+
+async function extractImageFromResponse(result: DashScopeResponse): Promise<Uint8Array> {
+  let imageData: string | null = null;
+
+  if (result.output?.result_image) {
+    imageData = result.output.result_image;
+  } else if (result.output?.choices?.[0]?.message?.content) {
+    const content = result.output.choices[0].message.content;
+    for (const item of content) {
+      if (item.image) {
+        imageData = item.image;
+        break;
+      }
+    }
+  }
+
+  if (!imageData) {
+    console.error("Response:", JSON.stringify(result, null, 2));
+    throw new Error("No image in response");
+  }
+
+  if (imageData.startsWith("http://") || imageData.startsWith("https://")) {
+    const imgRes = await fetch(imageData);
+    if (!imgRes.ok) throw new Error("Failed to download image");
+    const buf = await imgRes.arrayBuffer();
+    return new Uint8Array(buf);
+  }
+
+  return Uint8Array.from(Buffer.from(imageData, "base64"));
+}
+
+export async function generateImage(
+  prompt: string,
+  model: string,
+  args: CliArgs
+): Promise<Uint8Array> {
+  const apiKey = getApiKey();
+  if (!apiKey) throw new Error("DASHSCOPE_API_KEY is required");
+
+  if (args.referenceImages.length > 0) {
+    throw new Error(
+      "Reference images are not supported with DashScope provider in baoyu-imagine. Use --provider google with a Gemini multimodal model."
+    );
+  }
+
+  const spec = getModelSpec(model);
+  const size = resolveSizeForModel(model, args);
+  const url = `${getBaseUrl()}/api/v1/services/aigc/multimodal-generation/generation`;
+
+  const body = {
+    model,
+    input: {
+      messages: [
+        {
+          role: "user",
+          content: [{ text: prompt }],
+        },
+      ],
+    },
+    parameters: buildParameters(spec.family, size),
+  };
+
+  console.log(`Generating image with DashScope (${model})...`, { family: spec.family, size });
+
+  const res = await fetch(url, {
+    method: "POST",
+    headers: {
+      "Content-Type": "application/json",
+      Authorization: `Bearer ${apiKey}`,
+    },
+    body: JSON.stringify(body),
+  });
+
+  if (!res.ok) {
+    const err = await res.text();
+    throw new Error(`DashScope API error (${res.status}): ${err}`);
+  }
+
+  const result = await res.json() as DashScopeResponse;
+  return extractImageFromResponse(result);
+}
@@ -0,0 +1,126 @@
+import assert from "node:assert/strict";
+import test, { type TestContext } from "node:test";
+
+import type { CliArgs } from "../types.ts";
+import {
+  addAspectRatioToPrompt,
+  buildGoogleUrl,
+  buildPromptWithAspect,
+  extractInlineImageData,
+  extractPredictedImageData,
+  getGoogleImageSize,
+  isGoogleImagen,
+  isGoogleMultimodal,
+  normalizeGoogleModelId,
+} from "./google.ts";
+
+function useEnv(
+  t: TestContext,
+  values: Record<string, string | null>,
+): void {
+  const previous = new Map<string, string | undefined>();
+  for (const [key, value] of Object.entries(values)) {
+    previous.set(key, process.env[key]);
+    if (value == null) {
+      delete process.env[key];
+    } else {
+      process.env[key] = value;
+    }
+  }
+
+  t.after(() => {
+    for (const [key, value] of previous.entries()) {
+      if (value == null) {
+        delete process.env[key];
+      } else {
+        process.env[key] = value;
+      }
+    }
+  });
+}
+
+function makeArgs(overrides: Partial<CliArgs> = {}): CliArgs {
+  return {
+    prompt: null,
+    promptFiles: [],
+    imagePath: null,
+    provider: null,
+    model: null,
+    aspectRatio: null,
+    size: null,
+    quality: null,
+    imageSize: null,
+    referenceImages: [],
+    n: 1,
+    batchFile: null,
+    jobs: null,
+    json: false,
+    help: false,
+    ...overrides,
+  };
+}
+
+test("Google provider helpers normalize model IDs and select image size defaults", () => {
+  assert.equal(
+    normalizeGoogleModelId("models/gemini-3.1-flash-image-preview"),
+    "gemini-3.1-flash-image-preview",
+  );
+  assert.equal(isGoogleMultimodal("models/gemini-3-pro-image-preview"), true);
+  assert.equal(isGoogleImagen("imagen-3.0-generate-002"), true);
+  assert.equal(getGoogleImageSize(makeArgs({ imageSize: null, quality: "2k" })), "2K");
+  assert.equal(getGoogleImageSize(makeArgs({ imageSize: "4K", quality: "normal" })), "4K");
+});
+
+test("Google URL builder appends v1beta when the base URL does not already include it", (t) => {
+  useEnv(t, { GOOGLE_BASE_URL: "https://generativelanguage.googleapis.com" });
+  assert.equal(
+    buildGoogleUrl("models/demo:generateContent"),
+    "https://generativelanguage.googleapis.com/v1beta/models/demo:generateContent",
+  );
+});
+
+test("Google URL and prompt helpers preserve existing v1beta paths and aspect hints", (t) => {
+  useEnv(t, { GOOGLE_BASE_URL: "https://example.com/custom/v1beta/" });
+  assert.equal(
+    buildGoogleUrl("/models/demo:predict"),
+    "https://example.com/custom/v1beta/models/demo:predict",
+  );
+
+  assert.equal(
+    addAspectRatioToPrompt("A city skyline", "16:9"),
+    "A city skyline Aspect ratio: 16:9.",
+  );
+  assert.equal(
+    buildPromptWithAspect("A city skyline", "16:9", "2k"),
+    "A city skyline Aspect ratio: 16:9. High resolution 2048px.",
+  );
+});
+
+test("Google response extractors find inline and predicted image payloads", () => {
+  assert.equal(
+    extractInlineImageData({
+      candidates: [
+        {
+          content: {
+            parts: [{ inlineData: { data: "inline-base64" } }],
+          },
+        },
+      ],
+    }),
+    "inline-base64",
+  );
+
+  assert.equal(
+    extractPredictedImageData({
+      predictions: [{ image: { imageBytes: "predicted-base64" } }],
+    }),
+    "predicted-base64",
+  );
+
+  assert.equal(
+    extractPredictedImageData({
+      generatedImages: [{ bytesBase64Encoded: "generated-base64" }],
+    }),
+    "generated-base64",
+  );
+});
@@ -0,0 +1,349 @@
+import path from "node:path";
+import { readFile } from "node:fs/promises";
+import { execFileSync } from "node:child_process";
+import type { CliArgs } from "../types";
+
+const GOOGLE_MULTIMODAL_MODELS = [
+  "gemini-3-pro-image-preview",
+  "gemini-3-flash-preview",
+  "gemini-3.1-flash-image-preview",
+];
+const GOOGLE_IMAGEN_MODELS = [
+  "imagen-3.0-generate-002",
+  "imagen-3.0-generate-001",
+];
+
+export function getDefaultModel(): string {
+  return process.env.GOOGLE_IMAGE_MODEL || "gemini-3-pro-image-preview";
+}
+
+export function normalizeGoogleModelId(model: string): string {
+  return model.startsWith("models/") ? model.slice("models/".length) : model;
+}
+
+export function isGoogleMultimodal(model: string): boolean {
+  const normalized = normalizeGoogleModelId(model);
+  return GOOGLE_MULTIMODAL_MODELS.some((m) => normalized.includes(m));
+}
+
+export function isGoogleImagen(model: string): boolean {
+  const normalized = normalizeGoogleModelId(model);
+  return GOOGLE_IMAGEN_MODELS.some((m) => normalized.includes(m));
+}
+
+function getGoogleApiKey(): string | null {
+  return process.env.GOOGLE_API_KEY || process.env.GEMINI_API_KEY || null;
+}
+
+export function getGoogleImageSize(args: CliArgs): "1K" | "2K" | "4K" {
+  if (args.imageSize) return args.imageSize as "1K" | "2K" | "4K";
+  return args.quality === "2k" ? "2K" : "1K";
+}
+
+function getGoogleBaseUrl(): string {
+  const base =
+    process.env.GOOGLE_BASE_URL || "https://generativelanguage.googleapis.com";
+  return base.replace(/\/+$/g, "");
+}
+
+export function buildGoogleUrl(pathname: string): string {
+  const base = getGoogleBaseUrl();
+  const cleanedPath = pathname.replace(/^\/+/g, "");
+  if (base.endsWith("/v1beta")) return `${base}/${cleanedPath}`;
+  return `${base}/v1beta/${cleanedPath}`;
+}
+
+function toModelPath(model: string): string {
+  const modelId = normalizeGoogleModelId(model);
+  return `models/${modelId}`;
+}
+
+function getHttpProxy(): string | null {
+  return (
+    process.env.https_proxy ||
+    process.env.HTTPS_PROXY ||
+    process.env.http_proxy ||
+    process.env.HTTP_PROXY ||
+    process.env.ALL_PROXY ||
+    null
+  );
+}
+
+async function postGoogleJsonViaCurl<T>(
+  url: string,
+  apiKey: string,
+  body: unknown,
+): Promise<T> {
+  const proxy = getHttpProxy();
+  const bodyStr = JSON.stringify(body);
+  const args = [
+    "-s",
+    "--connect-timeout",
+    "30",
+    "--max-time",
+    "300",
+    ...(proxy ? ["-x", proxy] : []),
+    url,
+    "-H",
+    "Content-Type: application/json",
+    "-H",
+    `x-goog-api-key: ${apiKey}`,
+    "-d",
+    "@-",
+  ];
+
+  let result = "";
+  try {
+    result = execFileSync("curl", args, {
+      input: bodyStr,
+      encoding: "utf8",
+      maxBuffer: 100 * 1024 * 1024,
+      timeout: 310000,
+    });
+  } catch (error) {
+    const e = error as { message?: string; stderr?: string | Buffer };
+    const stderrText =
+      typeof e.stderr === "string"
+        ? e.stderr
+        : e.stderr
+          ? e.stderr.toString("utf8")
+          : "";
+    const details = stderrText.trim() || e.message || "curl request failed";
+    throw new Error(`Google API request failed via curl: ${details}`);
+  }
+
+  const parsed = JSON.parse(result) as any;
+  if (parsed.error) {
+    throw new Error(
+      `Google API error (${parsed.error.code}): ${parsed.error.message}`,
+    );
+  }
+  return parsed as T;
+}
+
+async function postGoogleJsonViaFetch<T>(
+  url: string,
+  apiKey: string,
+  body: unknown,
+): Promise<T> {
+  const res = await fetch(url, {
+    method: "POST",
+    headers: {
+      "Content-Type": "application/json",
+      "x-goog-api-key": apiKey,
+    },
+    body: JSON.stringify(body),
+  });
+
+  if (!res.ok) {
+    const err = await res.text();
+    throw new Error(`Google API error (${res.status}): ${err}`);
+  }
+
+  return (await res.json()) as T;
+}
+
+async function postGoogleJson<T>(pathname: string, body: unknown): Promise<T> {
+  const apiKey = getGoogleApiKey();
+  if (!apiKey) throw new Error("GOOGLE_API_KEY or GEMINI_API_KEY is required");
+
+  const url = buildGoogleUrl(pathname);
+  const proxy = getHttpProxy();
+
+  // When an HTTP proxy is detected, use curl instead of fetch.
+  // Bun's fetch has a known issue where long-lived connections through
+  // HTTP proxies get their sockets closed unexpectedly, causing image
+  // generation requests to fail with "socket connection was closed
+  // unexpectedly". Using curl as the HTTP client works around this.
+  if (proxy) {
+    return postGoogleJsonViaCurl<T>(url, apiKey, body);
+  }
+
+  return postGoogleJsonViaFetch<T>(url, apiKey, body);
+}
+
+export function buildPromptWithAspect(
+  prompt: string,
+  ar: string | null,
+  quality: CliArgs["quality"],
+): string {
+  let result = prompt;
+  if (ar) {
+    result += ` Aspect ratio: ${ar}.`;
+  }
+  if (quality === "2k") {
+    result += " High resolution 2048px.";
+  }
+  return result;
+}
+
+export function addAspectRatioToPrompt(prompt: string, ar: string | null): string {
+  if (!ar) return prompt;
+  return `${prompt} Aspect ratio: ${ar}.`;
+}
+
+async function readImageAsBase64(
+  p: string,
+): Promise<{ data: string; mimeType: string }> {
+  const buf = await readFile(p);
+  const ext = path.extname(p).toLowerCase();
+  let mimeType = "image/png";
+  if (ext === ".jpg" || ext === ".jpeg") mimeType = "image/jpeg";
+  else if (ext === ".gif") mimeType = "image/gif";
+  else if (ext === ".webp") mimeType = "image/webp";
+  return { data: buf.toString("base64"), mimeType };
+}
+
+export function extractInlineImageData(response: {
+  candidates?: Array<{
+    content?: { parts?: Array<{ inlineData?: { data?: string } }> };
+  }>;
+}): string | null {
+  for (const candidate of response.candidates || []) {
+    for (const part of candidate.content?.parts || []) {
+      const data = part.inlineData?.data;
+      if (typeof data === "string" && data.length > 0) return data;
+    }
+  }
+  return null;
+}
+
+export function extractPredictedImageData(response: {
+  predictions?: Array<any>;
+  generatedImages?: Array<any>;
+}): string | null {
+  const candidates = [
+    ...(response.predictions || []),
+    ...(response.generatedImages || []),
+  ];
+  for (const candidate of candidates) {
+    if (!candidate || typeof candidate !== "object") continue;
+    if (typeof candidate.imageBytes === "string") return candidate.imageBytes;
+    if (typeof candidate.bytesBase64Encoded === "string")
+      return candidate.bytesBase64Encoded;
+    if (typeof candidate.data === "string") return candidate.data;
+    const image = candidate.image;
+    if (image && typeof image === "object") {
+      if (typeof image.imageBytes === "string") return image.imageBytes;
+      if (typeof image.bytesBase64Encoded === "string")
+        return image.bytesBase64Encoded;
+      if (typeof image.data === "string") return image.data;
+    }
+  }
+  return null;
+}
+
+async function generateWithGemini(
+  prompt: string,
+  model: string,
+  args: CliArgs,
+): Promise<Uint8Array> {
+  const promptWithAspect = addAspectRatioToPrompt(prompt, args.aspectRatio);
+  const parts: Array<{
+    text?: string;
+    inlineData?: { data: string; mimeType: string };
+  }> = [];
+  for (const refPath of args.referenceImages) {
+    const { data, mimeType } = await readImageAsBase64(refPath);
+    parts.push({ inlineData: { data, mimeType } });
+  }
+  parts.push({ text: promptWithAspect });
+
+  const imageConfig: { imageSize: "1K" | "2K" | "4K" } = {
+    imageSize: getGoogleImageSize(args),
+  };
+
+  console.log("Generating image with Gemini...", imageConfig);
+  const response = await postGoogleJson<{
+    candidates?: Array<{
+      content?: { parts?: Array<{ inlineData?: { data?: string } }> };
+    }>;
+  }>(`${toModelPath(model)}:generateContent`, {
+    contents: [
+      {
+        role: "user",
+        parts,
+      },
+    ],
+    generationConfig: {
+      responseModalities: ["IMAGE"],
+      imageConfig,
+    },
+  });
+  console.log("Generation completed.");
+
+  const imageData = extractInlineImageData(response);
+  if (imageData) return Uint8Array.from(Buffer.from(imageData, "base64"));
+
+  throw new Error("No image in response");
+}
+
+async function generateWithImagen(
+  prompt: string,
+  model: string,
+  args: CliArgs,
+): Promise<Uint8Array> {
+  const fullPrompt = buildPromptWithAspect(
+    prompt,
+    args.aspectRatio,
+    args.quality,
+  );
+  const imageSize = getGoogleImageSize(args);
+  if (imageSize === "4K") {
+    console.error(
+      "Warning: Imagen models do not support 4K imageSize, using 2K instead.",
+    );
+  }
+
+  const parameters: Record<string, unknown> = {
+    sampleCount: args.n,
+  };
+  if (args.aspectRatio) {
+    parameters.aspectRatio = args.aspectRatio;
+  }
+  if (imageSize === "1K" || imageSize === "2K") {
+    parameters.imageSize = imageSize;
+  } else {
+    parameters.imageSize = "2K";
+  }
+
+  const response = await postGoogleJson<{
+    predictions?: Array<any>;
+    generatedImages?: Array<any>;
+  }>(`${toModelPath(model)}:predict`, {
+    instances: [
+      {
+        prompt: fullPrompt,
+      },
+    ],
+    parameters,
+  });
+
+  const imageData = extractPredictedImageData(response);
+  if (imageData) return Uint8Array.from(Buffer.from(imageData, "base64"));
+
+  throw new Error("No image in response");
+}
+
+export async function generateImage(
+  prompt: string,
+  model: string,
+  args: CliArgs,
+): Promise<Uint8Array> {
+  if (isGoogleImagen(model)) {
+    if (args.referenceImages.length > 0) {
+      throw new Error(
+        "Reference images are not supported with Imagen models. Use gemini-3-pro-image-preview, gemini-3-flash-preview, or gemini-3.1-flash-image-preview.",
+      );
+    }
+    return generateWithImagen(prompt, model, args);
+  }
+
+  if (!isGoogleMultimodal(model) && args.referenceImages.length > 0) {
+    throw new Error(
+      "Reference images are only supported with Gemini multimodal models. Use gemini-3-pro-image-preview, gemini-3-flash-preview, or gemini-3.1-flash-image-preview.",
+    );
+  }
+
+  return generateWithGemini(prompt, model, args);
+}
@@ -0,0 +1,114 @@
+import assert from "node:assert/strict";
+import test, { type TestContext } from "node:test";
+
+import type { CliArgs } from "../types.ts";
+import { generateImage } from "./jimeng.ts";
+
+function makeArgs(overrides: Partial<CliArgs> = {}): CliArgs {
+  return {
+    prompt: null,
+    promptFiles: [],
+    imagePath: null,
+    provider: null,
+    model: null,
+    aspectRatio: null,
+    size: null,
+    quality: null,
+    imageSize: null,
+    referenceImages: [],
+    n: 1,
+    batchFile: null,
+    jobs: null,
+    json: false,
+    help: false,
+    ...overrides,
+  };
+}
+
+function useEnv(
+  t: TestContext,
+  values: Record<string, string | null>,
+): void {
+  const previous = new Map<string, string | undefined>();
+  for (const [key, value] of Object.entries(values)) {
+    previous.set(key, process.env[key]);
+    if (value == null) {
+      delete process.env[key];
+    } else {
+      process.env[key] = value;
+    }
+  }
+
+  t.after(() => {
+    for (const [key, value] of previous.entries()) {
+      if (value == null) {
+        delete process.env[key];
+      } else {
+        process.env[key] = value;
+      }
+    }
+  });
+}
+
+test("Jimeng submit request uses prompt field expected by current API", async (t) => {
+  useEnv(t, {
+    JIMENG_ACCESS_KEY_ID: "test-access-key",
+    JIMENG_SECRET_ACCESS_KEY: "test-secret-key",
+    JIMENG_BASE_URL: null,
+    JIMENG_REGION: null,
+  });
+
+  const originalFetch = globalThis.fetch;
+  t.after(() => {
+    globalThis.fetch = originalFetch;
+  });
+
+  const calls: Array<{
+    input: string;
+    init?: RequestInit;
+  }> = [];
+
+  globalThis.fetch = async (input, init) => {
+    calls.push({
+      input: String(input),
+      init,
+    });
+
+    if (calls.length === 1) {
+      return Response.json({
+        code: 10000,
+        data: {
+          task_id: "task-123",
+        },
+      });
+    }
+
+    return Response.json({
+      code: 10000,
+      data: {
+        status: "done",
+        binary_data_base64: [Buffer.from("jimeng-image").toString("base64")],
+      },
+    });
+  };
+
+  const image = await generateImage(
+    "A quiet bamboo forest",
+    "jimeng_t2i_v40",
+    makeArgs({ quality: "normal" }),
+  );
+
+  assert.equal(Buffer.from(image).toString("utf8"), "jimeng-image");
+  assert.equal(calls.length, 2);
+  assert.equal(
+    calls[0]?.input,
+    "https://visual.volcengineapi.com/?Action=CVSync2AsyncSubmitTask&Version=2022-08-31",
+  );
+
+  const submitBody = JSON.parse(String(calls[0]?.init?.body)) as Record<string, unknown>;
+  assert.equal(submitBody.req_key, "jimeng_t2i_v40");
+  assert.equal(submitBody.prompt, "A quiet bamboo forest");
+  assert.ok(!("prompt_text" in submitBody));
+  assert.equal(submitBody.width, 1024);
+  assert.equal(submitBody.height, 1024);
+});
@@ -0,0 +1,467 @@
+import type { CliArgs } from "../types";
+import * as crypto from "node:crypto";
+
+type JimengSizePreset = "normal" | "2k" | "4k";
+
+export function getDefaultModel(): string {
+  return process.env.JIMENG_IMAGE_MODEL || "jimeng_t2i_v40";
+}
+
+function getAccessKey(): string | null {
+  return process.env.JIMENG_ACCESS_KEY_ID || null;
+}
+
+function getSecretKey(): string | null {
+  return process.env.JIMENG_SECRET_ACCESS_KEY || null;
+}
+
+function getRegion(): string {
+  return process.env.JIMENG_REGION || "cn-north-1";
+}
+
+function getBaseUrl(): string {
+  return process.env.JIMENG_BASE_URL || "https://visual.volcengineapi.com";
+}
+
+function resolveEndpoint(query: Record<string, string>): {
+  url: string;
+  host: string;
+  canonicalUri: string;
+} {
+  let baseUrl: URL;
+  try {
+    baseUrl = new URL(getBaseUrl());
+  } catch {
+    throw new Error(`Invalid JIMENG_BASE_URL: ${getBaseUrl()}`);
+  }
+
+  baseUrl.search = "";
+  for (const [key, value] of Object.entries(query).sort(([a], [b]) => a.localeCompare(b))) {
+    baseUrl.searchParams.set(key, value);
+  }
+
+  return {
+    url: baseUrl.toString(),
+    host: baseUrl.host,
+    canonicalUri: baseUrl.pathname || "/",
+  };
+}
+
+/**
+ * Volcengine HMAC-SHA256 signature generation
+ * Following the official documentation at:
+ * https://www.volcengine.com/docs/85621/1817045
+ */
+function generateSignature(
+  method: string,
+  query: Record<string, string>,
+  headers: Record<string, string>,
+  body: string,
+  accessKey: string,
+  secretKey: string,
+  region: string,
+  service: string,
+  canonicalUri: string
+): string {
+  // 1. Create canonical request
+  // Sort query parameters alphabetically
+  const sortedQuery = Object.entries(query)
+    .sort(([a], [b]) => a.localeCompare(b))
+    .map(([k, v]) => `${encodeURIComponent(k)}=${encodeURIComponent(v)}`)
+    .join("&");
+
+  // Sort headers alphabetically and create canonical headers
+  const sortedHeaders = Object.entries(headers)
+    .sort(([a], [b]) => a.localeCompare(b))
+    .map(([k, v]) => `${k.toLowerCase()}:${v.trim()}\n`)
+    .join("");
+
+  const signedHeaders = Object.keys(headers)
+    .sort()
+    .map(k => k.toLowerCase())
+    .join(";");
+
+  const hashedPayload = crypto.createHash("sha256").update(body, "utf8").digest("hex");
+
+  const canonicalRequest = [
+    method,
+    canonicalUri,
+    sortedQuery,
+    sortedHeaders,
+    signedHeaders,
+    hashedPayload,
+  ].join("\n");
+
+  const hashedCanonicalRequest = crypto
+    .createHash("sha256")
+    .update(canonicalRequest, "utf8")
+    .digest("hex");
+
+  // 2. Create string to sign
+  const algorithm = "HMAC-SHA256";
+  const timestamp = headers["X-Date"] || headers["x-date"];
+  if (!timestamp) {
+    throw new Error("Jimeng signature generation requires an X-Date header.");
+  }
+  const dateStamp = timestamp.slice(0, 8);
+
+  const credentialScope = `${dateStamp}/${region}/${service}/request`;
+
+  const stringToSign = [
+    algorithm,
+    timestamp,
+    credentialScope,
+    hashedCanonicalRequest,
+  ].join("\n");
+
+  // 3. Calculate signature
+  const kDate = crypto
+    .createHmac("sha256", secretKey)
+    .update(dateStamp)
+    .digest();
+
+  const kRegion = crypto.createHmac("sha256", kDate).update(region).digest();
+  const kService = crypto.createHmac("sha256", kRegion).update(service).digest();
+  const kSigning = crypto.createHmac("sha256", kService).update("request").digest();
+
+  const signature = crypto
+    .createHmac("sha256", kSigning)
+    .update(stringToSign)
+    .digest("hex");
+
+  // 4. Create authorization header
+  return `${algorithm} Credential=${accessKey}/${credentialScope}, SignedHeaders=${signedHeaders}, Signature=${signature}`;
+}
+
+/**
+ * Parse aspect ratio string like "16:9", "1:1", "4:3" into width and height
+ */
+function parseAspectRatio(ar: string): { width: number; height: number } | null {
+  const match = ar.match(/^(\d+(?:\.\d+)?):(\d+(?:\.\d+)?)$/);
+  if (!match) return null;
+  const w = parseFloat(match[1]!);
+  const h = parseFloat(match[2]!);
+  if (w <= 0 || h <= 0) return null;
+  return { width: w, height: h };
+}
+
+/**
+ * Supported size presets for different quality levels
+ * Based on Volcengine Jimeng documentation
+ */
+const SIZE_PRESETS: Record<string, Record<string, string>> = {
+  normal: {
+    "1:1": "1024x1024",
+    "4:3": "1360x1020",
+    "16:9": "1536x864",
+    "3:2": "1440x960",
+    "21:9": "1920x824",
+  },
+  "2k": {
+    "1:1": "2048x2048",
+    "4:3": "2304x1728",
+    "16:9": "2560x1440",
+    "3:2": "2496x1664",
+    "21:9": "3024x1296",
+  },
+  "4k": {
+    "1:1": "4096x4096",
+    "4:3": "4694x3520",
+    "16:9": "5404x3040",
+    "3:2": "4992x3328",
+    "21:9": "6198x2656",
+  },
+};
+
+function normalizeDimensions(value: string): string | null {
+  const match = value.trim().match(/^(\d+)\s*[xX*]\s*(\d+)$/);
+  if (!match) return null;
+  return `${match[1]}x${match[2]}`;
+}
+
+function getClosestPresetSize(ar: string | null, qualityLevel: JimengSizePreset): string {
+  const presets = SIZE_PRESETS[qualityLevel];
+  const defaultSize = presets["1:1"]!;
+
+  if (!ar) return defaultSize;
+
+  const parsed = parseAspectRatio(ar);
+  if (!parsed) return defaultSize;
+
+  const targetRatio = parsed.width / parsed.height;
+  let bestMatch = defaultSize;
+  let bestDiff = Infinity;
+
+  for (const [ratio, size] of Object.entries(presets)) {
+    const [w, h] = ratio.split(":").map(Number);
+    const presetRatio = w / h;
+    const diff = Math.abs(presetRatio - targetRatio);
+    if (diff < bestDiff) {
+      bestDiff = diff;
+      bestMatch = size;
+    }
+  }
+
+  return bestMatch;
+}
+
+function normalizeImageSizePreset(imageSize: string, ar: string | null): string | null {
+  const preset = imageSize.trim().toUpperCase();
+  if (preset === "1K") return getClosestPresetSize(ar, "normal");
+  if (preset === "2K") return getClosestPresetSize(ar, "2k");
+  if (preset === "4K") return getClosestPresetSize(ar, "4k");
+  return normalizeDimensions(imageSize);
+}
+
+function getImageSize(ar: string | null, quality: CliArgs["quality"], imageSize?: string | null): string {
+  if (imageSize) {
+    const normalizedSize = normalizeImageSizePreset(imageSize, ar);
+    if (normalizedSize) return normalizedSize;
+  }
+
+  // Default to 2K quality if not specified
+  const qualityLevel: JimengSizePreset = quality === "normal" ? "normal" : "2k";
+  return getClosestPresetSize(ar, qualityLevel);
+}
+
+/**
+ * Step 1: Submit async task to Volcengine Jimeng API
+ */
+async function submitTask(
+  prompt: string,
+  model: string,
+  size: string,
+  accessKey: string,
+  secretKey: string,
+  region: string
+): Promise<string> {
+  // Query parameters for submit endpoint
+  const query = {
+    Action: "CVSync2AsyncSubmitTask",
+    Version: "2022-08-31",
+  };
+  const endpoint = resolveEndpoint(query);
+
+  // Request body - Jimeng API expects width/height as separate integers
+  const [width, height] = size.split("x").map(Number);
+  const bodyObj = {
+    req_key: model,
+    prompt,
+    // Use separate width and height parameters instead of size string
+    width: width,
+    height: height,
+    // Optional: seed for reproducibility
+    // seed: Math.floor(Math.random() * 999999),
+  };
+
+  const body = JSON.stringify(bodyObj);
+
+  // Headers
+  const timestampHeader = new Date().toISOString().replace(/[:\-]|\.\d{3}/g, "");
+  const headers = {
+    "Content-Type": "application/json",
+    "X-Date": timestampHeader,
+    "Host": endpoint.host,
+  };
+
+  // Generate signature
+  const authorization = generateSignature(
+    "POST",
+    query,
+    headers,
+    body,
+    accessKey,
+    secretKey,
+    region,
+    "cv",
+    endpoint.canonicalUri
+  );
+
+  console.error(`Submitting task to Jimeng (${model})...`, { width, height });
+
+  const res = await fetch(endpoint.url, {
+    method: "POST",
+    headers: {
+      ...headers,
+      "Authorization": authorization,
+    },
+    body,
+  });
+
+  if (!res.ok) {
+    const err = await res.text();
+    throw new Error(`Jimeng API submit error (${res.status}): ${err}`);
+  }
+
+  const result = (await res.json()) as {
+    code?: number;
+    message?: string;
+    data?: {
+      task_id?: string;
+    };
+  };
+
+  // Volcengine API returns code 10000 for success
+  if (result.code !== 10000 || !result.data?.task_id) {
+    console.error("Submit response:", JSON.stringify(result, null, 2));
+    throw new Error(`Failed to submit task: ${result.message || "Unknown error"}`);
+  }
+
+  return result.data.task_id;
+}
+
+/**
+ * Step 2: Poll for task result
+ * Returns image data directly as Uint8Array
+ */
+async function pollForResult(
+  taskId: string,
+  model: string,
+  accessKey: string,
+  secretKey: string,
+  region: string
+): Promise<Uint8Array> {
+  const maxAttempts = 60;
+  const pollIntervalMs = 2000;
+
+  for (let attempt = 0; attempt < maxAttempts; attempt++) {
+    // Query parameters for result endpoint
+    const query = {
+      Action: "CVSync2AsyncGetResult",
+      Version: "2022-08-31",
+    };
+    const endpoint = resolveEndpoint(query);
+
+    // Request body - include req_key and task_id
+    const bodyObj = {
+      req_key: model,
+      task_id: taskId,
+    };
+
+    const body = JSON.stringify(bodyObj);
+
+    // Headers
+    const timestampHeader = new Date().toISOString().replace(/[:\-]|\.\d{3}/g, "");
+    const headers = {
+      "Content-Type": "application/json",
+      "X-Date": timestampHeader,
+      "Host": endpoint.host,
+    };
+
+    // Generate signature
+    const authorization = generateSignature(
+      "POST",
+      query,
+      headers,
+      body,
+      accessKey,
+      secretKey,
+      region,
+      "cv",
+      endpoint.canonicalUri
+    );
+
+    const res = await fetch(endpoint.url, {
+      method: "POST",
+      headers: {
+        ...headers,
+        "Authorization": authorization,
+      },
+      body,
+    });
+
+    if (!res.ok) {
+      const err = await res.text();
+      throw new Error(`Jimeng API poll error (${res.status}): ${err}`);
+    }
+
+    const result = (await res.json()) as {
+      code?: number;
+      message?: string;
+      data?: {
+        status?: string;
+        image_urls?: string[];
+        binary_data_base64?: string[];
+      };
+    };
+
+    // Volcengine API returns code 10000 for success
+    if (result.code === 10000 && result.data) {
+      const { status, image_urls, binary_data_base64 } = result.data;
+
+      // Check for base64 image data (preferred by Jimeng)
+      if (binary_data_base64 && binary_data_base64.length > 0) {
+        console.error("Image received as base64 data");
+        const base64Data = binary_data_base64[0]!;
+        // Convert base64 to Uint8Array
+        const binaryString = Buffer.from(base64Data, "base64").toString("binary");
+        const bytes = new Uint8Array(binaryString.length);
+        for (let i = 0; i < binaryString.length; i++) {
+          bytes[i] = binaryString.charCodeAt(i);
+        }
+        return bytes;
+      }
+
+      // Fallback to URL format
+      if (status === "done" && image_urls && image_urls.length > 0) {
+        // Download from URL
+        console.error(`Downloading image from ${image_urls[0]}...`);
+        const imgRes = await fetch(image_urls[0]!);
+        if (!imgRes.ok) {
+          throw new Error(`Failed to download image from ${image_urls[0]}`);
+        }
+        const buffer = await imgRes.arrayBuffer();
+        return new Uint8Array(buffer);
+      }
+
+      if (status === "in_queue" || status === "generating") {
+        console.error(`Task status: ${status} (${attempt + 1}/${maxAttempts})`);
+        await new Promise(resolve => setTimeout(resolve, pollIntervalMs));
+        continue;
+      }
+
+      if (status === "fail") {
+        throw new Error(`Jimeng task failed: ${result.message || "Generation failed"}`);
+      }
+    }
+
+    console.error("Poll response:", JSON.stringify(result, null, 2));
+    throw new Error(`Unexpected response during polling: ${result.message || "Unknown error"}`);
+  }
+
+  throw new Error("Task timeout: image generation took too long");
+}
+
+export async function generateImage(
+  prompt: string,
+  model: string,
+  args: CliArgs
+): Promise<Uint8Array> {
+  if (args.referenceImages.length > 0) {
+    throw new Error(
+      "Jimeng does not support reference images. Use --provider google, openai, openrouter, or replicate."
+    );
+  }
+
+  const accessKey = getAccessKey();
+  const secretKey = getSecretKey();
+  const region = getRegion();
+
+  if (!accessKey || !secretKey) {
+    throw new Error(
+      "JIMENG_ACCESS_KEY_ID and JIMENG_SECRET_ACCESS_KEY are required. " +
+      "Get your credentials from https://console.volcengine.com/iam/keymanage"
+    );
+  }
+
+  const size = getImageSize(args.aspectRatio, args.quality, args.imageSize);
+
+  // Step 1: Submit task
+  const taskId = await submitTask(prompt, model, size, accessKey, secretKey, region);
+
+  // Step 2: Poll for result (returns image data directly)
+  const imageData = await pollForResult(taskId, model, accessKey, secretKey, region);
+
+  console.error("Image generation complete!");
+  return imageData;
+}
@@ -0,0 +1,171 @@
+import assert from "node:assert/strict";
+import fs from "node:fs/promises";
+import os from "node:os";
+import path from "node:path";
+import test, { type TestContext } from "node:test";
+
+import type { CliArgs } from "../types.ts";
+import {
+  buildMinimaxUrl,
+  buildRequestBody,
+  buildSubjectReference,
+  extractImageFromResponse,
+  parsePixelSize,
+  validateArgs,
+} from "./minimax.ts";
+
+function useEnv(
+  t: TestContext,
+  values: Record<string, string | null>,
+): void {
+  const previous = new Map<string, string | undefined>();
+  for (const [key, value] of Object.entries(values)) {
+    previous.set(key, process.env[key]);
+    if (value == null) {
+      delete process.env[key];
+    } else {
+      process.env[key] = value;
+    }
+  }
+
+  t.after(() => {
+    for (const [key, value] of previous.entries()) {
+      if (value == null) {
+        delete process.env[key];
+      } else {
+        process.env[key] = value;
+      }
+    }
+  });
+}
+
+function makeArgs(overrides: Partial<CliArgs> = {}): CliArgs {
+  return {
+    prompt: null,
+    promptFiles: [],
+    imagePath: null,
+    provider: null,
+    model: null,
+    aspectRatio: null,
+    size: null,
+    quality: null,
+    imageSize: null,
+    referenceImages: [],
+    n: 1,
+    batchFile: null,
+    jobs: null,
+    json: false,
+    help: false,
+    ...overrides,
+  };
+}
+
+test("MiniMax URL builder normalizes /v1 suffixes", (t) => {
+  useEnv(t, { MINIMAX_BASE_URL: "https://api.minimax.io" });
+  assert.equal(buildMinimaxUrl(), "https://api.minimax.io/v1/image_generation");
+
+  process.env.MINIMAX_BASE_URL = "https://proxy.example.com/custom/v1/";
+  assert.equal(buildMinimaxUrl(), "https://proxy.example.com/custom/v1/image_generation");
+});
+
+test("MiniMax size parsing and validation follow documented constraints", () => {
+  assert.deepEqual(parsePixelSize("1536x1024"), { width: 1536, height: 1024 });
+  assert.deepEqual(parsePixelSize("1536*1024"), { width: 1536, height: 1024 });
+  assert.equal(parsePixelSize("wide"), null);
+
+  validateArgs("image-01", makeArgs({ size: "1536x1024", n: 9 }));
+
+  assert.throws(
+    () => validateArgs("image-01-live", makeArgs({ size: "1536x1024" })),
+    /only supported with model image-01/,
+  );
+  assert.throws(
+    () => validateArgs("image-01", makeArgs({ size: "1537x1024" })),
+    /divisible by 8/,
+  );
+  assert.throws(
+    () => validateArgs("image-01", makeArgs({ aspectRatio: "2.35:1" })),
+    /aspect_ratio must be one of/,
+  );
+  assert.throws(
+    () => validateArgs("image-01", makeArgs({ n: 10 })),
+    /at most 9 images/,
+  );
+});
+
+test("MiniMax request body maps aspect ratio, size, n, and subject references", async (t) => {
+  const dir = await fs.mkdtemp(path.join(os.tmpdir(), "minimax-test-"));
+  t.after(() => fs.rm(dir, { recursive: true, force: true }));
+
+  const refPath = path.join(dir, "portrait.png");
+  await fs.writeFile(refPath, Buffer.from("portrait"));
+
+  const ratioBody = await buildRequestBody(
+    "A portrait by the window",
+    "image-01",
+    makeArgs({ aspectRatio: "16:9", n: 2, referenceImages: [refPath] }),
+  );
+  assert.equal(ratioBody.aspect_ratio, "16:9");
+  assert.equal(ratioBody.n, 2);
+  assert.equal(ratioBody.response_format, "base64");
+  assert.match(ratioBody.subject_reference?.[0]?.image_file || "", /^data:image\/png;base64,/);
+
+  const sizeBody = await buildRequestBody(
+    "A portrait by the window",
+    "image-01",
+    makeArgs({ size: "1536x1024" }),
+  );
+  assert.equal(sizeBody.width, 1536);
+  assert.equal(sizeBody.height, 1024);
+  assert.equal(sizeBody.aspect_ratio, undefined);
+});
+
+test("MiniMax subject references require supported file types", async (t) => {
+  const dir = await fs.mkdtemp(path.join(os.tmpdir(), "minimax-ref-"));
+  t.after(() => fs.rm(dir, { recursive: true, force: true }));
+
+  const good = path.join(dir, "portrait.jpg");
+  const bad = path.join(dir, "portrait.webp");
+  await fs.writeFile(good, Buffer.from("portrait"));
+  await fs.writeFile(bad, Buffer.from("portrait"));
+
+  const subjectReference = await buildSubjectReference([good]);
+  assert.equal(subjectReference?.[0]?.type, "character");
+
+  await assert.rejects(
+    () => buildSubjectReference([bad]),
+    /only supports JPG, JPEG, or PNG/,
+  );
+});
+
+test("MiniMax response extraction supports base64 and URL payloads", async (t) => {
+  const originalFetch = globalThis.fetch;
+  t.after(() => {
+    globalThis.fetch = originalFetch;
+  });
+
+  const fromBase64 = await extractImageFromResponse({
+    data: {
+      image_base64: [Buffer.from("hello").toString("base64")],
+    },
+  });
+  assert.equal(Buffer.from(fromBase64).toString("utf8"), "hello");
+
+  globalThis.fetch = async () =>
+    new Response(Uint8Array.from([1, 2, 3]), {
+      status: 200,
+      headers: { "Content-Type": "image/jpeg" },
+    });
+
+  const fromUrl = await extractImageFromResponse({
+    data: {
+      image_urls: ["https://example.com/output.jpg"],
+    },
+  });
+  assert.deepEqual([...fromUrl], [1, 2, 3]);
+
+  await assert.rejects(
+    () => extractImageFromResponse({ base_resp: { status_code: 1001, status_msg: "blocked" } }),
+    /blocked/,
+  );
+});
@@ -0,0 +1,220 @@
+import path from "node:path";
+import { readFile } from "node:fs/promises";
+
+import type { CliArgs } from "../types";
+
+const DEFAULT_MODEL = "image-01";
+const MAX_REFERENCE_IMAGE_BYTES = 10 * 1024 * 1024;
+const SUPPORTED_ASPECT_RATIOS = new Set(["1:1", "16:9", "4:3", "3:2", "2:3", "3:4", "9:16", "21:9"]);
+
+type MinimaxSubjectReference = {
+  type: "character";
+  image_file: string;
+};
+
+type MinimaxRequestBody = {
+  model: string;
+  prompt: string;
+  response_format: "base64";
+  aspect_ratio?: string;
+  width?: number;
+  height?: number;
+  n?: number;
+  subject_reference?: MinimaxSubjectReference[];
+};
+
+type MinimaxResponse = {
+  id?: string;
+  data?: {
+    image_urls?: string[];
+    image_base64?: string[];
+  };
+  base_resp?: {
+    status_code?: number;
+    status_msg?: string;
+  };
+};
+
+export function getDefaultModel(): string {
+  return process.env.MINIMAX_IMAGE_MODEL || DEFAULT_MODEL;
+}
+
+function getApiKey(): string | null {
+  return process.env.MINIMAX_API_KEY || null;
+}
+
+export function buildMinimaxUrl(): string {
+  const base = (process.env.MINIMAX_BASE_URL || "https://api.minimax.io").replace(/\/+$/g, "");
+  return base.endsWith("/v1") ? `${base}/image_generation` : `${base}/v1/image_generation`;
+}
+
+function getMimeType(filename: string): "image/jpeg" | "image/png" {
+  const ext = path.extname(filename).toLowerCase();
+  if (ext === ".jpg" || ext === ".jpeg") return "image/jpeg";
+  if (ext === ".png") return "image/png";
+  throw new Error(
+    `MiniMax subject_reference only supports JPG, JPEG, or PNG files: ${filename}`
+  );
+}
+
+export function parsePixelSize(size: string): { width: number; height: number } | null {
+  const match = size.trim().match(/^(\d+)\s*[xX*]\s*(\d+)$/);
+  if (!match) return null;
+
+  const width = parseInt(match[1]!, 10);
+  const height = parseInt(match[2]!, 10);
+  if (!Number.isFinite(width) || !Number.isFinite(height) || width <= 0 || height <= 0) {
+    return null;
+  }
+
+  return { width, height };
+}
+
+function validatePixelSize(width: number, height: number): void {
+  if (width < 512 || width > 2048 || height < 512 || height > 2048) {
+    throw new Error("MiniMax custom size must keep width and height between 512 and 2048.");
+  }
+  if (width % 8 !== 0 || height % 8 !== 0) {
+    throw new Error("MiniMax custom size requires width and height divisible by 8.");
+  }
+}
+
+export function validateArgs(model: string, args: CliArgs): void {
+  if (args.n > 9) {
+    throw new Error("MiniMax supports at most 9 images per request.");
+  }
+
+  if (args.aspectRatio && !SUPPORTED_ASPECT_RATIOS.has(args.aspectRatio)) {
+    throw new Error(
+      `MiniMax aspect_ratio must be one of: ${Array.from(SUPPORTED_ASPECT_RATIOS).join(", ")}.`
+    );
+  }
+
+  if (args.size && !args.aspectRatio) {
+    if (model !== "image-01") {
+      throw new Error("MiniMax custom --size is only supported with model image-01. Use --model image-01 or pass --ar instead.");
+    }
+    const parsed = parsePixelSize(args.size);
+    if (!parsed) {
+      throw new Error("MiniMax --size must be in WxH format, for example 1536x1024.");
+    }
+    validatePixelSize(parsed.width, parsed.height);
+  }
+}
+
+export async function buildSubjectReference(
+  referenceImages: string[],
+): Promise<MinimaxSubjectReference[] | undefined> {
+  if (referenceImages.length === 0) return undefined;
+
+  const subjectReference: MinimaxSubjectReference[] = [];
+  for (const refPath of referenceImages) {
+    const bytes = await readFile(refPath);
+    if (bytes.length > MAX_REFERENCE_IMAGE_BYTES) {
+      throw new Error(`MiniMax subject_reference images must be smaller than 10MB: ${refPath}`);
+    }
+
+    subjectReference.push({
+      type: "character",
+      image_file: `data:${getMimeType(refPath)};base64,${bytes.toString("base64")}`,
+    });
+  }
+
+  return subjectReference;
+}
+
+export async function buildRequestBody(
+  prompt: string,
+  model: string,
+  args: CliArgs,
+): Promise<MinimaxRequestBody> {
+  validateArgs(model, args);
+
+  const body: MinimaxRequestBody = {
+    model,
+    prompt,
+    response_format: "base64",
+  };
+
+  if (args.aspectRatio) {
+    body.aspect_ratio = args.aspectRatio;
+  } else if (args.size) {
+    const parsed = parsePixelSize(args.size);
+    if (!parsed) {
+      throw new Error("MiniMax --size must be in WxH format, for example 1536x1024.");
+    }
+    body.width = parsed.width;
+    body.height = parsed.height;
+  }
+
+  if (args.n > 1) {
+    body.n = args.n;
+  }
+
+  const subjectReference = await buildSubjectReference(args.referenceImages);
+  if (subjectReference) {
+    body.subject_reference = subjectReference;
+  }
+
+  return body;
+}
+
+async function downloadImage(url: string): Promise<Uint8Array> {
+  const response = await fetch(url);
+  if (!response.ok) {
+    throw new Error(`Failed to download image from MiniMax: ${response.status}`);
+  }
+  return new Uint8Array(await response.arrayBuffer());
+}
+
+export async function extractImageFromResponse(result: MinimaxResponse): Promise<Uint8Array> {
+  const baseResp = result.base_resp;
+  if (baseResp && baseResp.status_code !== undefined && baseResp.status_code !== 0) {
+    throw new Error(baseResp.status_msg || `MiniMax API returned status_code=${baseResp.status_code}`);
+  }
+
+  const base64Image = result.data?.image_base64?.[0];
+  if (base64Image) {
+    return Uint8Array.from(Buffer.from(base64Image, "base64"));
+  }
+
+  const url = result.data?.image_urls?.[0];
+  if (url) {
+    return downloadImage(url);
+  }
+
+  throw new Error("No image data in MiniMax response");
+}
+
+export function getDefaultOutputExtension(): ".jpg" {
+  return ".jpg";
+}
+
+export async function generateImage(
+  prompt: string,
+  model: string,
+  args: CliArgs
+): Promise<Uint8Array> {
+  const apiKey = getApiKey();
+  if (!apiKey) {
+    throw new Error("MINIMAX_API_KEY is required. Get one from https://platform.minimax.io/");
+  }
+
+  const body = await buildRequestBody(prompt, model, args);
+  const response = await fetch(buildMinimaxUrl(), {
+    method: "POST",
+    headers: {
+      "Content-Type": "application/json",
+      Authorization: `Bearer ${apiKey}`,
+    },
+    body: JSON.stringify(body),
+  });
+
+  if (!response.ok) {
+    const err = await response.text();
+    throw new Error(`MiniMax API error (${response.status}): ${err}`);
+  }
+
+  const result = (await response.json()) as MinimaxResponse;
+  return extractImageFromResponse(result);
+}
@@ -0,0 +1,56 @@
+import assert from "node:assert/strict";
+import test from "node:test";
+
+import {
+  extractImageFromResponse,
+  getMimeType,
+  getOpenAISize,
+  parseAspectRatio,
+} from "./openai.ts";
+
+test("OpenAI aspect-ratio parsing and size selection match model families", () => {
+  assert.deepEqual(parseAspectRatio("16:9"), { width: 16, height: 9 });
+  assert.equal(parseAspectRatio("wide"), null);
+  assert.equal(parseAspectRatio("0:1"), null);
+
+  assert.equal(getOpenAISize("dall-e-3", "16:9", "2k"), "1792x1024");
+  assert.equal(getOpenAISize("dall-e-3", "9:16", "normal"), "1024x1792");
+  assert.equal(getOpenAISize("dall-e-2", "16:9", "2k"), "1024x1024");
+  assert.equal(getOpenAISize("gpt-image-1.5", "16:9", "2k"), "1536x1024");
+  assert.equal(getOpenAISize("gpt-image-1.5", "4:3", "2k"), "1024x1024");
+});
+
+test("OpenAI mime-type detection covers supported reference image extensions", () => {
+  assert.equal(getMimeType("frame.png"), "image/png");
+  assert.equal(getMimeType("frame.jpg"), "image/jpeg");
+  assert.equal(getMimeType("frame.webp"), "image/webp");
+  assert.equal(getMimeType("frame.gif"), "image/gif");
+});
+
+test("OpenAI response extraction supports base64 and URL download flows", async (t) => {
+  const originalFetch = globalThis.fetch;
+  t.after(() => {
+    globalThis.fetch = originalFetch;
+  });
+
+  const fromBase64 = await extractImageFromResponse({
+    data: [{ b64_json: Buffer.from("hello").toString("base64") }],
+  });
+  assert.equal(Buffer.from(fromBase64).toString("utf8"), "hello");
+
+  globalThis.fetch = async () =>
+    new Response(Uint8Array.from([1, 2, 3]), {
+      status: 200,
+      headers: { "Content-Type": "application/octet-stream" },
+    });
+
+  const fromUrl = await extractImageFromResponse({
+    data: [{ url: "https://example.com/image.png" }],
+  });
+  assert.deepEqual([...fromUrl], [1, 2, 3]);
+
+  await assert.rejects(
+    () => extractImageFromResponse({ data: [{}] }),
+    /No image in response/,
+  );
+});
@@ -0,0 +1,227 @@
+import path from "node:path";
+import { readFile } from "node:fs/promises";
+import type { CliArgs } from "../types";
+
+export function getDefaultModel(): string {
+  return process.env.OPENAI_IMAGE_MODEL || "gpt-image-1.5";
+}
+
+type OpenAIImageResponse = { data: Array<{ url?: string; b64_json?: string }> };
+
+export function parseAspectRatio(ar: string): { width: number; height: number } | null {
+  const match = ar.match(/^(\d+(?:\.\d+)?):(\d+(?:\.\d+)?)$/);
+  if (!match) return null;
+  const w = parseFloat(match[1]!);
+  const h = parseFloat(match[2]!);
+  if (w <= 0 || h <= 0) return null;
+  return { width: w, height: h };
+}
+
+type SizeMapping = {
+  square: string;
+  landscape: string;
+  portrait: string;
+};
+
+export function getOpenAISize(
+  model: string,
+  ar: string | null,
+  quality: CliArgs["quality"]
+): string {
+  const isDalle3 = model.includes("dall-e-3");
+  const isDalle2 = model.includes("dall-e-2");
+
+  if (isDalle2) {
+    return "1024x1024";
+  }
+
+  const sizes: SizeMapping = isDalle3
+    ? {
+        square: "1024x1024",
+        landscape: "1792x1024",
+        portrait: "1024x1792",
+      }
+    : {
+        square: "1024x1024",
+        landscape: "1536x1024",
+        portrait: "1024x1536",
+      };
+
+  if (!ar) return sizes.square;
+
+  const parsed = parseAspectRatio(ar);
+  if (!parsed) return sizes.square;
+
+  const ratio = parsed.width / parsed.height;
+
+  if (Math.abs(ratio - 1) < 0.1) return sizes.square;
+  if (ratio > 1.5) return sizes.landscape;
+  if (ratio < 0.67) return sizes.portrait;
+  return sizes.square;
+}
+
+export async function generateImage(
+  prompt: string,
+  model: string,
+  args: CliArgs
+): Promise<Uint8Array> {
+  const baseURL = process.env.OPENAI_BASE_URL || "https://api.openai.com/v1";
+  const apiKey = process.env.OPENAI_API_KEY;
+
+  if (!apiKey) {
+    throw new Error(
+      "OPENAI_API_KEY is required. Codex/ChatGPT desktop login does not automatically grant OpenAI Images API access to this script."
+    );
+  }
+
+  if (process.env.OPENAI_IMAGE_USE_CHAT === "true") {
+    return generateWithChatCompletions(baseURL, apiKey, prompt, model);
+  }
+
+  const size = args.size || getOpenAISize(model, args.aspectRatio, args.quality);
+
+  if (args.referenceImages.length > 0) {
+    if (model.includes("dall-e-2") || model.includes("dall-e-3")) {
+      throw new Error(
+        "Reference images with OpenAI in this skill require GPT Image models. Use --model gpt-image-1.5 (or another gpt-image model)."
+      );
+    }
+    return generateWithOpenAIEdits(baseURL, apiKey, prompt, model, size, args.referenceImages, args.quality);
+  }
+
+  return generateWithOpenAIGenerations(baseURL, apiKey, prompt, model, size, args.quality);
+}
+
+async function generateWithChatCompletions(
+  baseURL: string,
+  apiKey: string,
+  prompt: string,
+  model: string
+): Promise<Uint8Array> {
+  const res = await fetch(`${baseURL}/chat/completions`, {
+    method: "POST",
+    headers: {
+      "Content-Type": "application/json",
+      Authorization: `Bearer ${apiKey}`,
+    },
+    body: JSON.stringify({
+      model,
+      messages: [{ role: "user", content: prompt }],
+    }),
+  });
+
+  if (!res.ok) {
+    const err = await res.text();
+    throw new Error(`OpenAI API error: ${err}`);
+  }
+
+  const result = (await res.json()) as { choices: Array<{ message: { content: string } }> };
+  const content = result.choices[0]?.message?.content ?? "";
+
+  const match = content.match(/data:image\/[^;]+;base64,([A-Za-z0-9+/=]+)/);
+  if (match) {
+    return Uint8Array.from(Buffer.from(match[1]!, "base64"));
+  }
+
+  throw new Error("No image found in chat completions response");
+}
+
+async function generateWithOpenAIGenerations(
+  baseURL: string,
+  apiKey: string,
+  prompt: string,
+  model: string,
+  size: string,
+  quality: CliArgs["quality"]
+): Promise<Uint8Array> {
+  const body: Record<string, any> = { model, prompt, size };
+
+  if (model.includes("dall-e-3")) {
+    body.quality = quality === "2k" ? "hd" : "standard";
+  }
+
+  const res = await fetch(`${baseURL}/images/generations`, {
+    method: "POST",
+    headers: {
+      "Content-Type": "application/json",
+      Authorization: `Bearer ${apiKey}`,
+    },
+    body: JSON.stringify(body),
+  });
+
+  if (!res.ok) {
+    const err = await res.text();
+    throw new Error(`OpenAI API error: ${err}`);
+  }
+
+  const result = (await res.json()) as OpenAIImageResponse;
+  return extractImageFromResponse(result);
+}
+
+async function generateWithOpenAIEdits(
+  baseURL: string,
+  apiKey: string,
+  prompt: string,
+  model: string,
+  size: string,
+  referenceImages: string[],
+  quality: CliArgs["quality"]
+): Promise<Uint8Array> {
+  const form = new FormData();
+  form.append("model", model);
+  form.append("prompt", prompt);
+  form.append("size", size);
+
+  if (model.includes("gpt-image")) {
+    form.append("quality", quality === "2k" ? "high" : "medium");
+  }
+
+  for (const refPath of referenceImages) {
+    const bytes = await readFile(refPath);
+    const filename = path.basename(refPath);
+    const mimeType = getMimeType(filename);
+    const blob = new Blob([bytes], { type: mimeType });
+    form.append("image[]", blob, filename);
+  }
+
+  const res = await fetch(`${baseURL}/images/edits`, {
+    method: "POST",
+    headers: {
+      Authorization: `Bearer ${apiKey}`,
+    },
+    body: form,
+  });
+
+  if (!res.ok) {
+    const err = await res.text();
+    throw new Error(`OpenAI edits API error: ${err}`);
+  }
+
+  const result = (await res.json()) as OpenAIImageResponse;
+  return extractImageFromResponse(result);
+}
+
+export function getMimeType(filename: string): string {
+  const ext = path.extname(filename).toLowerCase();
+  if (ext === ".jpg" || ext === ".jpeg") return "image/jpeg";
+  if (ext === ".webp") return "image/webp";
+  if (ext === ".gif") return "image/gif";
+  return "image/png";
+}
+
+export async function extractImageFromResponse(result: OpenAIImageResponse): Promise<Uint8Array> {
+  const img = result.data[0];
+
+  if (img?.b64_json) {
+    return Uint8Array.from(Buffer.from(img.b64_json, "base64"));
+  }
+
+  if (img?.url) {
+    const imgRes = await fetch(img.url);
+    if (!imgRes.ok) throw new Error("Failed to download image");
+    const buf = await imgRes.arrayBuffer();
+    return new Uint8Array(buf);
+  }
+
+  throw new Error("No image in response");
+}
@@ -0,0 +1,168 @@
+import assert from "node:assert/strict";
+import test from "node:test";
+
+import type { CliArgs } from "../types.ts";
+import {
+  buildContent,
+  buildRequestBody,
+  extractImageFromResponse,
+  getAspectRatio,
+  getImageSize,
+  validateArgs,
+} from "./openrouter.ts";
+
+const GEMINI_MODEL = "google/gemini-3.1-flash-image-preview";
+const GEMINI_25_MODEL = "google/gemini-2.5-flash-image";
+const GPT_5_IMAGE_MODEL = "openai/gpt-5-image";
+const OPENROUTER_AUTO_MODEL = "openrouter/auto";
+const FLUX_MODEL = "black-forest-labs/flux.2-pro";
+
+function makeArgs(overrides: Partial<CliArgs> = {}): CliArgs {
+  return {
+    prompt: null,
+    promptFiles: [],
+    imagePath: null,
+    provider: null,
+    model: null,
+    aspectRatio: null,
+    size: null,
+    quality: null,
+    imageSize: null,
+    referenceImages: [],
+    n: 1,
+    batchFile: null,
+    jobs: null,
+    json: false,
+    help: false,
+    ...overrides,
+  };
+}
+
+test("OpenRouter request body uses image_config and string content for text-only prompts", () => {
+  const args = makeArgs({ aspectRatio: "16:9", quality: "2k" });
+  const body = buildRequestBody("hello", GEMINI_MODEL, args, []);
+
+  assert.deepEqual(body.image_config, {
+    image_size: "2K",
+    aspect_ratio: "16:9",
+  });
+  assert.deepEqual(body.provider, {
+    require_parameters: true,
+  });
+  assert.deepEqual(body.modalities, ["image", "text"]);
+  assert.equal(body.stream, false);
+  assert.equal(body.messages[0].content, "hello");
+});
+
+test("OpenRouter request body keeps text+image modalities for current text+image models", () => {
+  for (const model of [GEMINI_MODEL, GEMINI_25_MODEL, GPT_5_IMAGE_MODEL, OPENROUTER_AUTO_MODEL]) {
+    const body = buildRequestBody("hello", model, makeArgs({ quality: "2k" }), []);
+
+    assert.deepEqual(body.image_config, {
+      image_size: "2K",
+    });
+    assert.deepEqual(body.provider, {
+      require_parameters: true,
+    });
+    assert.deepEqual(body.modalities, ["image", "text"]);
+    assert.equal(body.messages[0].content, "hello");
+  }
+});
+
+test("OpenRouter request body uses image-only modalities for image-only models under CLI defaults", () => {
+  const body = buildRequestBody("hello", FLUX_MODEL, makeArgs({ quality: "2k" }), []);
+
+  assert.deepEqual(body.image_config, {
+    image_size: "2K",
+  });
+  assert.deepEqual(body.provider, {
+    require_parameters: true,
+  });
+  assert.deepEqual(body.modalities, ["image"]);
+  assert.equal(body.stream, false);
+  assert.equal(body.messages[0].content, "hello");
+});
+
+test("OpenRouter helper omits image_config when no size or quality is passed", () => {
+  const body = buildRequestBody("hello", FLUX_MODEL, makeArgs(), []);
+
+  assert.equal(body.image_config, undefined);
+  assert.equal(body.provider, undefined);
+  assert.deepEqual(body.modalities, ["image"]);
+  assert.equal(body.stream, false);
+  assert.equal(body.messages[0].content, "hello");
+});
+
+test("OpenRouter request body keeps multimodal array content when references are provided", () => {
+  const content = buildContent("hello", ["data:image/png;base64,abc"]);
+  assert.ok(Array.isArray(content));
+  assert.deepEqual(content[0], { type: "text", text: "hello" });
+  assert.deepEqual(content[1], {
+    type: "image_url",
+    image_url: { url: "data:image/png;base64,abc" },
+  });
+});
+
+test("OpenRouter size and aspect helpers infer supported values", () => {
+  assert.equal(getImageSize(makeArgs()), null);
+  assert.equal(getImageSize(makeArgs({ quality: "normal" })), "1K");
+  assert.equal(getImageSize(makeArgs({ size: "2048x1024" })), "2K");
+  assert.equal(getAspectRatio(GEMINI_MODEL, makeArgs({ size: "1600x900" })), "16:9");
+  assert.equal(getAspectRatio(GEMINI_MODEL, makeArgs({ size: "1024x4096" })), "1:4");
+  assert.equal(getAspectRatio(GEMINI_25_MODEL, makeArgs({ size: "1600x900" })), "16:9");
+  assert.equal(getAspectRatio(FLUX_MODEL, makeArgs({ size: "1024x4096" })), null);
+});
+
+test("OpenRouter validates explicit aspect ratios and inferred size ratios against model support", () => {
+  assert.doesNotThrow(() =>
+    validateArgs(GEMINI_MODEL, makeArgs({ aspectRatio: "1:4" })),
+  );
+  assert.doesNotThrow(() =>
+    validateArgs(GEMINI_MODEL, makeArgs({ size: "1024x4096" })),
+  );
+  assert.throws(
+    () => validateArgs(GEMINI_25_MODEL, makeArgs({ aspectRatio: "1:4" })),
+    /does not support aspect ratio 1:4/,
+  );
+  assert.throws(
+    () => validateArgs(FLUX_MODEL, makeArgs({ aspectRatio: "1:4" })),
+    /does not support aspect ratio 1:4/,
+  );
+  assert.throws(
+    () => validateArgs(GEMINI_MODEL, makeArgs({ size: "2048x1024" })),
+    /does not support size 2048x1024 \(aspect ratio 2:1\)/,
+  );
+});
+
+test("OpenRouter response extraction supports inline image data and finish_reason errors", async () => {
+  const bytes = await extractImageFromResponse({
+    choices: [
+      {
+        message: {
+          images: [
+            {
+              image_url: {
+                url: `data:image/png;base64,${Buffer.from("hello").toString("base64")}`,
+              },
+            },
+          ],
+        },
+      },
+    ],
+  });
+  assert.equal(Buffer.from(bytes).toString("utf8"), "hello");
+
+  await assert.rejects(
+    () =>
+      extractImageFromResponse({
+        choices: [
+          {
+            finish_reason: "error",
+            native_finish_reason: "MALFORMED_FUNCTION_CALL",
+            message: { content: null },
+          },
+        ],
+      }),
+    /finish_reason=MALFORMED_FUNCTION_CALL/,
+  );
+});
@@ -0,0 +1,369 @@
+import path from "node:path";
+import { readFile } from "node:fs/promises";
+import type { CliArgs } from "../types";
+
+const DEFAULT_MODEL = "google/gemini-3.1-flash-image-preview";
+const COMMON_ASPECT_RATIOS = [
+  "1:1",
+  "2:3",
+  "3:2",
+  "3:4",
+  "4:3",
+  "4:5",
+  "5:4",
+  "9:16",
+  "16:9",
+  "21:9",
+];
+const GEMINI_EXTENDED_ASPECT_RATIOS = ["1:4", "4:1", "1:8", "8:1"];
+
+type OpenRouterImageEntry = {
+  image_url?: string | { url?: string | null } | null;
+  imageUrl?: string | { url?: string | null } | null;
+};
+
+type OpenRouterMessagePart = {
+  type?: string;
+  text?: string;
+  image_url?: string | { url?: string | null } | null;
+  imageUrl?: string | { url?: string | null } | null;
+};
+
+type OpenRouterResponse = {
+  choices?: Array<{
+    finish_reason?: string | null;
+    native_finish_reason?: string | null;
+    message?: {
+      images?: OpenRouterImageEntry[];
+      content?: string | OpenRouterMessagePart[] | null;
+    };
+  }>;
+};
+
+export function getDefaultModel(): string {
+  return process.env.OPENROUTER_IMAGE_MODEL || DEFAULT_MODEL;
+}
+
+function normalizeModelId(model: string): string {
+  return model.trim().toLowerCase().split(":")[0]!;
+}
+
+function isTextAndImageModel(model: string): boolean {
+  const normalized = normalizeModelId(model);
+  if (normalized === "openrouter/auto") {
+    return true;
+  }
+
+  if (normalized.startsWith("google/gemini-") && normalized.includes("image")) {
+    return true;
+  }
+
+  if (normalized.startsWith("openai/gpt-") && normalized.includes("image")) {
+    return true;
+  }
+
+  return false;
+}
+
+function getSupportedAspectRatios(model: string): Set<string> {
+  const normalized = normalizeModelId(model);
+  if (normalized !== "google/gemini-3.1-flash-image-preview") {
+    return new Set(COMMON_ASPECT_RATIOS);
+  }
+
+  return new Set([...COMMON_ASPECT_RATIOS, ...GEMINI_EXTENDED_ASPECT_RATIOS]);
+}
+
+function getApiKey(): string | null {
+  return process.env.OPENROUTER_API_KEY || null;
+}
+
+function getBaseUrl(): string {
+  const base = process.env.OPENROUTER_BASE_URL || "https://openrouter.ai/api/v1";
+  return base.replace(/\/+$/g, "");
+}
+
+function getHeaders(apiKey: string): Record<string, string> {
+  const headers: Record<string, string> = {
+    "Content-Type": "application/json",
+    Authorization: `Bearer ${apiKey}`,
+  };
+
+  const referer = process.env.OPENROUTER_HTTP_REFERER?.trim();
+  if (referer) {
+    headers["HTTP-Referer"] = referer;
+  }
+
+  const title = process.env.OPENROUTER_TITLE?.trim();
+  if (title) {
+    headers["X-OpenRouter-Title"] = title;
+    headers["X-Title"] = title;
+  }
+
+  return headers;
+}
+
+function parsePixelSize(value: string): { width: number; height: number } | null {
+  const match = value.match(/^(\d+)\s*[xX]\s*(\d+)$/);
+  if (!match) return null;
+
+  const width = parseInt(match[1]!, 10);
+  const height = parseInt(match[2]!, 10);
+
+  if (!Number.isFinite(width) || !Number.isFinite(height) || width <= 0 || height <= 0) {
+    return null;
+  }
+
+  return { width, height };
+}
+
+function gcd(a: number, b: number): number {
+  let x = Math.abs(a);
+  let y = Math.abs(b);
+  while (y !== 0) {
+    const next = x % y;
+    x = y;
+    y = next;
+  }
+  return x || 1;
+}
+
+function inferAspectRatio(size: string | null): string | null {
+  if (!size) return null;
+  const parsed = parsePixelSize(size);
+  if (!parsed) return null;
+
+  const divisor = gcd(parsed.width, parsed.height);
+  return `${parsed.width / divisor}:${parsed.height / divisor}`;
+}
+
+function inferImageSize(size: string | null): "1K" | "2K" | "4K" | null {
+  if (!size) return null;
+  const parsed = parsePixelSize(size);
+  if (!parsed) return null;
+
+  const longestEdge = Math.max(parsed.width, parsed.height);
+  if (longestEdge <= 1024) return "1K";
+  if (longestEdge <= 2048) return "2K";
+  return "4K";
+}
+
+export function getImageSize(args: CliArgs): "1K" | "2K" | "4K" | null {
+  if (args.imageSize) return args.imageSize as "1K" | "2K" | "4K";
+
+  const inferredFromSize = inferImageSize(args.size);
+  if (inferredFromSize) return inferredFromSize;
+
+  if (args.quality === "normal") return "1K";
+  if (args.quality === "2k") return "2K";
+  return null;
+}
+
+export function getAspectRatio(model: string, args: CliArgs): string | null {
+  if (args.aspectRatio) return args.aspectRatio;
+
+  const inferred = inferAspectRatio(args.size);
+  if (!inferred || !getSupportedAspectRatios(model).has(inferred)) {
+    return null;
+  }
+
+  return inferred;
+}
+
+function getModalities(model: string): string[] {
+  return isTextAndImageModel(model) ? ["image", "text"] : ["image"];
+}
+
+export function validateArgs(model: string, args: CliArgs): void {
+  const requestedAspectRatio = args.aspectRatio || inferAspectRatio(args.size);
+  if (!requestedAspectRatio) {
+    return;
+  }
+
+  const supported = getSupportedAspectRatios(model);
+  if (supported.has(requestedAspectRatio)) {
+    return;
+  }
+
+  const requestedValue = args.aspectRatio
+    ? `aspect ratio ${requestedAspectRatio}`
+    : `size ${args.size} (aspect ratio ${requestedAspectRatio})`;
+
+  throw new Error(
+    `OpenRouter model ${model} does not support ${requestedValue}. Supported values: ${Array.from(supported).join(", ")}`
+  );
+}
+
+function getMimeType(filename: string): string {
+  const ext = path.extname(filename).toLowerCase();
+  if (ext === ".jpg" || ext === ".jpeg") return "image/jpeg";
+  if (ext === ".webp") return "image/webp";
+  if (ext === ".gif") return "image/gif";
+  return "image/png";
+}
+
+async function readImageAsDataUrl(filePath: string): Promise<string> {
+  const bytes = await readFile(filePath);
+  return `data:${getMimeType(filePath)};base64,${bytes.toString("base64")}`;
+}
+
+export function buildContent(
+  prompt: string,
+  referenceImages: string[],
+): string | Array<Record<string, unknown>> {
+  if (referenceImages.length === 0) {
+    return prompt;
+  }
+
+  const content: Array<Record<string, unknown>> = [{ type: "text", text: prompt }];
+
+  for (const imageUrl of referenceImages) {
+    content.push({
+      type: "image_url",
+      image_url: { url: imageUrl },
+    });
+  }
+
+  return content;
+}
+
+function extractImageUrl(entry: OpenRouterImageEntry | OpenRouterMessagePart): string | null {
+  const value = entry.image_url ?? entry.imageUrl;
+  if (!value) return null;
+  if (typeof value === "string") return value;
+  return value.url ?? null;
+}
+
+function decodeDataUrl(value: string): Uint8Array | null {
+  const match = value.match(/^data:image\/[^;]+;base64,([A-Za-z0-9+/=]+)$/);
+  if (!match) return null;
+  return Uint8Array.from(Buffer.from(match[1]!, "base64"));
+}
+
+async function downloadImage(value: string): Promise<Uint8Array> {
+  const inline = decodeDataUrl(value);
+  if (inline) return inline;
+
+  if (value.startsWith("http://") || value.startsWith("https://")) {
+    const response = await fetch(value);
+    if (!response.ok) {
+      throw new Error(`Failed to download OpenRouter image: ${response.status}`);
+    }
+    const buffer = await response.arrayBuffer();
+    return new Uint8Array(buffer);
+  }
+
+  return Uint8Array.from(Buffer.from(value, "base64"));
+}
+
+export async function extractImageFromResponse(result: OpenRouterResponse): Promise<Uint8Array> {
+  const choice = result.choices?.[0];
+  const message = choice?.message;
+
+  for (const image of message?.images ?? []) {
+    const imageUrl = extractImageUrl(image);
+    if (imageUrl) return downloadImage(imageUrl);
+  }
+
+  if (Array.isArray(message?.content)) {
+    for (const item of message.content) {
+      const imageUrl = extractImageUrl(item);
+      if (imageUrl) return downloadImage(imageUrl);
+
+      if (item.type === "text" && item.text) {
+        const inline = decodeDataUrl(item.text);
+        if (inline) return inline;
+      }
+    }
+  } else if (typeof message?.content === "string") {
+    const inline = decodeDataUrl(message.content);
+    if (inline) return inline;
+  }
+
+  const finishReason =
+    choice?.native_finish_reason || choice?.finish_reason || "unknown";
+  throw new Error(
+    `No image in OpenRouter response (finish_reason=${finishReason})`,
+  );
+}
+
+export function buildRequestBody(
+  prompt: string,
+  model: string,
+  args: CliArgs,
+  referenceImages: string[],
+): Record<string, unknown> {
+  validateArgs(model, args);
+
+  const imageConfig: Record<string, string> = {};
+
+  const imageSize = getImageSize(args);
+  if (imageSize) {
+    imageConfig.image_size = imageSize;
+  }
+
+  const aspectRatio = getAspectRatio(model, args);
+  if (aspectRatio) {
+    imageConfig.aspect_ratio = aspectRatio;
+  }
+
+  const body: Record<string, unknown> = {
+    messages: [
+      {
+        role: "user",
+        content: buildContent(prompt, referenceImages),
+      },
+    ],
+    modalities: getModalities(model),
+    stream: false,
+  };
+
+  if (Object.keys(imageConfig).length > 0) {
+    body.image_config = imageConfig;
+    body.provider = {
+      require_parameters: true,
+    };
+  }
+
+  return body;
+}
+
+export async function generateImage(
+  prompt: string,
+  model: string,
+  args: CliArgs
+): Promise<Uint8Array> {
+  const apiKey = getApiKey();
+  if (!apiKey) {
+    throw new Error("OPENROUTER_API_KEY is required. Get one at https://openrouter.ai/settings/keys");
+  }
+
+  const referenceImages: string[] = [];
+  for (const refPath of args.referenceImages) {
+    referenceImages.push(await readImageAsDataUrl(refPath));
+  }
+
+  const body = {
+    model,
+    ...buildRequestBody(prompt, model, args, referenceImages),
+  };
+
+  console.log(
+    `Generating image with OpenRouter (${model})...`,
+    (body.image_config as Record<string, string>),
+  );
+
+  const response = await fetch(`${getBaseUrl()}/chat/completions`, {
+    method: "POST",
+    headers: getHeaders(apiKey),
+    body: JSON.stringify(body),
+  });
+
+  if (!response.ok) {
+    const errorText = await response.text();
+    throw new Error(`OpenRouter API error (${response.status}): ${errorText}`);
+  }
+
+  const result = (await response.json()) as OpenRouterResponse;
+  return extractImageFromResponse(result);
+}
@@ -0,0 +1,101 @@
+import assert from "node:assert/strict";
+import test from "node:test";
+
+import type { CliArgs } from "../types.ts";
+import {
+  buildInput,
+  extractOutputUrl,
+  parseModelId,
+} from "./replicate.ts";
+
+function makeArgs(overrides: Partial<CliArgs> = {}): CliArgs {
+  return {
+    prompt: null,
+    promptFiles: [],
+    imagePath: null,
+    provider: null,
+    model: null,
+    aspectRatio: null,
+    size: null,
+    quality: null,
+    imageSize: null,
+    referenceImages: [],
+    n: 1,
+    batchFile: null,
+    jobs: null,
+    json: false,
+    help: false,
+    ...overrides,
+  };
+}
+
+test("Replicate model parsing accepts official formats and rejects malformed ones", () => {
+  assert.deepEqual(parseModelId("google/nano-banana-pro"), {
+    owner: "google",
+    name: "nano-banana-pro",
+    version: null,
+  });
+  assert.deepEqual(parseModelId("owner/model:abc123"), {
+    owner: "owner",
+    name: "model",
+    version: "abc123",
+  });
+
+  assert.throws(
+    () => parseModelId("just-a-model-name"),
+    /Invalid Replicate model format/,
+  );
+});
+
+test("Replicate input builder maps aspect ratio, image count, quality, and refs", () => {
+  assert.deepEqual(
+    buildInput(
+      "A robot painter",
+      makeArgs({
+        aspectRatio: "16:9",
+        quality: "2k",
+        n: 3,
+      }),
+      ["data:image/png;base64,AAAA"],
+    ),
+    {
+      prompt: "A robot painter",
+      aspect_ratio: "16:9",
+      number_of_images: 3,
+      resolution: "2K",
+      output_format: "png",
+      image_input: ["data:image/png;base64,AAAA"],
+    },
+  );
+
+  assert.deepEqual(
+    buildInput("A robot painter", makeArgs({ quality: "normal" }), ["ref"]),
+    {
+      prompt: "A robot painter",
+      aspect_ratio: "match_input_image",
+      resolution: "1K",
+      output_format: "png",
+      image_input: ["ref"],
+    },
+  );
+});
+
+test("Replicate output extraction supports string, array, and object URLs", () => {
+  assert.equal(
+    extractOutputUrl({ output: "https://example.com/a.png" } as never),
+    "https://example.com/a.png",
+  );
+  assert.equal(
+    extractOutputUrl({ output: ["https://example.com/b.png"] } as never),
+    "https://example.com/b.png",
+  );
+  assert.equal(
+    extractOutputUrl({ output: { url: "https://example.com/c.png" } } as never),
+    "https://example.com/c.png",
+  );
+
+  assert.throws(
+    () => extractOutputUrl({ output: { invalid: true } } as never),
+    /Unexpected Replicate output format/,
+  );
+});
@@ -0,0 +1,205 @@
+import path from "node:path";
+import { readFile } from "node:fs/promises";
+import type { CliArgs } from "../types";
+
+const DEFAULT_MODEL = "google/nano-banana-pro";
+const SYNC_WAIT_SECONDS = 60;
+const POLL_INTERVAL_MS = 2000;
+const MAX_POLL_MS = 300_000;
+
+export function getDefaultModel(): string {
+  return process.env.REPLICATE_IMAGE_MODEL || DEFAULT_MODEL;
+}
+
+function getApiToken(): string | null {
+  return process.env.REPLICATE_API_TOKEN || null;
+}
+
+function getBaseUrl(): string {
+  const base = process.env.REPLICATE_BASE_URL || "https://api.replicate.com";
+  return base.replace(/\/+$/g, "");
+}
+
+export function parseModelId(model: string): { owner: string; name: string; version: string | null } {
+  const [ownerName, version] = model.split(":");
+  const parts = ownerName!.split("/");
+  if (parts.length !== 2 || !parts[0] || !parts[1]) {
+    throw new Error(
+      `Invalid Replicate model format: "${model}". Expected "owner/name" or "owner/name:version".`
+    );
+  }
+  return { owner: parts[0], name: parts[1], version: version || null };
+}
+
+export function buildInput(prompt: string, args: CliArgs, referenceImages: string[]): Record<string, unknown> {
+  const input: Record<string, unknown> = { prompt };
+
+  if (args.aspectRatio) {
+    input.aspect_ratio = args.aspectRatio;
+  } else if (referenceImages.length > 0) {
+    input.aspect_ratio = "match_input_image";
+  }
+
+  if (args.n > 1) {
+    input.number_of_images = args.n;
+  }
+
+  if (args.quality === "normal") {
+    input.resolution = "1K";
+  } else if (args.quality === "2k") {
+    input.resolution = "2K";
+  }
+
+  input.output_format = "png";
+
+  if (referenceImages.length > 0) {
+    input.image_input = referenceImages;
+  }
+
+  return input;
+}
+
+async function readImageAsDataUrl(p: string): Promise<string> {
+  const buf = await readFile(p);
+  const ext = path.extname(p).toLowerCase();
+  let mimeType = "image/png";
+  if (ext === ".jpg" || ext === ".jpeg") mimeType = "image/jpeg";
+  else if (ext === ".gif") mimeType = "image/gif";
+  else if (ext === ".webp") mimeType = "image/webp";
+  return `data:${mimeType};base64,${buf.toString("base64")}`;
+}
+
+type PredictionResponse = {
+  id: string;
+  status: string;
+  output: unknown;
+  error: string | null;
+  urls?: { get?: string };
+};
+
+async function createPrediction(
+  apiToken: string,
+  model: { owner: string; name: string; version: string | null },
+  input: Record<string, unknown>,
+  sync: boolean
+): Promise<PredictionResponse> {
+  const baseUrl = getBaseUrl();
+
+  let url: string;
+  const body: Record<string, unknown> = { input };
+
+  if (model.version) {
+    url = `${baseUrl}/v1/predictions`;
+    body.version = model.version;
+  } else {
+    url = `${baseUrl}/v1/models/${model.owner}/${model.name}/predictions`;
+  }
+
+  const headers: Record<string, string> = {
+    Authorization: `Bearer ${apiToken}`,
+    "Content-Type": "application/json",
+  };
+
+  if (sync) {
+    headers["Prefer"] = `wait=${SYNC_WAIT_SECONDS}`;
+  }
+
+  const res = await fetch(url, {
+    method: "POST",
+    headers,
+    body: JSON.stringify(body),
+  });
+
+  if (!res.ok) {
+    const err = await res.text();
+    throw new Error(`Replicate API error (${res.status}): ${err}`);
+  }
+
+  return (await res.json()) as PredictionResponse;
+}
+
+async function pollPrediction(apiToken: string, getUrl: string): Promise<PredictionResponse> {
+  const start = Date.now();
+
+  while (Date.now() - start < MAX_POLL_MS) {
+    const res = await fetch(getUrl, {
+      headers: { Authorization: `Bearer ${apiToken}` },
+    });
+
+    if (!res.ok) {
+      const err = await res.text();
+      throw new Error(`Replicate poll error (${res.status}): ${err}`);
+    }
+
+    const prediction = (await res.json()) as PredictionResponse;
+
+    if (prediction.status === "succeeded") return prediction;
+    if (prediction.status === "failed" || prediction.status === "canceled") {
+      throw new Error(`Replicate prediction ${prediction.status}: ${prediction.error || "unknown error"}`);
+    }
+
+    await new Promise((r) => setTimeout(r, POLL_INTERVAL_MS));
+  }
+
+  throw new Error(`Replicate prediction timed out after ${MAX_POLL_MS / 1000}s`);
+}
+
+export function extractOutputUrl(prediction: PredictionResponse): string {
+  const output = prediction.output;
+
+  if (typeof output === "string") return output;
+
+  if (Array.isArray(output)) {
+    const first = output[0];
+    if (typeof first === "string") return first;
+  }
+
+  if (output && typeof output === "object" && "url" in output) {
+    const url = (output as Record<string, unknown>).url;
+    if (typeof url === "string") return url;
+  }
+
+  throw new Error(`Unexpected Replicate output format: ${JSON.stringify(output)}`);
+}
+
+async function downloadImage(url: string): Promise<Uint8Array> {
+  const res = await fetch(url);
+  if (!res.ok) throw new Error(`Failed to download image from Replicate: ${res.status}`);
+  const buf = await res.arrayBuffer();
+  return new Uint8Array(buf);
+}
+
+export async function generateImage(
+  prompt: string,
+  model: string,
+  args: CliArgs
+): Promise<Uint8Array> {
+  const apiToken = getApiToken();
+  if (!apiToken) throw new Error("REPLICATE_API_TOKEN is required. Get one at https://replicate.com/account/api-tokens");
+
+  const parsedModel = parseModelId(model);
+
+  const refDataUrls: string[] = [];
+  for (const refPath of args.referenceImages) {
+    refDataUrls.push(await readImageAsDataUrl(refPath));
+  }
+
+  const input = buildInput(prompt, args, refDataUrls);
+
+  console.log(`Generating image with Replicate (${model})...`);
+
+  let prediction = await createPrediction(apiToken, parsedModel, input, true);
+
+  if (prediction.status !== "succeeded") {
+    if (!prediction.urls?.get) {
+      throw new Error("Replicate prediction did not return a poll URL");
+    }
+    console.log("Waiting for prediction to complete...");
+    prediction = await pollPrediction(apiToken, prediction.urls.get);
+  }
+
+  console.log("Generation completed.");
+
+  const outputUrl = extractOutputUrl(prediction);
+  return downloadImage(outputUrl);
+}
@@ -0,0 +1,244 @@
+import assert from "node:assert/strict";
+import fs from "node:fs/promises";
+import os from "node:os";
+import path from "node:path";
+import test, { type TestContext } from "node:test";
+
+import type { CliArgs } from "../types.ts";
+import {
+  buildImageInput,
+  buildRequestBody,
+  generateImage,
+  getDefaultOutputExtension,
+  resolveSeedreamSize,
+  validateArgs,
+} from "./seedream.ts";
+
+function makeArgs(overrides: Partial<CliArgs> = {}): CliArgs {
+  return {
+    prompt: null,
+    promptFiles: [],
+    imagePath: null,
+    provider: null,
+    model: null,
+    aspectRatio: null,
+    size: null,
+    quality: null,
+    imageSize: null,
+    referenceImages: [],
+    n: 1,
+    batchFile: null,
+    jobs: null,
+    json: false,
+    help: false,
+    ...overrides,
+  };
+}
+
+function useEnv(
+  t: TestContext,
+  values: Record<string, string | null>,
+): void {
+  const previous = new Map<string, string | undefined>();
+  for (const [key, value] of Object.entries(values)) {
+    previous.set(key, process.env[key]);
+    if (value == null) {
+      delete process.env[key];
+    } else {
+      process.env[key] = value;
+    }
+  }
+
+  t.after(() => {
+    for (const [key, value] of previous.entries()) {
+      if (value == null) {
+        delete process.env[key];
+      } else {
+        process.env[key] = value;
+      }
+    }
+  });
+}
+
+async function makeTempPng(t: TestContext, name: string): Promise<string> {
+  const dir = await fs.mkdtemp(path.join(os.tmpdir(), "seedream-test-"));
+  t.after(() => fs.rm(dir, { recursive: true, force: true }));
+
+  const filePath = path.join(dir, name);
+  const png1x1 =
+    "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mP8/x8AAwMCAO+a7m0AAAAASUVORK5CYII=";
+  await fs.writeFile(filePath, Buffer.from(png1x1, "base64"));
+  return filePath;
+}
+
+test("Seedream request body and default extensions follow official model capabilities", () => {
+  const five = buildRequestBody(
+    "A robot illustrator",
+    "doubao-seedream-5-0-260128",
+    makeArgs(),
+  );
+  assert.equal(five.size, "2K");
+  assert.equal(five.response_format, "url");
+  assert.equal(five.output_format, "png");
+  assert.equal(getDefaultOutputExtension("doubao-seedream-5-0-260128"), ".png");
+
+  const fourFive = buildRequestBody(
+    "A robot illustrator",
+    "doubao-seedream-4-5-251128",
+    makeArgs(),
+  );
+  assert.equal(fourFive.size, "2K");
+  assert.equal(fourFive.response_format, "url");
+  assert.ok(!("output_format" in fourFive));
+  assert.equal(getDefaultOutputExtension("doubao-seedream-4-5-251128"), ".jpg");
+
+  assert.throws(
+    () =>
+      buildRequestBody(
+        "Change the bubbles into hearts",
+        "doubao-seededit-3-0-i2i-250628",
+        makeArgs({ referenceImages: ["ref.png"] }),
+        "data:image/png;base64,AAAA",
+      ),
+    /no longer supported/,
+  );
+});
+
+test("Seedream size selection validates model-specific presets", () => {
+  assert.equal(
+    resolveSeedreamSize("doubao-seedream-4-0-250828", makeArgs({ quality: "normal" })),
+    "1K",
+  );
+  assert.equal(
+    resolveSeedreamSize("doubao-seedream-3-0-t2i-250415", makeArgs({ quality: "2k" })),
+    "2048x2048",
+  );
+
+  assert.throws(
+    () =>
+      resolveSeedreamSize("doubao-seedream-5-0-260128", makeArgs({ size: "4K" })),
+    /only supports 2K, 3K/,
+  );
+  assert.throws(
+    () =>
+      resolveSeedreamSize("doubao-seedream-3-0-t2i-250415", makeArgs({ imageSize: "2K" })),
+    /only supports explicit WxH sizes/,
+  );
+  assert.throws(
+    () =>
+      resolveSeedreamSize("doubao-seededit-3-0-i2i-250628", makeArgs({ size: "1024x1024" })),
+    /no longer supported/,
+  );
+});
+
+test("Seedream reference-image support is model-specific", () => {
+  assert.doesNotThrow(() =>
+    validateArgs(
+      "doubao-seedream-5-0-260128",
+      makeArgs({ referenceImages: ["a.png", "b.png"] }),
+    ),
+  );
+
+  assert.throws(
+    () =>
+      validateArgs(
+        "doubao-seedream-3-0-t2i-250415",
+        makeArgs({ referenceImages: ["a.png"] }),
+      ),
+    /does not support reference images/,
+  );
+
+  assert.throws(
+    () =>
+      validateArgs(
+        "doubao-seededit-3-0-i2i-250628",
+        makeArgs(),
+      ),
+    /no longer supported/,
+  );
+
+  assert.throws(
+    () =>
+      validateArgs(
+        "ep-20260315171508-t8br2",
+        makeArgs({ referenceImages: ["a.png"] }),
+      ),
+    /require a known model ID/,
+  );
+});
+
+test("Seedream image input encodes local references as data URLs", async (t) => {
+  const refOne = await makeTempPng(t, "one.png");
+  const refTwo = await makeTempPng(t, "two.png");
+
+  const single = await buildImageInput("doubao-seedream-4-5-251128", [refOne]);
+  assert.match(String(single), /^data:image\/png;base64,/);
+
+  const multiple = await buildImageInput("doubao-seedream-5-0-260128", [refOne, refTwo]);
+  assert.ok(Array.isArray(multiple));
+  assert.equal(multiple.length, 2);
+});
+
+test("Seedream generateImage posts the documented response_format and downloads the returned URL", async (t) => {
+  useEnv(t, { ARK_API_KEY: "test-key", SEEDREAM_BASE_URL: null });
+
+  const originalFetch = globalThis.fetch;
+  t.after(() => {
+    globalThis.fetch = originalFetch;
+  });
+
+  const calls: Array<{
+    input: string;
+    init?: RequestInit;
+  }> = [];
+
+  globalThis.fetch = async (input, init) => {
+    calls.push({
+      input: String(input),
+      init,
+    });
+
+    if (calls.length === 1) {
+      return Response.json({
+        model: "doubao-seedream-4-5-251128",
+        created: 1740000000,
+        data: [
+          {
+            url: "https://example.com/generated-image",
+            size: "2048x2048",
+          },
+        ],
+        usage: {
+          generated_images: 1,
+          output_tokens: 1,
+          total_tokens: 1,
+        },
+      });
+    }
+
+    return new Response(Uint8Array.from([7, 8, 9]), {
+      status: 200,
+      headers: { "Content-Type": "image/jpeg" },
+    });
+  };
+
+  const image = await generateImage(
+    "A robot illustrator",
+    "doubao-seedream-4-5-251128",
+    makeArgs(),
+  );
+
+  assert.deepEqual([...image], [7, 8, 9]);
+  assert.equal(calls.length, 2);
+  assert.equal(
+    calls[0]?.input,
+    "https://ark.cn-beijing.volces.com/api/v3/images/generations",
+  );
+
+  const requestBody = JSON.parse(String(calls[0]?.init?.body)) as Record<string, unknown>;
+  assert.equal(requestBody.model, "doubao-seedream-4-5-251128");
+  assert.equal(requestBody.size, "2K");
+  assert.equal(requestBody.response_format, "url");
+  assert.ok(!("output_format" in requestBody));
+  assert.equal(calls[1]?.input, "https://example.com/generated-image");
+});
@@ -0,0 +1,341 @@
+import path from "node:path";
+import { readFile } from "node:fs/promises";
+
+import type { CliArgs } from "../types";
+
+export type SeedreamModelFamily =
+  | "seedream5"
+  | "seedream45"
+  | "seedream40"
+  | "seedream30"
+  | "unknown";
+
+type SeedreamRequestImage = string | string[];
+
+type SeedreamRequestBody = {
+  model: string;
+  prompt: string;
+  size: string;
+  response_format: "url";
+  watermark: boolean;
+  image?: SeedreamRequestImage;
+  output_format?: "png";
+};
+
+type SeedreamImageResponse = {
+  model?: string;
+  created?: number;
+  data?: Array<{
+    url?: string;
+    b64_json?: string;
+    size?: string;
+    error?: {
+      code?: string;
+      message?: string;
+    };
+  }>;
+  usage?: {
+    generated_images: number;
+    output_tokens: number;
+    total_tokens: number;
+  };
+  error?: {
+    code?: string;
+    message?: string;
+  };
+};
+
+export function getDefaultModel(): string {
+  return process.env.SEEDREAM_IMAGE_MODEL || "doubao-seedream-5-0-260128";
+}
+
+function getApiKey(): string | null {
+  return process.env.ARK_API_KEY || null;
+}
+
+function getBaseUrl(): string {
+  return process.env.SEEDREAM_BASE_URL || "https://ark.cn-beijing.volces.com/api/v3";
+}
+
+function parsePixelSize(value: string): { width: number; height: number } | null {
+  const match = value.trim().match(/^(\d+)\s*[xX]\s*(\d+)$/);
+  if (!match) return null;
+
+  const width = parseInt(match[1]!, 10);
+  const height = parseInt(match[2]!, 10);
+  if (!Number.isFinite(width) || !Number.isFinite(height) || width <= 0 || height <= 0) {
+    return null;
+  }
+
+  return { width, height };
+}
+
+function normalizePixelSize(value: string): string | null {
+  const parsed = parsePixelSize(value);
+  if (!parsed) return null;
+  return `${parsed.width}x${parsed.height}`;
+}
+
+function normalizeSizePreset(value: string): string | null {
+  const upper = value.trim().toUpperCase();
+  if (upper === "ADAPTIVE") return "adaptive";
+  if (upper === "1K" || upper === "2K" || upper === "3K" || upper === "4K") return upper;
+  return null;
+}
+
+function normalizeSizeValue(value: string): string | null {
+  return normalizeSizePreset(value) ?? normalizePixelSize(value);
+}
+
+function getMimeType(filename: string): string {
+  const ext = path.extname(filename).toLowerCase();
+  if (ext === ".jpg" || ext === ".jpeg") return "image/jpeg";
+  if (ext === ".webp") return "image/webp";
+  if (ext === ".gif") return "image/gif";
+  if (ext === ".bmp") return "image/bmp";
+  if (ext === ".tiff" || ext === ".tif") return "image/tiff";
+  return "image/png";
+}
+
+async function readImageAsDataUrl(filePath: string): Promise<string> {
+  const bytes = await readFile(filePath);
+  return `data:${getMimeType(filePath)};base64,${bytes.toString("base64")}`;
+}
+
+export function getModelFamily(model: string): SeedreamModelFamily {
+  const normalized = model.trim();
+  if (/^doubao-seedream-5-0(?:-lite)?-\d+$/.test(normalized)) return "seedream5";
+  if (/^doubao-seedream-4-5-\d+$/.test(normalized)) return "seedream45";
+  if (/^doubao-seedream-4-0-\d+$/.test(normalized)) return "seedream40";
+  if (/^doubao-seedream-3-0-t2i-\d+$/.test(normalized)) return "seedream30";
+  return "unknown";
+}
+
+function isRemovedSeededitModel(model: string): boolean {
+  return /^doubao-seededit-3-0-i2i-\d+$/.test(model.trim());
+}
+
+function assertSupportedModel(model: string): void {
+  if (isRemovedSeededitModel(model)) {
+    throw new Error(
+      `${model} is no longer supported. SeedEdit 3.0 support has been removed from this tool; use Seedream 5.0/4.5/4.0/3.0 instead.`
+    );
+  }
+}
+
+export function supportsReferenceImages(model: string): boolean {
+  const family = getModelFamily(model);
+  return family === "seedream5" || family === "seedream45" || family === "seedream40";
+}
+
+function supportsOutputFormat(model: string): boolean {
+  return getModelFamily(model) === "seedream5";
+}
+
+export function getDefaultOutputExtension(model: string): ".png" | ".jpg" {
+  assertSupportedModel(model);
+  return supportsOutputFormat(model) ? ".png" : ".jpg";
+}
+
+export function getDefaultSeedreamSize(model: string, args: CliArgs): string {
+  assertSupportedModel(model);
+  const family = getModelFamily(model);
+
+  if (family === "seedream5") return "2K";
+  if (family === "seedream45") return "2K";
+  if (family === "seedream40") return args.quality === "normal" ? "1K" : "2K";
+  if (family === "seedream30") return args.quality === "2k" ? "2048x2048" : "1024x1024";
+  return "2K";
+}
+
+export function resolveSeedreamSize(model: string, args: CliArgs): string {
+  assertSupportedModel(model);
+  const family = getModelFamily(model);
+  const requested = args.size || args.imageSize || null;
+  const normalized = requested ? normalizeSizeValue(requested) : null;
+
+  if (!normalized) {
+    return getDefaultSeedreamSize(model, args);
+  }
+
+  if (family === "seedream30") {
+    const pixelSize = normalizePixelSize(normalized);
+    if (!pixelSize) {
+      throw new Error("Seedream 3.0 only supports explicit WxH sizes such as 1024x1024.");
+    }
+    return pixelSize;
+  }
+
+  if (family === "seedream5") {
+    if (normalized === "4K" || normalized === "1K" || normalized === "adaptive") {
+      throw new Error("Seedream 5.0 only supports 2K, 3K, or explicit WxH sizes.");
+    }
+    return normalized;
+  }
+
+  if (family === "seedream45") {
+    if (normalized === "1K" || normalized === "3K" || normalized === "adaptive") {
+      throw new Error("Seedream 4.5 only supports 2K, 4K, or explicit WxH sizes.");
+    }
+    return normalized;
+  }
+
+  if (family === "seedream40") {
+    if (normalized === "3K" || normalized === "adaptive") {
+      throw new Error("Seedream 4.0 only supports 1K, 2K, 4K, or explicit WxH sizes.");
+    }
+    return normalized;
+  }
+
+  if (normalized === "adaptive") {
+    throw new Error("Adaptive size is not supported by Seedream image generation.");
+  }
+
+  if (normalized === "1K" || normalized === "3K" || normalized === "4K") {
+    throw new Error(
+      "Unknown Seedream model ID. Use a documented model ID or pass an explicit WxH size instead of preset imageSize."
+    );
+  }
+
+  return normalized;
+}
+
+export function validateArgs(model: string, args: CliArgs): void {
+  assertSupportedModel(model);
+  const family = getModelFamily(model);
+  const refCount = args.referenceImages.length;
+
+  if (refCount === 0) {
+    resolveSeedreamSize(model, args);
+    return;
+  }
+
+  if (family === "unknown") {
+    throw new Error(
+      "Reference images with Seedream require a known model ID. Use Seedream 5.0/4.5/4.0 model IDs instead of an endpoint ID."
+    );
+  }
+
+  if (!supportsReferenceImages(model)) {
+    throw new Error(`${model} does not support reference images.`);
+  }
+
+  if ((family === "seedream5" || family === "seedream45" || family === "seedream40") && refCount > 14) {
+    throw new Error(`${model} supports at most 14 reference images.`);
+  }
+
+  resolveSeedreamSize(model, args);
+}
+
+export async function buildImageInput(
+  model: string,
+  referenceImages: string[],
+): Promise<SeedreamRequestImage | undefined> {
+  if (referenceImages.length === 0) return undefined;
+  assertSupportedModel(model);
+
+  const encoded = await Promise.all(referenceImages.map((refPath) => readImageAsDataUrl(refPath)));
+
+  return encoded.length === 1 ? encoded[0]! : encoded;
+}
+
+export function buildRequestBody(
+  prompt: string,
+  model: string,
+  args: CliArgs,
+  imageInput?: SeedreamRequestImage,
+): SeedreamRequestBody {
+  validateArgs(model, args);
+
+  const requestBody: SeedreamRequestBody = {
+    model,
+    prompt,
+    size: resolveSeedreamSize(model, args),
+    response_format: "url",
+    watermark: false,
+  };
+
+  if (imageInput) {
+    requestBody.image = imageInput;
+  }
+
+  if (supportsOutputFormat(model)) {
+    requestBody.output_format = "png";
+  }
+
+  return requestBody;
+}
+
+async function downloadImage(url: string): Promise<Uint8Array> {
+  const imgResponse = await fetch(url);
+  if (!imgResponse.ok) {
+    throw new Error(`Failed to download image from ${url}`);
+  }
+
+  const buffer = await imgResponse.arrayBuffer();
+  return new Uint8Array(buffer);
+}
+
+export async function extractImageFromResponse(result: SeedreamImageResponse): Promise<Uint8Array> {
+  const first = result.data?.find((item) => item.url || item.b64_json || item.error);
+
+  if (!first) {
+    throw new Error("No image data in Seedream response");
+  }
+
+  if (first.error) {
+    throw new Error(first.error.message || "Seedream returned an image generation error");
+  }
+
+  if (first.b64_json) {
+    return Uint8Array.from(Buffer.from(first.b64_json, "base64"));
+  }
+
+  if (first.url) {
+    console.error(`Downloading image from ${first.url}...`);
+    return downloadImage(first.url);
+  }
+
+  throw new Error("No image URL or base64 data in Seedream response");
+}
+
+export async function generateImage(
+  prompt: string,
+  model: string,
+  args: CliArgs,
+): Promise<Uint8Array> {
+  const apiKey = getApiKey();
+  if (!apiKey) {
+    throw new Error(
+      "ARK_API_KEY is required. " +
+        "Get your API key from https://console.volcengine.com/ark"
+    );
+  }
+
+  validateArgs(model, args);
+  const imageInput = await buildImageInput(model, args.referenceImages);
+  const requestBody = buildRequestBody(prompt, model, args, imageInput);
+
+  console.error(`Calling Seedream API (${model}) with size: ${requestBody.size}`);
+
+  const response = await fetch(`${getBaseUrl()}/images/generations`, {
+    method: "POST",
+    headers: {
+      "Content-Type": "application/json",
+      Authorization: `Bearer ${apiKey}`,
+    },
+    body: JSON.stringify(requestBody),
+  });
+
+  if (!response.ok) {
+    const err = await response.text();
+    throw new Error(`Seedream API error (${response.status}): ${err}`);
+  }
+
+  const result = (await response.json()) as SeedreamImageResponse;
+  if (result.error) {
+    throw new Error(result.error.message || "Seedream API returned an error");
+  }
+
+  return extractImageFromResponse(result);
+}
@@ -0,0 +1,82 @@
+export type Provider =
+  | "google"
+  | "openai"
+  | "openrouter"
+  | "dashscope"
+  | "minimax"
+  | "replicate"
+  | "jimeng"
+  | "seedream"
+  | "azure";
+export type Quality = "normal" | "2k";
+
+export type CliArgs = {
+  prompt: string | null;
+  promptFiles: string[];
+  imagePath: string | null;
+  provider: Provider | null;
+  model: string | null;
+  aspectRatio: string | null;
+  size: string | null;
+  quality: Quality | null;
+  imageSize: string | null;
+  referenceImages: string[];
+  n: number;
+  batchFile: string | null;
+  jobs: number | null;
+  json: boolean;
+  help: boolean;
+};
+
+export type BatchTaskInput = {
+  id?: string;
+  prompt?: string | null;
+  promptFiles?: string[];
+  image?: string;
+  provider?: Provider | null;
+  model?: string | null;
+  ar?: string | null;
+  size?: string | null;
+  quality?: Quality | null;
+  imageSize?: "1K" | "2K" | "4K" | null;
+  ref?: string[];
+  n?: number;
+};
+
+export type BatchFile =
+  | BatchTaskInput[]
+  | {
+      tasks: BatchTaskInput[];
+      jobs?: number | null;
+    };
+
+export type ExtendConfig = {
+  version: number;
+  default_provider: Provider | null;
+  default_quality: Quality | null;
+  default_aspect_ratio: string | null;
+  default_image_size: "1K" | "2K" | "4K" | null;
+  default_model: {
+    google: string | null;
+    openai: string | null;
+    openrouter: string | null;
+    dashscope: string | null;
+    minimax: string | null;
+    replicate: string | null;
+    jimeng: string | null;
+    seedream: string | null;
+    azure: string | null;
+  };
+  batch?: {
+    max_workers?: number | null;
+    provider_limits?: Partial<
+      Record<
+        Provider,
+        {
+          concurrency?: number | null;
+          start_interval_ms?: number | null;
+        }
+      >
+    >;
+  };
+};
@@ -1,6 +1,6 @@
 ---
 name: baoyu-markdown-to-html
-description: Converts Markdown to styled HTML with WeChat-compatible themes. Supports code highlighting, math, PlantUML, footnotes, alerts, infographics, and optional bottom citations for external links. Use when user asks for "markdown to html", "convert md to html", "md转html", "微信外链转底部引用", or needs styled HTML output from markdown.
+description: Converts Markdown to styled HTML with WeChat-compatible themes. Supports code highlighting, math, PlantUML, footnotes, alerts, infographics, and optional bottom citations for external links. Use when user asks for "markdown to html", "convert md to html", "md 转 html", "微信外链转底部引用", or needs styled HTML output from markdown.
 version: 1.56.1
 metadata:
  openclaw:
@@ -0,0 +1,56 @@
+import assert from "node:assert/strict";
+import { execFile } from "node:child_process";
+import fs from "node:fs/promises";
+import os from "node:os";
+import path from "node:path";
+import process from "node:process";
+import test from "node:test";
+import { fileURLToPath } from "node:url";
+import { promisify } from "node:util";
+
+const execFileAsync = promisify(execFile);
+const SCRIPT_DIR = path.dirname(fileURLToPath(import.meta.url));
+const SCRIPT_PATH = path.join(SCRIPT_DIR, "main.ts");
+
+async function makeTempDir(prefix: string): Promise<string> {
+  return fs.mkdtemp(path.join(os.tmpdir(), prefix));
+}
+
+test("CLI forwards wrapper title and vendor render options", async () => {
+  const root = await makeTempDir("baoyu-markdown-to-html-cli-");
+  const markdownPath = path.join(root, "article.md");
+  await fs.writeFile(markdownPath, "## Section\n\nParagraph with **bold** text.\n", "utf-8");
+
+  const { stdout } = await execFileAsync(
+    process.execPath,
+    [
+      "--import",
+      "tsx",
+      SCRIPT_PATH,
+      markdownPath,
+      "--theme", "grace",
+      "--color", "red",
+      "--font-family", "mono",
+      "--font-size", "18",
+      "--keep-title",
+      "--title", "Overridden",
+    ],
+    { cwd: SCRIPT_DIR },
+  );
+
+  const result = JSON.parse(stdout.trim()) as {
+    htmlPath: string;
+    title: string;
+  };
+
+  assert.equal(result.title, "Overridden");
+
+  const html = await fs.readFile(result.htmlPath, "utf-8");
+  assert.match(html, /<title>Overridden<\/title>/);
+  assert.match(html, /<h2[^>]*style="[^"]*background: #A93226/);
+  assert.match(html, /<strong[^>]*style="[^"]*color: #A93226/);
+  assert.match(
+    html,
+    /<body[^>]*style="[^"]*font-family: Menlo, Monaco, 'Courier New', monospace;[^"]*font-size: 18px/,
+  );
+});
@@ -4,16 +4,22 @@ import path from "node:path";
 import process from "node:process";

 import {
+  COLOR_PRESETS,
+  FONT_FAMILY_MAP,
+  FONT_SIZE_OPTIONS,
+  THEME_NAMES,
  extractSummaryFromBody,
  extractTitleFromMarkdown,
  formatTimestamp,
+  parseArgs,
  parseFrontmatter,
  renderMarkdownDocument,
  replaceMarkdownImagesWithPlaceholders,
  resolveContentImages,
  serializeFrontmatter,
  stripWrappingQuotes,
-} from "baoyu-md";
+} from "./vendor/baoyu-md/src/index.ts";
+import type { CliOptions } from "./vendor/baoyu-md/src/types.ts";

 interface ImageInfo {
  placeholder: string;
@@ -30,9 +36,13 @@ interface ParsedResult {
  contentImages: ImageInfo[];
 }

+type ConvertMarkdownOptions = Partial<Omit<CliOptions, "inputPath">> & {
+  title?: string;
+};
+
 export async function convertMarkdown(
  markdownPath: string,
-  options?: { title?: string; theme?: string; keepTitle?: boolean; citeStatus?: boolean },
+  options?: ConvertMarkdownOptions,
 ): Promise<ParsedResult> {
  const baseDir = path.dirname(markdownPath);
  const content = fs.readFileSync(markdownPath, "utf-8");
@@ -56,20 +66,32 @@ export async function convertMarkdown(
    summary = extractSummaryFromBody(body, 120);
  }

+  const effectiveFrontmatter = options?.title
+    ? { ...frontmatter, title }
+    : frontmatter;
+
  const { images, markdown: rewrittenBody } = replaceMarkdownImagesWithPlaceholders(
    body,
    "MDTOHTMLIMGPH_",
  );
-  const rewrittenMarkdown = `${serializeFrontmatter(frontmatter)}${rewrittenBody}`;
+  const rewrittenMarkdown = `${serializeFrontmatter(effectiveFrontmatter)}${rewrittenBody}`;

  console.error(
    `[markdown-to-html] Rendering with theme: ${theme ?? "default"}, keepTitle: ${keepTitle}, citeStatus: ${citeStatus}`,
  );

  const { html } = await renderMarkdownDocument(rewrittenMarkdown, {
+    codeTheme: options?.codeTheme,
+    countStatus: options?.countStatus,
    citeStatus,
    defaultTitle: title,
+    fontFamily: options?.fontFamily,
+    fontSize: options?.fontSize,
+    isMacCodeBlock: options?.isMacCodeBlock,
+    isShowLineNumber: options?.isShowLineNumber,
    keepTitle,
+    legend: options?.legend,
+    primaryColor: options?.primaryColor,
    theme,
  });

@@ -111,18 +133,30 @@ export async function convertMarkdown(
  };
 }

-function printUsage(): never {
+function printUsage(exitCode = 0): never {
+  const colorNames = Object.keys(COLOR_PRESETS).join(", ");
+  const fontFamilyNames = Object.keys(FONT_FAMILY_MAP).join(", ");
+
  console.log(`Convert Markdown to styled HTML

 Usage:
  npx -y bun main.ts <markdown_file> [options]

 Options:
-  --title <title>     Override title
-  --theme <name>      Theme name (default, grace, simple). Default: default
-  --cite              Convert ordinary external links to bottom citations. Default: off
-  --keep-title        Keep the first heading in content. Default: false (removed)
-  --help              Show this help
+  --title <title>         Override title
+  --theme <name>          Theme name (${THEME_NAMES.join(", ")}). Default: default
+  --color <name|hex>      Primary color: ${colorNames}
+  --font-family <name>    Font: ${fontFamilyNames}, or CSS value
+  --font-size <N>         Font size: ${FONT_SIZE_OPTIONS.join(", ")} (default: 16px)
+  --code-theme <name>     Code highlight theme (default: github)
+  --mac-code-block        Show Mac-style code block header
+  --no-mac-code-block     Hide Mac-style code block header
+  --line-number           Show line numbers in code blocks
+  --cite                  Convert ordinary external links to bottom citations. Default: off
+  --count                 Show reading time / word count
+  --legend <value>        Image caption: title-alt, alt-title, title, alt, none
+  --keep-title            Keep the first heading in content. Default: false (removed)
+  --help                  Show this help

 Output:
  HTML file saved to same directory as input markdown file.
@@ -142,40 +176,60 @@ Output JSON format:
 Example:
  npx -y bun main.ts article.md
  npx -y bun main.ts article.md --theme grace
+  npx -y bun main.ts article.md --theme modern --color red
  npx -y bun main.ts article.md --cite
 `);
-  process.exit(0);
+  process.exit(exitCode);
+}
+
+function parseArgValue(argv: string[], i: number, flag: string): string | null {
+  const arg = argv[i]!;
+  if (arg.includes("=")) {
+    return arg.slice(flag.length + 1);
+  }
+  const next = argv[i + 1];
+  return next ?? null;
+}
+
+function extractTitleArg(argv: string[]): { renderArgs: string[]; title?: string } {
+  let title: string | undefined;
+  const renderArgs: string[] = [];
+
+  for (let i = 0; i < argv.length; i += 1) {
+    const arg = argv[i]!;
+    if (arg === "--title" || arg.startsWith("--title=")) {
+      const value = parseArgValue(argv, i, "--title");
+      if (!value) {
+        console.error("Missing value for --title");
+        printUsage(1);
+      }
+      title = value;
+      if (!arg.includes("=")) {
+        i += 1;
+      }
+      continue;
+    }
+    renderArgs.push(arg);
+  }
+
+  return { renderArgs, title };
 }

 async function main(): Promise<void> {
  const args = process.argv.slice(2);
  if (args.length === 0 || args.includes("--help") || args.includes("-h")) {
-    printUsage();
+    printUsage(0);
  }

-  let markdownPath: string | undefined;
-  let title: string | undefined;
-  let theme: string | undefined;
-  let citeStatus = false;
-  let keepTitle = false;
-
-  for (let i = 0; i < args.length; i++) {
-    const arg = args[i]!;
-    if (arg === "--title" && args[i + 1]) {
-      title = args[++i];
-    } else if (arg === "--theme" && args[i + 1]) {
-      theme = args[++i];
-    } else if (arg === "--cite") {
-      citeStatus = true;
-    } else if (arg === "--keep-title") {
-      keepTitle = true;
-    } else if (!arg.startsWith("-")) {
-      markdownPath = arg;
-    }
+  const { renderArgs, title } = extractTitleArg(args);
+  const options = parseArgs(renderArgs);
+  if (!options) {
+    printUsage(1);
  }

-  if (!markdownPath) {
-    console.error("Error: Markdown file path is required");
+  const markdownPath = path.resolve(process.cwd(), options.inputPath);
+  if (!markdownPath.toLowerCase().endsWith(".md")) {
+    console.error("Input file must end with .md");
    process.exit(1);
  }

@@ -184,7 +238,7 @@ async function main(): Promise<void> {
    process.exit(1);
  }

-  const result = await convertMarkdown(markdownPath, { title, theme, keepTitle, citeStatus });
+  const result = await convertMarkdown(markdownPath, { ...options, title });
  console.log(JSON.stringify(result, null, 2));
 }

@@ -9,12 +9,26 @@ import { COLOR_PRESETS, FONT_FAMILY_MAP } from "./constants.ts";
 import {
  buildMarkdownDocumentMeta,
  formatTimestamp,
+  renderMarkdownDocument,
  resolveColorToken,
  resolveFontFamilyToken,
  resolveMarkdownStyle,
  resolveRenderOptions,
 } from "./document.ts";

+function escapeRegExp(value: string): string {
+  return value.replace(/[.*+?^${}()|[\]\\]/g, `\\$&`);
+}
+
+function findInlineStyle(html: string, tagName: string, text: string): string {
+  const pattern = new RegExp(
+    `<${tagName}[^>]*style="([^"]*)"[^>]*>${escapeRegExp(text)}</${tagName}>`,
+  );
+  const match = html.match(pattern);
+  assert.ok(match, `Expected inline style for <${tagName}>${text}</${tagName}>`);
+  return match![1]!;
+}
+
 function useCwd(t: TestContext, cwd: string): void {
  const previous = process.cwd();
  process.chdir(cwd);
@@ -138,3 +152,23 @@ keep_title: true
  assert.equal(explicit.fontSize, "18px");
  assert.equal(explicit.keepTitle, false);
 });
+
+test("renderMarkdownDocument layers default rules into grace theme before CSS inlining", async () => {
+  const { html } = await renderMarkdownDocument(
+    `## Section\n\nParagraph with **bold** text.`,
+    { keepTitle: true, theme: "grace" },
+  );
+
+  const h2Style = findInlineStyle(html, "h2", "Section");
+  assert.match(h2Style, /background: #92617E/);
+  assert.match(h2Style, /box-shadow: 0 4px 6px rgba\(0, 0, 0, 0\.1\)/);
+
+  const pMatch = html.match(/<p[^>]*style="([^"]*)"[^>]*>/);
+  assert.ok(pMatch, "Expected inline style on <p> tag");
+  assert.match(pMatch![1]!, /color:/);
+
+  const strongPattern = /<strong[^>]*style="([^"]*)"[^>]*>bold<\/strong>/;
+  const strongMatch = html.match(strongPattern);
+  assert.ok(strongMatch, "Expected inline style for <strong>bold</strong>");
+  assert.match(strongMatch![1]!, /font-weight:/);
+});
@@ -59,6 +59,17 @@ test("normalizeCssText and normalizeInlineCss replace variables and strip declar
  assert.doesNotMatch(normalizedHtml, /var\(--md-primary-color\)/);
 });

+test("normalizeInlineCss removes quoted custom property values without leaving fragments behind", () => {
+  const normalizedHtml = normalizeInlineCss(
+    `<html style="--md-font-family: Menlo, Monaco, 'Courier New', monospace; color: var(--md-primary-color)"></html>`,
+    DEFAULT_STYLE,
+  );
+
+  assert.match(normalizedHtml, /style=" color: #0F4C81"/);
+  assert.doesNotMatch(normalizedHtml, /Courier New/);
+  assert.doesNotMatch(normalizedHtml, /--md-font-family/);
+});
+
 test("HTML structure helpers hoist nested lists and remove the first heading", () => {
  const nestedList = `<ul><li>Parent<ul><li>Child</li></ul></li></ul>`;
  assert.equal(
@@ -100,13 +100,13 @@ export function normalizeCssText(cssText: string, style: StyleConfig = DEFAULT_S
    .replace(/var\(--md-accent-color\)/g, style.accentColor)
    .replace(/var\(--md-container-bg\)/g, style.containerBg)
    .replace(/hsl\(var\(--foreground\)\)/g, "#3f3f3f")
-    .replace(/--md-primary-color:\s*[^;"']+;?/g, "")
-    .replace(/--md-font-family:\s*[^;"']+;?/g, "")
-    .replace(/--md-font-size:\s*[^;"']+;?/g, "")
-    .replace(/--blockquote-background:\s*[^;"']+;?/g, "")
-    .replace(/--md-accent-color:\s*[^;"']+;?/g, "")
-    .replace(/--md-container-bg:\s*[^;"']+;?/g, "")
-    .replace(/--foreground:\s*[^;"']+;?/g, "");
+    .replace(/--md-primary-color:\s*[^;]+;?/g, "")
+    .replace(/--md-font-family:\s*[^;]+;?/g, "")
+    .replace(/--md-font-size:\s*[^;]+;?/g, "")
+    .replace(/--blockquote-background:\s*[^;]+;?/g, "")
+    .replace(/--md-accent-color:\s*[^;]+;?/g, "")
+    .replace(/--md-container-bg:\s*[^;]+;?/g, "")
+    .replace(/--foreground:\s*[^;]+;?/g, "");
 }

 export function normalizeInlineCss(html: string, style: StyleConfig = DEFAULT_STYLE): string {
@@ -6,6 +6,7 @@ import type { ThemeName } from "./types.js";
 const SCRIPT_DIR = path.dirname(fileURLToPath(import.meta.url));
 export const THEME_DIR = path.resolve(SCRIPT_DIR, "themes");
 const FALLBACK_THEMES: ThemeName[] = ["default", "grace", "simple"];
+const THEMES_EXTENDING_DEFAULT = new Set<ThemeName>(["grace", "simple"]);

 function stripOutputScope(cssContent: string): string {
  let css = cssContent;
@@ -41,6 +42,7 @@ export function loadThemeCss(theme: ThemeName): {
  themeCss: string;
 } {
  const basePath = path.join(THEME_DIR, "base.css");
+  const defaultThemePath = path.join(THEME_DIR, "default.css");
  const themePath = path.join(THEME_DIR, `${theme}.css`);

  if (!fs.existsSync(basePath)) {
@@ -51,9 +53,18 @@ export function loadThemeCss(theme: ThemeName): {
    throw new Error(`Missing theme CSS for "${theme}": ${themePath}`);
  }

+  const layeredThemeCss: string[] = [];
+  if (theme !== "default" && THEMES_EXTENDING_DEFAULT.has(theme)) {
+    if (!fs.existsSync(defaultThemePath)) {
+      throw new Error(`Missing default theme CSS: ${defaultThemePath}`);
+    }
+    layeredThemeCss.push(fs.readFileSync(defaultThemePath, "utf-8"));
+  }
+  layeredThemeCss.push(fs.readFileSync(themePath, "utf-8"));
+
  return {
    baseCss: fs.readFileSync(basePath, "utf-8"),
-    themeCss: fs.readFileSync(themePath, "utf-8"),
+    themeCss: layeredThemeCss.join("\n"),
  };
 }

@@ -4,32 +4,108 @@
    "": {
      "name": "baoyu-post-to-wechat-scripts",
      "dependencies": {
+        "@jsquash/webp": "^1.5.0",
        "baoyu-chrome-cdp": "file:./vendor/baoyu-chrome-cdp",
        "baoyu-md": "file:./vendor/baoyu-md",
+        "jimp": "^1.6.0",
      },
    },
  },
  "packages": {
+    "@jimp/core": ["@jimp/core@1.6.0", "", { "dependencies": { "@jimp/file-ops": "1.6.0", "@jimp/types": "1.6.0", "@jimp/utils": "1.6.0", "await-to-js": "^3.0.0", "exif-parser": "^0.1.12", "file-type": "^16.0.0", "mime": "3" } }, "sha512-EQQlKU3s9QfdJqiSrZWNTxBs3rKXgO2W+GxNXDtwchF3a4IqxDheFX1ti+Env9hdJXDiYLp2jTRjlxhPthsk8w=="],
+
+    "@jimp/diff": ["@jimp/diff@1.6.0", "", { "dependencies": { "@jimp/plugin-resize": "1.6.0", "@jimp/types": "1.6.0", "@jimp/utils": "1.6.0", "pixelmatch": "^5.3.0" } }, "sha512-+yUAQ5gvRC5D1WHYxjBHZI7JBRusGGSLf8AmPRPCenTzh4PA+wZ1xv2+cYqQwTfQHU5tXYOhA0xDytfHUf1Zyw=="],
+
+    "@jimp/file-ops": ["@jimp/file-ops@1.6.0", "", {}, "sha512-Dx/bVDmgnRe1AlniRpCKrGRm5YvGmUwbDzt+MAkgmLGf+jvBT75hmMEZ003n9HQI/aPnm/YKnXjg/hOpzNCpHQ=="],
+
+    "@jimp/js-bmp": ["@jimp/js-bmp@1.6.0", "", { "dependencies": { "@jimp/core": "1.6.0", "@jimp/types": "1.6.0", "@jimp/utils": "1.6.0", "bmp-ts": "^1.0.9" } }, "sha512-FU6Q5PC/e3yzLyBDXupR3SnL3htU7S3KEs4e6rjDP6gNEOXRFsWs6YD3hXuXd50jd8ummy+q2WSwuGkr8wi+Gw=="],
+
+    "@jimp/js-gif": ["@jimp/js-gif@1.6.0", "", { "dependencies": { "@jimp/core": "1.6.0", "@jimp/types": "1.6.0", "gifwrap": "^0.10.1", "omggif": "^1.0.10" } }, "sha512-N9CZPHOrJTsAUoWkWZstLPpwT5AwJ0wge+47+ix3++SdSL/H2QzyMqxbcDYNFe4MoI5MIhATfb0/dl/wmX221g=="],
+
+    "@jimp/js-jpeg": ["@jimp/js-jpeg@1.6.0", "", { "dependencies": { "@jimp/core": "1.6.0", "@jimp/types": "1.6.0", "jpeg-js": "^0.4.4" } }, "sha512-6vgFDqeusblf5Pok6B2DUiMXplH8RhIKAryj1yn+007SIAQ0khM1Uptxmpku/0MfbClx2r7pnJv9gWpAEJdMVA=="],
+
+    "@jimp/js-png": ["@jimp/js-png@1.6.0", "", { "dependencies": { "@jimp/core": "1.6.0", "@jimp/types": "1.6.0", "pngjs": "^7.0.0" } }, "sha512-AbQHScy3hDDgMRNfG0tPjL88AV6qKAILGReIa3ATpW5QFjBKpisvUaOqhzJ7Reic1oawx3Riyv152gaPfqsBVg=="],
+
+    "@jimp/js-tiff": ["@jimp/js-tiff@1.6.0", "", { "dependencies": { "@jimp/core": "1.6.0", "@jimp/types": "1.6.0", "utif2": "^4.1.0" } }, "sha512-zhReR8/7KO+adijj3h0ZQUOiun3mXUv79zYEAKvE0O+rP7EhgtKvWJOZfRzdZSNv0Pu1rKtgM72qgtwe2tFvyw=="],
+
+    "@jimp/plugin-blit": ["@jimp/plugin-blit@1.6.0", "", { "dependencies": { "@jimp/types": "1.6.0", "@jimp/utils": "1.6.0", "zod": "^3.23.8" } }, "sha512-M+uRWl1csi7qilnSK8uxK4RJMSuVeBiO1AY0+7APnfUbQNZm6hCe0CCFv1Iyw1D/Dhb8ph8fQgm5mwM0eSxgVA=="],
+
+    "@jimp/plugin-blur": ["@jimp/plugin-blur@1.6.0", "", { "dependencies": { "@jimp/core": "1.6.0", "@jimp/utils": "1.6.0" } }, "sha512-zrM7iic1OTwUCb0g/rN5y+UnmdEsT3IfuCXCJJNs8SZzP0MkZ1eTvuwK9ZidCuMo4+J3xkzCidRwYXB5CyGZTw=="],
+
+    "@jimp/plugin-circle": ["@jimp/plugin-circle@1.6.0", "", { "dependencies": { "@jimp/types": "1.6.0", "zod": "^3.23.8" } }, "sha512-xt1Gp+LtdMKAXfDp3HNaG30SPZW6AQ7dtAtTnoRKorRi+5yCJjKqXRgkewS5bvj8DEh87Ko1ydJfzqS3P2tdWw=="],
+
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    "baoyu-chrome-cdp": ["baoyu-chrome-cdp@file:vendor/baoyu-chrome-cdp", {}],

    "baoyu-md": ["baoyu-md@file:vendor/baoyu-md", { "dependencies": { "fflate": "^0.8.2", "front-matter": "^4.0.2", "highlight.js": "^11.11.1", "juice": "^11.0.1", "marked": "^15.0.6", "reading-time": "^1.5.0", "remark-cjk-friendly": "^1.1.0", "remark-parse": "^11.0.0", "remark-stringify": "^11.0.0", "unified": "^11.0.5" } }],

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@@ -66,22 +142,40 @@

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+
+    "exif-parser": ["exif-parser@0.1.12", "", {}, "sha512-c2bQfLNbMzLPmzQuOr8fy0csy84WmwnER81W88DzTp9CYNPJ6yzOj2EZAh9pywYpqHnshVLHQJ8WzldAyfY+Iw=="],
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    "fflate": ["fflate@0.8.2", "", {}, "sha512-cPJU47OaAoCbg0pBvzsgpTPhmhqI5eJjh/JIu8tPj5q+T7iLvW/JAYUqmE7KOB4R1ZyEhzBaIQpQpardBF5z8A=="],

+    "file-type": ["file-type@16.5.4", "", { "dependencies": { "readable-web-to-node-stream": "^3.0.0", "strtok3": "^6.2.4", "token-types": "^4.1.1" } }, "sha512-/yFHK0aGjFEgDJjEKP0pWCplsPFPhwyfwevf/pVxiN0tmE4L9LmwWxWukdJSHdoCli4VgQLehjJtwQBnqmsKcw=="],
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    "get-east-asian-width": ["get-east-asian-width@1.5.0", "", {}, "sha512-CQ+bEO+Tva/qlmw24dCejulK5pMzVnUOFOijVogd3KQs07HnRIgp8TGipvCCRT06xeYEbpbgwaCxglFyiuIcmA=="],

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+
    "js-yaml": ["js-yaml@3.14.2", "", { "dependencies": { "argparse": "^1.0.7", "esprima": "^4.0.0" }, "bin": { "js-yaml": "bin/js-yaml.js" } }, "sha512-PMSmkqxr106Xa156c2M265Z+FTrPl+oxd/rgOQy2tijQeK5TxQ43psO1ZCwhVOSdnn+RzkzlRz/eY4BgJBYVpg=="],

    "juice": ["juice@11.1.1", "", { "dependencies": { "cheerio": "1.0.0", "commander": "^12.1.0", "entities": "^7.0.0", "mensch": "^0.3.4", "slick": "^1.12.2", "web-resource-inliner": "^8.0.0" }, "bin": { "juice": "bin/juice" } }, "sha512-4SBfZqKcc6DrIS+5b/WiGoWaZsdUPBH+e6SbRlNjJpaIRtfoBhYReAtobIEW6mcLeFFDXLBJMuZwkJLkBJjs2w=="],
@@ -146,18 +240,40 @@

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-    "mime": ["mime@2.6.0", "", { "bin": { "mime": "cli.js" } }, "sha512-USPkMeET31rOMiarsBNIHZKLGgvKc/LrjofAnBlOttf5ajRvqiRA8QsenbcooctK6d6Ts6aqZXBA+XbkKthiQg=="],
+    "mime": ["mime@3.0.0", "", { "bin": { "mime": "cli.js" } }, "sha512-jSCU7/VB1loIWBZe14aEYHU/+1UMEHoaO7qxCOVJOw9GgH72VAWppxNcjU+x9a2k3GSIBXNKxXQFqRvvZ7vr3A=="],

    "ms": ["ms@2.1.3", "", {}, "sha512-6FlzubTLZG3J2a/NVCAleEhjzq5oxgHyaCU9yYXvcLsvoVaHJq/s5xXI6/XXP6tz7R9xAOtHnSO/tXtF3WRTlA=="],

    "nth-check": ["nth-check@2.1.1", "", { "dependencies": { "boolbase": "^1.0.0" } }, "sha512-lqjrjmaOoAnWfMmBPL+XNnynZh2+swxiX3WUE0s4yEHI6m+AwrK2UZOimIRl3X/4QctVqS8AiZjFqyOGrMXb/w=="],

+    "omggif": ["omggif@1.0.10", "", {}, "sha512-LMJTtvgc/nugXj0Vcrrs68Mn2D1r0zf630VNtqtpI1FEO7e+O9FP4gqs9AcnBaSEeoHIPm28u6qgPR0oyEpGSw=="],
+
+    "pako": ["pako@1.0.11", "", {}, "sha512-4hLB8Py4zZce5s4yd9XzopqwVv/yGNhV1Bl8NTmCq1763HeK2+EwVTv+leGeL13Dnh2wfbqowVPXCIO0z4taYw=="],
+
+    "parse-bmfont-ascii": ["parse-bmfont-ascii@1.0.6", "", {}, "sha512-U4RrVsUFCleIOBsIGYOMKjn9PavsGOXxbvYGtMOEfnId0SVNsgehXh1DxUdVPLoxd5mvcEtvmKs2Mmf0Mpa1ZA=="],
+
+    "parse-bmfont-binary": ["parse-bmfont-binary@1.0.6", "", {}, "sha512-GxmsRea0wdGdYthjuUeWTMWPqm2+FAd4GI8vCvhgJsFnoGhTrLhXDDupwTo7rXVAgaLIGoVHDZS9p/5XbSqeWA=="],
+
+    "parse-bmfont-xml": ["parse-bmfont-xml@1.1.6", "", { "dependencies": { "xml-parse-from-string": "^1.0.0", "xml2js": "^0.5.0" } }, "sha512-0cEliVMZEhrFDwMh4SxIyVJpqYoOWDJ9P895tFuS+XuNzI5UBmBk5U5O4KuJdTnZpSBI4LFA2+ZiJaiwfSwlMA=="],
+
    "parse5": ["parse5@7.3.0", "", { "dependencies": { "entities": "^6.0.0" } }, "sha512-IInvU7fabl34qmi9gY8XOVxhYyMyuH2xUNpb2q8/Y+7552KlejkRvqvD19nMoUW/uQGGbqNpA6Tufu5FL5BZgw=="],

    "parse5-htmlparser2-tree-adapter": ["parse5-htmlparser2-tree-adapter@7.1.0", "", { "dependencies": { "domhandler": "^5.0.3", "parse5": "^7.0.0" } }, "sha512-ruw5xyKs6lrpo9x9rCZqZZnIUntICjQAd0Wsmp396Ul9lN/h+ifgVV1x1gZHi8euej6wTfpqX8j+BFQxF0NS/g=="],

    "parse5-parser-stream": ["parse5-parser-stream@7.1.2", "", { "dependencies": { "parse5": "^7.0.0" } }, "sha512-JyeQc9iwFLn5TbvvqACIF/VXG6abODeB3Fwmv/TGdLk2LfbWkaySGY72at4+Ty7EkPZj854u4CrICqNk2qIbow=="],

+    "peek-readable": ["peek-readable@4.1.0", "", {}, "sha512-ZI3LnwUv5nOGbQzD9c2iDG6toheuXSZP5esSHBjopsXH4dg19soufvpUGA3uohi5anFtGb2lhAVdHzH6R/Evvg=="],
+
+    "pixelmatch": ["pixelmatch@5.3.0", "", { "dependencies": { "pngjs": "^6.0.0" }, "bin": { "pixelmatch": "bin/pixelmatch" } }, "sha512-o8mkY4E/+LNUf6LzX96ht6k6CEDi65k9G2rjMtBe9Oo+VPKSvl+0GKHuH/AlG+GA5LPG/i5hrekkxUc3s2HU+Q=="],
+
+    "pngjs": ["pngjs@7.0.0", "", {}, "sha512-LKWqWJRhstyYo9pGvgor/ivk2w94eSjE3RGVuzLGlr3NmD8bf7RcYGze1mNdEHRP6TRP6rMuDHk5t44hnTRyow=="],
+
+    "process": ["process@0.11.10", "", {}, "sha512-cdGef/drWFoydD1JsMzuFf8100nZl+GT+yacc2bEced5f9Rjk4z+WtFUTBu9PhOi9j/jfmBPu0mMEY4wIdAF8A=="],
+
+    "readable-stream": ["readable-stream@4.7.0", "", { "dependencies": { "abort-controller": "^3.0.0", "buffer": "^6.0.3", "events": "^3.3.0", "process": "^0.11.10", "string_decoder": "^1.3.0" } }, "sha512-oIGGmcpTLwPga8Bn6/Z75SVaH1z5dUut2ibSyAMVhmUggWpmDn2dapB0n7f8nwaSiRtepAsfJyfXIO5DCVAODg=="],
+
+    "readable-web-to-node-stream": ["readable-web-to-node-stream@3.0.4", "", { "dependencies": { "readable-stream": "^4.7.0" } }, "sha512-9nX56alTf5bwXQ3ZDipHJhusu9NTQJ/CVPtb/XHAJCXihZeitfJvIRS4GqQ/mfIoOE3IelHMrpayVrosdHBuLw=="],
+
    "reading-time": ["reading-time@1.5.0", "", {}, "sha512-onYyVhBNr4CmAxFsKS7bz+uTLRakypIe4R+5A824vBSkQy/hB3fZepoVEf8OVAxzLvK+H/jm9TzpI3ETSm64Kg=="],

    "remark-cjk-friendly": ["remark-cjk-friendly@1.2.3", "", { "dependencies": { "micromark-extension-cjk-friendly": "1.2.3" }, "peerDependencies": { "@types/mdast": "^4.0.0", "unified": "^11.0.0" }, "optionalPeers": ["@types/mdast"] }, "sha512-UvAgxwlNk+l9Oqgl/9MWK2eWRS7zgBW/nXX9AthV7nd/3lNejF138E7Xbmk9Zs4WjTJGs721r7fAEc7tNFoH7g=="],
@@ -166,12 +282,26 @@

    "remark-stringify": ["remark-stringify@11.0.0", "", { "dependencies": { "@types/mdast": "^4.0.0", "mdast-util-to-markdown": "^2.0.0", "unified": "^11.0.0" } }, "sha512-1OSmLd3awB/t8qdoEOMazZkNsfVTeY4fTsgzcQFdXNq8ToTN4ZGwrMnlda4K6smTFKD+GRV6O48i6Z4iKgPPpw=="],

+    "safe-buffer": ["safe-buffer@5.2.1", "", {}, "sha512-rp3So07KcdmmKbGvgaNxQSJr7bGVSVk5S9Eq1F+ppbRo70+YeaDxkw5Dd8NPN+GD6bjnYm2VuPuCXmpuYvmCXQ=="],
+
    "safer-buffer": ["safer-buffer@2.1.2", "", {}, "sha512-YZo3K82SD7Riyi0E1EQPojLz7kpepnSQI9IyPbHHg1XXXevb5dJI7tpyN2ADxGcQbHG7vcyRHk0cbwqcQriUtg=="],

+    "sax": ["sax@1.6.0", "", {}, "sha512-6R3J5M4AcbtLUdZmRv2SygeVaM7IhrLXu9BmnOGmmACak8fiUtOsYNWUS4uK7upbmHIBbLBeFeI//477BKLBzA=="],
+
+    "simple-xml-to-json": ["simple-xml-to-json@1.2.4", "", {}, "sha512-3MY16e0ocMHL7N1ufpdObURGyX+lCo0T/A+y6VCwosLdH1HSda4QZl1Sdt/O+2qWp48WFi26XEp5rF0LoaL0Dg=="],
+
    "slick": ["slick@1.12.2", "", {}, "sha512-4qdtOGcBjral6YIBCWJ0ljFSKNLz9KkhbWtuGvUyRowl1kxfuE1x/Z/aJcaiilpb3do9bl5K7/1h9XC5wWpY/A=="],

    "sprintf-js": ["sprintf-js@1.0.3", "", {}, "sha512-D9cPgkvLlV3t3IzL0D0YLvGA9Ahk4PcvVwUbN0dSGr1aP0Nrt4AEnTUbuGvquEC0mA64Gqt1fzirlRs5ibXx8g=="],

+    "string_decoder": ["string_decoder@1.3.0", "", { "dependencies": { "safe-buffer": "~5.2.0" } }, "sha512-hkRX8U1WjJFd8LsDJ2yQ/wWWxaopEsABU1XfkM8A+j0+85JAGppt16cr1Whg6KIbb4okU6Mql6BOj+uup/wKeA=="],
+
+    "strtok3": ["strtok3@6.3.0", "", { "dependencies": { "@tokenizer/token": "^0.3.0", "peek-readable": "^4.1.0" } }, "sha512-fZtbhtvI9I48xDSywd/somNqgUHl2L2cstmXCCif0itOf96jeW18MBSyrLuNicYQVkvpOxkZtkzujiTJ9LW5Jw=="],
+
+    "tinycolor2": ["tinycolor2@1.6.0", "", {}, "sha512-XPaBkWQJdsf3pLKJV9p4qN/S+fm2Oj8AIPo1BTUhg5oxkvm9+SVEGFdhyOz7tTdUTfvxMiAs4sp6/eZO2Ew+pw=="],
+
+    "token-types": ["token-types@4.2.1", "", { "dependencies": { "@tokenizer/token": "^0.3.0", "ieee754": "^1.2.1" } }, "sha512-6udB24Q737UD/SDsKAHI9FCRP7Bqc9D/MQUV02ORQg5iskjtLJlZJNdN4kKtcdtwCeWIwIHDGaUsTsCCAa8sFQ=="],
+
    "trough": ["trough@2.2.0", "", {}, "sha512-tmMpK00BjZiUyVyvrBK7knerNgmgvcV/KLVyuma/SC+TQN167GrMRciANTz09+k3zW8L8t60jWO1GpfkZdjTaw=="],

    "undici": ["undici@6.24.0", "", {}, "sha512-lVLNosgqo5EkGqh5XUDhGfsMSoO8K0BAN0TyJLvwNRSl4xWGZlCVYsAIpa/OpA3TvmnM01GWcoKmc3ZWo5wKKA=="],
@@ -186,18 +316,30 @@

    "unist-util-visit-parents": ["unist-util-visit-parents@6.0.2", "", { "dependencies": { "@types/unist": "^3.0.0", "unist-util-is": "^6.0.0" } }, "sha512-goh1s1TBrqSqukSc8wrjwWhL0hiJxgA8m4kFxGlQ+8FYQ3C/m11FcTs4YYem7V664AhHVvgoQLk890Ssdsr2IQ=="],

+    "utif2": ["utif2@4.1.0", "", { "dependencies": { "pako": "^1.0.11" } }, "sha512-+oknB9FHrJ7oW7A2WZYajOcv4FcDR4CfoGB0dPNfxbi4GO05RRnFmt5oa23+9w32EanrYcSJWspUiJkLMs+37w=="],
+
    "valid-data-url": ["valid-data-url@3.0.1", "", {}, "sha512-jOWVmzVceKlVVdwjNSenT4PbGghU0SBIizAev8ofZVgivk/TVHXSbNL8LP6M3spZvkR9/QolkyJavGSX5Cs0UA=="],

    "vfile": ["vfile@6.0.3", "", { "dependencies": { "@types/unist": "^3.0.0", "vfile-message": "^4.0.0" } }, "sha512-KzIbH/9tXat2u30jf+smMwFCsno4wHVdNmzFyL+T/L3UGqqk6JKfVqOFOZEpZSHADH1k40ab6NUIXZq422ov3Q=="],

    "vfile-message": ["vfile-message@4.0.3", "", { "dependencies": { "@types/unist": "^3.0.0", "unist-util-stringify-position": "^4.0.0" } }, "sha512-QTHzsGd1EhbZs4AsQ20JX1rC3cOlt/IWJruk893DfLRr57lcnOeMaWG4K0JrRta4mIJZKth2Au3mM3u03/JWKw=="],

+    "wasm-feature-detect": ["wasm-feature-detect@1.8.0", "", {}, "sha512-zksaLKM2fVlnB5jQQDqKXXwYHLQUVH9es+5TOOHwGOVJOCeRBCiPjwSg+3tN2AdTCzjgli4jijCH290kXb/zWQ=="],
+
    "web-resource-inliner": ["web-resource-inliner@8.0.0", "", { "dependencies": { "ansi-colors": "^4.1.1", "escape-goat": "^3.0.0", "htmlparser2": "^9.1.0", "mime": "^2.4.6", "valid-data-url": "^3.0.0" } }, "sha512-Ezr98sqXW/+OCGoUEXuOKVR+oVFlSdn1tIySEEJdiSAw4IjrW8hQkwARSSBJTSB5Us5dnytDgL0ZDliAYBhaNA=="],

    "whatwg-encoding": ["whatwg-encoding@3.1.1", "", { "dependencies": { "iconv-lite": "0.6.3" } }, "sha512-6qN4hJdMwfYBtE3YBTTHhoeuUrDBPZmbQaxWAqSALV/MeEnR5z1xd8UKud2RAkFoPkmB+hli1TZSnyi84xz1vQ=="],

    "whatwg-mimetype": ["whatwg-mimetype@4.0.0", "", {}, "sha512-QaKxh0eNIi2mE9p2vEdzfagOKHCcj1pJ56EEHGQOVxp8r9/iszLUUV7v89x9O1p/T+NlTM5W7jW6+cz4Fq1YVg=="],

+    "xml-parse-from-string": ["xml-parse-from-string@1.0.1", "", {}, "sha512-ErcKwJTF54uRzzNMXq2X5sMIy88zJvfN2DmdoQvy7PAFJ+tPRU6ydWuOKNMyfmOjdyBQTFREi60s0Y0SyI0G0g=="],
+
+    "xml2js": ["xml2js@0.5.0", "", { "dependencies": { "sax": ">=0.6.0", "xmlbuilder": "~11.0.0" } }, "sha512-drPFnkQJik/O+uPKpqSgr22mpuFHqKdbS835iAQrUC73L2F5WkboIRd63ai/2Yg6I1jzifPFKH2NTK+cfglkIA=="],
+
+    "xmlbuilder": ["xmlbuilder@11.0.1", "", {}, "sha512-fDlsI/kFEx7gLvbecc0/ohLG50fugQp8ryHzMTuW9vSa1GJ0XYWKnhsUx7oie3G98+r56aTQIUB4kht42R3JvA=="],
+
+    "zod": ["zod@3.25.76", "", {}, "sha512-gzUt/qt81nXsFGKIFcC3YnfEAx5NkunCfnDlvuBSSFS02bcXu4Lmea0AFIUwbLWxWPx3d9p8S5QoaujKcNQxcQ=="],
+
    "zwitch": ["zwitch@2.0.4", "", {}, "sha512-bXE4cR/kVZhKZX/RjPEflHaKVhUVl85noU3v6b8apfQEc1x4A+zBxjZ4lN8LqGd6WZ3dl98pY4o717VFmoPp+A=="],

    "dom-serializer/entities": ["entities@4.5.0", "", {}, "sha512-V0hjH4dGPh9Ao5p0MoRY6BVqtwCjhz6vI5LT8AJ55H+4g9/4vbHx1I54fS0XuclLhDHArPQCiMjDxjaL8fPxhw=="],
@@ -205,5 +347,9 @@
    "htmlparser2/entities": ["entities@4.5.0", "", {}, "sha512-V0hjH4dGPh9Ao5p0MoRY6BVqtwCjhz6vI5LT8AJ55H+4g9/4vbHx1I54fS0XuclLhDHArPQCiMjDxjaL8fPxhw=="],

    "parse5/entities": ["entities@6.0.1", "", {}, "sha512-aN97NXWF6AWBTahfVOIrB/NShkzi5H7F9r1s9mD3cDj4Ko5f2qhhVoYMibXF7GlLveb/D2ioWay8lxI97Ven3g=="],
+
+    "pixelmatch/pngjs": ["pngjs@6.0.0", "", {}, "sha512-TRzzuFRRmEoSW/p1KVAmiOgPco2Irlah+bGFCeNfJXxxYGwSw7YwAOAcd7X28K/m5bjBWKsC29KyoMfHbypayg=="],
+
+    "web-resource-inliner/mime": ["mime@2.6.0", "", { "bin": { "mime": "cli.js" } }, "sha512-USPkMeET31rOMiarsBNIHZKLGgvKc/LrjofAnBlOttf5ajRvqiRA8QsenbcooctK6d6Ts6aqZXBA+XbkKthiQg=="],
  }
 }
@@ -3,7 +3,9 @@
  "private": true,
  "type": "module",
  "dependencies": {
+    "@jsquash/webp": "^1.5.0",
    "baoyu-chrome-cdp": "file:./vendor/baoyu-chrome-cdp",
-    "baoyu-md": "file:./vendor/baoyu-md"
+    "baoyu-md": "file:./vendor/baoyu-md",
+    "jimp": "^1.6.0"
  }
 }
@@ -9,12 +9,26 @@ import { COLOR_PRESETS, FONT_FAMILY_MAP } from "./constants.ts";
 import {
  buildMarkdownDocumentMeta,
  formatTimestamp,
+  renderMarkdownDocument,
  resolveColorToken,
  resolveFontFamilyToken,
  resolveMarkdownStyle,
  resolveRenderOptions,
 } from "./document.ts";

+function escapeRegExp(value: string): string {
+  return value.replace(/[.*+?^${}()|[\]\\]/g, `\\$&`);
+}
+
+function findInlineStyle(html: string, tagName: string, text: string): string {
+  const pattern = new RegExp(
+    `<${tagName}[^>]*style="([^"]*)"[^>]*>${escapeRegExp(text)}</${tagName}>`,
+  );
+  const match = html.match(pattern);
+  assert.ok(match, `Expected inline style for <${tagName}>${text}</${tagName}>`);
+  return match![1]!;
+}
+
 function useCwd(t: TestContext, cwd: string): void {
  const previous = process.cwd();
  process.chdir(cwd);
@@ -138,3 +152,23 @@ keep_title: true
  assert.equal(explicit.fontSize, "18px");
  assert.equal(explicit.keepTitle, false);
 });
+
+test("renderMarkdownDocument layers default rules into grace theme before CSS inlining", async () => {
+  const { html } = await renderMarkdownDocument(
+    `## Section\n\nParagraph with **bold** text.`,
+    { keepTitle: true, theme: "grace" },
+  );
+
+  const h2Style = findInlineStyle(html, "h2", "Section");
+  assert.match(h2Style, /background: #92617E/);
+  assert.match(h2Style, /box-shadow: 0 4px 6px rgba\(0, 0, 0, 0\.1\)/);
+
+  const pMatch = html.match(/<p[^>]*style="([^"]*)"[^>]*>/);
+  assert.ok(pMatch, "Expected inline style on <p> tag");
+  assert.match(pMatch![1]!, /color:/);
+
+  const strongPattern = /<strong[^>]*style="([^"]*)"[^>]*>bold<\/strong>/;
+  const strongMatch = html.match(strongPattern);
+  assert.ok(strongMatch, "Expected inline style for <strong>bold</strong>");
+  assert.match(strongMatch![1]!, /font-weight:/);
+});
@@ -59,6 +59,17 @@ test("normalizeCssText and normalizeInlineCss replace variables and strip declar
  assert.doesNotMatch(normalizedHtml, /var\(--md-primary-color\)/);
 });

+test("normalizeInlineCss removes quoted custom property values without leaving fragments behind", () => {
+  const normalizedHtml = normalizeInlineCss(
+    `<html style="--md-font-family: Menlo, Monaco, 'Courier New', monospace; color: var(--md-primary-color)"></html>`,
+    DEFAULT_STYLE,
+  );
+
+  assert.match(normalizedHtml, /style=" color: #0F4C81"/);
+  assert.doesNotMatch(normalizedHtml, /Courier New/);
+  assert.doesNotMatch(normalizedHtml, /--md-font-family/);
+});
+
 test("HTML structure helpers hoist nested lists and remove the first heading", () => {
  const nestedList = `<ul><li>Parent<ul><li>Child</li></ul></li></ul>`;
  assert.equal(
@@ -100,13 +100,13 @@ export function normalizeCssText(cssText: string, style: StyleConfig = DEFAULT_S
    .replace(/var\(--md-accent-color\)/g, style.accentColor)
    .replace(/var\(--md-container-bg\)/g, style.containerBg)
    .replace(/hsl\(var\(--foreground\)\)/g, "#3f3f3f")
-    .replace(/--md-primary-color:\s*[^;"']+;?/g, "")
-    .replace(/--md-font-family:\s*[^;"']+;?/g, "")
-    .replace(/--md-font-size:\s*[^;"']+;?/g, "")
-    .replace(/--blockquote-background:\s*[^;"']+;?/g, "")
-    .replace(/--md-accent-color:\s*[^;"']+;?/g, "")
-    .replace(/--md-container-bg:\s*[^;"']+;?/g, "")
-    .replace(/--foreground:\s*[^;"']+;?/g, "");
+    .replace(/--md-primary-color:\s*[^;]+;?/g, "")
+    .replace(/--md-font-family:\s*[^;]+;?/g, "")
+    .replace(/--md-font-size:\s*[^;]+;?/g, "")
+    .replace(/--blockquote-background:\s*[^;]+;?/g, "")
+    .replace(/--md-accent-color:\s*[^;]+;?/g, "")
+    .replace(/--md-container-bg:\s*[^;]+;?/g, "")
+    .replace(/--foreground:\s*[^;]+;?/g, "");
 }

 export function normalizeInlineCss(html: string, style: StyleConfig = DEFAULT_STYLE): string {
@@ -6,6 +6,7 @@ import type { ThemeName } from "./types.js";
 const SCRIPT_DIR = path.dirname(fileURLToPath(import.meta.url));
 export const THEME_DIR = path.resolve(SCRIPT_DIR, "themes");
 const FALLBACK_THEMES: ThemeName[] = ["default", "grace", "simple"];
+const THEMES_EXTENDING_DEFAULT = new Set<ThemeName>(["grace", "simple"]);

 function stripOutputScope(cssContent: string): string {
  let css = cssContent;
@@ -41,6 +42,7 @@ export function loadThemeCss(theme: ThemeName): {
  themeCss: string;
 } {
  const basePath = path.join(THEME_DIR, "base.css");
+  const defaultThemePath = path.join(THEME_DIR, "default.css");
  const themePath = path.join(THEME_DIR, `${theme}.css`);

  if (!fs.existsSync(basePath)) {
@@ -51,9 +53,18 @@ export function loadThemeCss(theme: ThemeName): {
    throw new Error(`Missing theme CSS for "${theme}": ${themePath}`);
  }

+  const layeredThemeCss: string[] = [];
+  if (theme !== "default" && THEMES_EXTENDING_DEFAULT.has(theme)) {
+    if (!fs.existsSync(defaultThemePath)) {
+      throw new Error(`Missing default theme CSS: ${defaultThemePath}`);
+    }
+    layeredThemeCss.push(fs.readFileSync(defaultThemePath, "utf-8"));
+  }
+  layeredThemeCss.push(fs.readFileSync(themePath, "utf-8"));
+
  return {
    baseCss: fs.readFileSync(basePath, "utf-8"),
-    themeCss: fs.readFileSync(themePath, "utf-8"),
+    themeCss: layeredThemeCss.join("\n"),
  };
 }

@@ -3,6 +3,11 @@ import path from "node:path";
 import { spawnSync } from "node:child_process";
 import { fileURLToPath } from "node:url";
 import { loadWechatExtendConfig, resolveAccount, loadCredentials } from "./wechat-extend-config.ts";
+import {
+  type WechatUploadAsset,
+  prepareWechatBodyImageUpload,
+  needsWechatBodyImageProcessing,
+} from "./wechat-image-processor.ts";

 interface AccessTokenResponse {
  access_token?: string;
@@ -52,10 +57,10 @@ interface ArticleOptions {
 }

 const TOKEN_URL = "https://api.weixin.qq.com/cgi-bin/token";
-const UPLOAD_URL = "https://api.weixin.qq.com/cgi-bin/material/add_material";
+const UPLOAD_BODY_IMG_URL = "https://api.weixin.qq.com/cgi-bin/media/uploadimg";
+const UPLOAD_MATERIAL_URL = "https://api.weixin.qq.com/cgi-bin/material/add_material";
 const DRAFT_URL = "https://api.weixin.qq.com/cgi-bin/draft/add";

-
 async function fetchAccessToken(appId: string, appSecret: string): Promise<string> {
  const url = `${TOKEN_URL}?grant_type=client_credential&appid=${appId}&secret=${appSecret}`;
  const res = await fetch(url);
@@ -72,14 +77,20 @@ async function fetchAccessToken(appId: string, appSecret: string): Promise<strin
  return data.access_token;
 }

-async function uploadImage(
+function toHttpsUrl(url: string | undefined): string {
+  if (!url) return "";
+  return url.startsWith("http://") ? url.replace(/^http:\/\//i, "https://") : url;
+}
+
+async function loadUploadAsset(
  imagePath: string,
-  accessToken: string,
-  baseDir?: string
-): Promise<UploadResponse> {
+  baseDir?: string,
+): Promise<WechatUploadAsset> {
  let fileBuffer: Buffer;
  let filename: string;
  let contentType: string;
+  let fileSize = 0;
+  let fileExt = "";

  if (imagePath.startsWith("http://") || imagePath.startsWith("https://")) {
    const response = await fetch(imagePath);
@@ -91,8 +102,10 @@ async function uploadImage(
      throw new Error(`Remote image is empty: ${imagePath}`);
    }
    fileBuffer = Buffer.from(buffer);
+    fileSize = buffer.byteLength;
    const urlPath = imagePath.split("?")[0];
    filename = path.basename(urlPath) || "image.jpg";
+    fileExt = path.extname(filename).toLowerCase();
    contentType = response.headers.get("content-type") || "image/jpeg";
  } else {
    const resolvedPath = path.isAbsolute(imagePath)
@@ -106,19 +119,85 @@ async function uploadImage(
    if (stats.size === 0) {
      throw new Error(`Local image is empty: ${resolvedPath}`);
    }
+    fileSize = stats.size;
    fileBuffer = fs.readFileSync(resolvedPath);
    filename = path.basename(resolvedPath);
-    const ext = path.extname(filename).toLowerCase();
+    fileExt = path.extname(filename).toLowerCase();
    const mimeTypes: Record<string, string> = {
      ".jpg": "image/jpeg",
      ".jpeg": "image/jpeg",
      ".png": "image/png",
      ".gif": "image/gif",
      ".webp": "image/webp",
+      ".bmp": "image/bmp",
+      ".tiff": "image/tiff",
+      ".tif": "image/tiff",
+      ".svg": "image/svg+xml",
+      ".ico": "image/x-icon",
    };
-    contentType = mimeTypes[ext] || "image/jpeg";
+    contentType = mimeTypes[fileExt] || "image/jpeg";
  }

+  return {
+    buffer: fileBuffer,
+    filename,
+    contentType,
+    fileExt,
+    fileSize,
+  };
+}
+
+async function uploadImage(
+  imagePath: string,
+  accessToken: string,
+  baseDir?: string,
+  uploadType: "body" | "material" = "body"
+): Promise<UploadResponse> {
+  const asset = await loadUploadAsset(imagePath, baseDir);
+  let uploadAsset = asset;
+
+  if (uploadType === "body" && needsWechatBodyImageProcessing(asset)) {
+    const prepared = await prepareWechatBodyImageUpload(asset);
+    uploadAsset = {
+      ...asset,
+      buffer: prepared.buffer,
+      filename: prepared.filename,
+      contentType: prepared.contentType,
+      fileExt: path.extname(prepared.filename).toLowerCase(),
+      fileSize: prepared.buffer.length,
+    };
+    const note = prepared.processingNotes.join(", ");
+    console.error(`[wechat-api] Processed ${asset.filename} for body upload: ${note}`);
+  }
+
+  const result = await uploadToWechat(
+    uploadAsset.buffer,
+    uploadAsset.filename,
+    uploadAsset.contentType,
+    accessToken,
+    uploadType,
+  );
+
+  // media/uploadimg 接口只返回 URL，material/add_material 返回 media_id
+  if (uploadType === "body") {
+    return {
+      url: toHttpsUrl(result.url),
+      media_id: "",
+    } as UploadResponse;
+  } else {
+    result.url = toHttpsUrl(result.url);
+    return result;
+  }
+}
+
+// 实际的微信上传函数
+async function uploadToWechat(
+  fileBuffer: Buffer,
+  filename: string,
+  contentType: string,
+  accessToken: string,
+  uploadType: "body" | "material"
+): Promise<UploadResponse> {
  const boundary = `----WebKitFormBoundary${Date.now().toString(16)}`;
  const header = [
    `--${boundary}`,
@@ -133,7 +212,8 @@ async function uploadImage(
  const footerBuffer = Buffer.from(footer, "utf-8");
  const body = Buffer.concat([headerBuffer, fileBuffer, footerBuffer]);

-  const url = `${UPLOAD_URL}?access_token=${accessToken}&type=image`;
+  const uploadUrl = uploadType === "body" ? UPLOAD_BODY_IMG_URL : UPLOAD_MATERIAL_URL;
+  const url = `${uploadUrl}?type=image&access_token=${accessToken}`;
  const res = await fetch(url, {
    method: "POST",
    headers: {
@@ -147,10 +227,6 @@ async function uploadImage(
    throw new Error(`Upload failed ${data.errcode}: ${data.errmsg}`);
  }

-  if (data.url?.startsWith("http://")) {
-    data.url = data.url.replace(/^http:\/\//i, "https://");
-  }
-
  return data;
 }

@@ -159,17 +235,19 @@ async function uploadImagesInHtml(
  accessToken: string,
  baseDir: string,
  contentImages: ImageInfo[] = [],
-): Promise<{ html: string; firstMediaId: string; allMediaIds: string[] }> {
+  articleType: ArticleType = "news",
+  collectNewsCoverFallback: boolean = false,
+): Promise<{ html: string; firstCoverMediaId: string; imageMediaIds: string[] }> {
  const imgRegex = /<img[^>]*\ssrc=["']([^"']+)["'][^>]*>/gi;
  const matches = [...html.matchAll(imgRegex)];

  if (matches.length === 0 && contentImages.length === 0) {
-    return { html, firstMediaId: "", allMediaIds: [] };
+    return { html, firstCoverMediaId: "", imageMediaIds: [] };
  }

-  let firstMediaId = "";
+  let firstCoverMediaId = "";
  let updatedHtml = html;
-  const allMediaIds: string[] = [];
+  const imageMediaIds: string[] = [];
  const uploadedBySource = new Map<string, UploadResponse>();

  for (const match of matches) {
@@ -177,8 +255,13 @@ async function uploadImagesInHtml(
    if (!src) continue;

    if (src.startsWith("https://mmbiz.qpic.cn")) {
-      if (!firstMediaId) {
-        firstMediaId = src;
+      if (collectNewsCoverFallback && !firstCoverMediaId) {
+        try {
+          const coverResp = await uploadImage(src, accessToken, baseDir, "material");
+          firstCoverMediaId = coverResp.media_id;
+        } catch (err) {
+          console.error(`[wechat-api] Failed to reuse existing WeChat image as cover: ${src}`, err);
+        }
      }
      continue;
    }
@@ -186,20 +269,31 @@ async function uploadImagesInHtml(
    const localPathMatch = fullTag.match(/data-local-path=["']([^"']+)["']/);
    const imagePath = localPathMatch ? localPathMatch[1]! : src;

-    console.error(`[wechat-api] Uploading image: ${imagePath}`);
+    console.error(`[wechat-api] Uploading body image: ${imagePath}`);
    try {
      let resp = uploadedBySource.get(imagePath);
      if (!resp) {
-        resp = await uploadImage(imagePath, accessToken, baseDir);
+        // 正文图片使用 media/uploadimg 接口获取 URL
+        resp = await uploadImage(imagePath, accessToken, baseDir, "body");
        uploadedBySource.set(imagePath, resp);
      }
      const newTag = fullTag
        .replace(/\ssrc=["'][^"']+["']/, ` src="${resp.url}"`)
        .replace(/\sdata-local-path=["'][^"']+["']/, "");
      updatedHtml = updatedHtml.replace(fullTag, newTag);
-      allMediaIds.push(resp.media_id);
-      if (!firstMediaId) {
-        firstMediaId = resp.media_id;
+      const shouldUploadMaterial = articleType === "newspic" || (collectNewsCoverFallback && !firstCoverMediaId);
+      if (shouldUploadMaterial) {
+        let materialResp = uploadedBySource.get(`${imagePath}:material`);
+        if (!materialResp) {
+          materialResp = await uploadImage(imagePath, accessToken, baseDir, "material");
+          uploadedBySource.set(`${imagePath}:material`, materialResp);
+        }
+        if (articleType === "newspic" && materialResp.media_id) {
+          imageMediaIds.push(materialResp.media_id);
+        }
+        if (collectNewsCoverFallback && !firstCoverMediaId && materialResp.media_id) {
+          firstCoverMediaId = materialResp.media_id;
+        }
      }
    } catch (err) {
      console.error(`[wechat-api] Failed to upload ${imagePath}:`, err);
@@ -210,27 +304,38 @@ async function uploadImagesInHtml(
    if (!updatedHtml.includes(image.placeholder)) continue;

    const imagePath = image.localPath || image.originalPath;
-    console.error(`[wechat-api] Uploading placeholder image: ${imagePath}`);
+    console.error(`[wechat-api] Uploading body image: ${imagePath}`);

    try {
      let resp = uploadedBySource.get(imagePath);
      if (!resp) {
-        resp = await uploadImage(imagePath, accessToken, baseDir);
+        // 正文图片使用 media/uploadimg 接口获取 URL
+        resp = await uploadImage(imagePath, accessToken, baseDir, "body");
        uploadedBySource.set(imagePath, resp);
      }

      const replacementTag = `<img src="${resp.url}" style="display: block; width: 100%; margin: 1.5em auto;">`;
      updatedHtml = replaceAllPlaceholders(updatedHtml, image.placeholder, replacementTag);
-      allMediaIds.push(resp.media_id);
-      if (!firstMediaId) {
-        firstMediaId = resp.media_id;
+      const shouldUploadMaterial = articleType === "newspic" || (collectNewsCoverFallback && !firstCoverMediaId);
+      if (shouldUploadMaterial) {
+        let materialResp = uploadedBySource.get(`${imagePath}:material`);
+        if (!materialResp) {
+          materialResp = await uploadImage(imagePath, accessToken, baseDir, "material");
+          uploadedBySource.set(`${imagePath}:material`, materialResp);
+        }
+        if (articleType === "newspic" && materialResp.media_id) {
+          imageMediaIds.push(materialResp.media_id);
+        }
+        if (collectNewsCoverFallback && !firstCoverMediaId && materialResp.media_id) {
+          firstCoverMediaId = materialResp.media_id;
+        }
      }
    } catch (err) {
      console.error(`[wechat-api] Failed to upload placeholder ${image.placeholder}:`, err);
    }
  }

-  return { html: updatedHtml, firstMediaId, allMediaIds };
+  return { html: updatedHtml, firstCoverMediaId, imageMediaIds };
 }

 async function publishToDraft(
@@ -345,7 +450,7 @@ function renderMarkdownWithPlaceholders(

 function replaceAllPlaceholders(html: string, placeholder: string, replacement: string): string {
  const escapedPlaceholder = placeholder.replace(/[.*+?^${}()|[\]\\]/g, "\\$&");
-  return html.replace(new RegExp(escapedPlaceholder, "g"), replacement);
+  return html.replace(new RegExp(escapedPlaceholder + "(?!\\d)", "g"), replacement);
 }

 function extractHtmlContent(htmlPath: string): string {
@@ -589,19 +694,13 @@ async function main(): Promise<void> {
  }

  const creds = loadCredentials(resolved);
+  for (const skippedSource of creds.skippedSources) {
+    console.error(`[wechat-api] Skipped incomplete credential source: ${skippedSource}`);
+  }
+  console.error(`[wechat-api] Credentials source: ${creds.source}`);
  console.error("[wechat-api] Fetching access token...");
  const accessToken = await fetchAccessToken(creds.appId, creds.appSecret);

-  console.error("[wechat-api] Uploading images...");
-  const { html: processedHtml, firstMediaId, allMediaIds } = await uploadImagesInHtml(
-    htmlContent,
-    accessToken,
-    baseDir,
-    contentImages,
-  );
-  htmlContent = processedHtml;
-
-  let thumbMediaId = "";
  const rawCoverPath = args.cover ||
    frontmatter.coverImage ||
    frontmatter.featureImage ||
@@ -610,19 +709,31 @@ async function main(): Promise<void> {
  const coverPath = rawCoverPath && !path.isAbsolute(rawCoverPath) && args.cover
    ? path.resolve(process.cwd(), rawCoverPath)
    : rawCoverPath;
+  const needNewsCoverFallback = args.articleType === "news" && !coverPath;
+
+  console.error("[wechat-api] Uploading body images...");
+  const { html: processedHtml, firstCoverMediaId, imageMediaIds } = await uploadImagesInHtml(
+    htmlContent,
+    accessToken,
+    baseDir,
+    contentImages,
+    args.articleType,
+    needNewsCoverFallback,
+  );
+  htmlContent = processedHtml;
+
+  let thumbMediaId = "";

  if (coverPath) {
    console.error(`[wechat-api] Uploading cover: ${coverPath}`);
-    const coverResp = await uploadImage(coverPath, accessToken, baseDir);
+    // 封面图片使用 material/add_material 接口
+    const coverResp = await uploadImage(coverPath, accessToken, baseDir, "material");
    thumbMediaId = coverResp.media_id;
-  } else if (firstMediaId) {
-    if (firstMediaId.startsWith("https://")) {
-      console.error(`[wechat-api] Uploading first image as cover: ${firstMediaId}`);
-      const coverResp = await uploadImage(firstMediaId, accessToken, baseDir);
-      thumbMediaId = coverResp.media_id;
-    } else {
-      thumbMediaId = firstMediaId;
-    }
+    console.error(`[wechat-api] Cover uploaded successfully, media_id: ${thumbMediaId}`);
+  } else if (firstCoverMediaId && args.articleType === "news") {
+    // news 类型没有封面时，使用第一张正文图的 media_id 作为封面（兜底逻辑）
+    thumbMediaId = firstCoverMediaId;
+    console.error(`[wechat-api] Using first body image as cover (fallback), media_id: ${thumbMediaId}`);
  }

  if (args.articleType === "news" && !thumbMediaId) {
@@ -630,7 +741,7 @@ async function main(): Promise<void> {
    process.exit(1);
  }

-  if (args.articleType === "newspic" && allMediaIds.length === 0) {
+  if (args.articleType === "newspic" && imageMediaIds.length === 0) {
    console.error("Error: newspic requires at least one image in content.");
    process.exit(1);
  }
@@ -643,7 +754,7 @@ async function main(): Promise<void> {
    content: htmlContent,
    thumbMediaId,
    articleType: args.articleType,
-    imageMediaIds: args.articleType === "newspic" ? allMediaIds : undefined,
+    imageMediaIds: args.articleType === "newspic" ? imageMediaIds : undefined,
    needOpenComment: resolved.need_open_comment,
    onlyFansCanComment: resolved.only_fans_can_comment,
  }, accessToken);
@@ -0,0 +1,139 @@
+import assert from "node:assert/strict";
+import fs from "node:fs/promises";
+import os from "node:os";
+import path from "node:path";
+import process from "node:process";
+import test, { type TestContext } from "node:test";
+
+import { loadCredentials } from "./wechat-extend-config.ts";
+
+function useCwd(t: TestContext, cwd: string): void {
+  const previous = process.cwd();
+  process.chdir(cwd);
+  t.after(() => {
+    process.chdir(previous);
+  });
+}
+
+function useHome(t: TestContext, home: string): void {
+  const previous = process.env.HOME;
+  process.env.HOME = home;
+  t.after(() => {
+    if (previous === undefined) {
+      delete process.env.HOME;
+      return;
+    }
+    process.env.HOME = previous;
+  });
+}
+
+function useWechatEnv(
+  t: TestContext,
+  values: Partial<Record<"WECHAT_APP_ID" | "WECHAT_APP_SECRET", string | undefined>>,
+): void {
+  const previous = {
+    WECHAT_APP_ID: process.env.WECHAT_APP_ID,
+    WECHAT_APP_SECRET: process.env.WECHAT_APP_SECRET,
+  };
+
+  if (values.WECHAT_APP_ID === undefined) {
+    delete process.env.WECHAT_APP_ID;
+  } else {
+    process.env.WECHAT_APP_ID = values.WECHAT_APP_ID;
+  }
+
+  if (values.WECHAT_APP_SECRET === undefined) {
+    delete process.env.WECHAT_APP_SECRET;
+  } else {
+    process.env.WECHAT_APP_SECRET = values.WECHAT_APP_SECRET;
+  }
+
+  t.after(() => {
+    if (previous.WECHAT_APP_ID === undefined) {
+      delete process.env.WECHAT_APP_ID;
+    } else {
+      process.env.WECHAT_APP_ID = previous.WECHAT_APP_ID;
+    }
+
+    if (previous.WECHAT_APP_SECRET === undefined) {
+      delete process.env.WECHAT_APP_SECRET;
+    } else {
+      process.env.WECHAT_APP_SECRET = previous.WECHAT_APP_SECRET;
+    }
+  });
+}
+
+async function makeTempDir(prefix: string): Promise<string> {
+  return fs.mkdtemp(path.join(os.tmpdir(), prefix));
+}
+
+async function writeEnvFile(root: string, content: string): Promise<void> {
+  const envPath = path.join(root, ".baoyu-skills", ".env");
+  await fs.mkdir(path.dirname(envPath), { recursive: true });
+  await fs.writeFile(envPath, content);
+}
+
+test("loadCredentials selects the first complete source without mixing values across sources", async (t) => {
+  const cwdRoot = await makeTempDir("wechat-creds-cwd-");
+  const homeRoot = await makeTempDir("wechat-creds-home-");
+
+  useCwd(t, cwdRoot);
+  useHome(t, homeRoot);
+  useWechatEnv(t, {
+    WECHAT_APP_ID: undefined,
+    WECHAT_APP_SECRET: "stale-secret-from-process-env",
+  });
+
+  await writeEnvFile(cwdRoot, "WECHAT_APP_ID=cwd-app-id\nWECHAT_APP_SECRET=cwd-app-secret\n");
+  await writeEnvFile(homeRoot, "WECHAT_APP_ID=home-app-id\nWECHAT_APP_SECRET=home-app-secret\n");
+
+  const credentials = loadCredentials();
+
+  assert.equal(credentials.appId, "cwd-app-id");
+  assert.equal(credentials.appSecret, "cwd-app-secret");
+  assert.equal(credentials.source, "<cwd>/.baoyu-skills/.env");
+  assert.deepEqual(credentials.skippedSources, [
+    "process.env missing WECHAT_APP_ID",
+  ]);
+});
+
+test("loadCredentials prefers a complete process.env pair over lower-priority files", async (t) => {
+  const cwdRoot = await makeTempDir("wechat-creds-cwd-");
+  const homeRoot = await makeTempDir("wechat-creds-home-");
+
+  useCwd(t, cwdRoot);
+  useHome(t, homeRoot);
+  useWechatEnv(t, {
+    WECHAT_APP_ID: "env-app-id",
+    WECHAT_APP_SECRET: "env-app-secret",
+  });
+
+  await writeEnvFile(cwdRoot, "WECHAT_APP_ID=cwd-app-id\nWECHAT_APP_SECRET=cwd-app-secret\n");
+  await writeEnvFile(homeRoot, "WECHAT_APP_ID=home-app-id\nWECHAT_APP_SECRET=home-app-secret\n");
+
+  const credentials = loadCredentials();
+
+  assert.equal(credentials.appId, "env-app-id");
+  assert.equal(credentials.appSecret, "env-app-secret");
+  assert.equal(credentials.source, "process.env");
+  assert.deepEqual(credentials.skippedSources, []);
+});
+
+test("loadCredentials reports skipped incomplete sources when no complete pair exists", async (t) => {
+  const cwdRoot = await makeTempDir("wechat-creds-cwd-");
+  const homeRoot = await makeTempDir("wechat-creds-home-");
+
+  useCwd(t, cwdRoot);
+  useHome(t, homeRoot);
+  useWechatEnv(t, {
+    WECHAT_APP_ID: "env-app-id",
+    WECHAT_APP_SECRET: undefined,
+  });
+
+  await writeEnvFile(cwdRoot, "WECHAT_APP_SECRET=cwd-app-secret\n");
+
+  assert.throws(
+    () => loadCredentials(),
+    /Incomplete credential sources skipped:\n- process\.env missing WECHAT_APP_SECRET\n- <cwd>\/\.baoyu-skills\/\.env missing WECHAT_APP_ID/,
+  );
+});
@@ -196,48 +196,116 @@ function aliasToEnvKey(alias: string): string {
  return alias.toUpperCase().replace(/-/g, "_");
 }

-export function loadCredentials(account?: ResolvedAccount): { appId: string; appSecret: string } {
-  if (account?.app_id && account?.app_secret) {
-    return { appId: account.app_id, appSecret: account.app_secret };
+interface CredentialSource {
+  name: string;
+  appIdKey: string;
+  appSecretKey: string;
+  appId?: string;
+  appSecret?: string;
+}
+
+export interface LoadedCredentials {
+  appId: string;
+  appSecret: string;
+  source: string;
+  skippedSources: string[];
+}
+
+function normalizeCredentialValue(value?: string): string | undefined {
+  const trimmed = value?.trim();
+  return trimmed ? trimmed : undefined;
+}
+
+function describeMissingKeys(source: CredentialSource): string {
+  const missingKeys: string[] = [];
+  if (!source.appId) missingKeys.push(source.appIdKey);
+  if (!source.appSecret) missingKeys.push(source.appSecretKey);
+  return `${source.name} missing ${missingKeys.join(" and ")}`;
+}
+
+function buildCredentialSource(
+  name: string,
+  values: Record<string, string | undefined>,
+  appIdKey: string,
+  appSecretKey: string,
+): CredentialSource {
+  return {
+    name,
+    appIdKey,
+    appSecretKey,
+    appId: normalizeCredentialValue(values[appIdKey]),
+    appSecret: normalizeCredentialValue(values[appSecretKey]),
+  };
+}
+
+function resolveCredentialSource(
+  sources: CredentialSource[],
+  account?: ResolvedAccount,
+): LoadedCredentials {
+  const skippedSources: string[] = [];
+
+  for (const source of sources) {
+    if (source.appId && source.appSecret) {
+      return {
+        appId: source.appId,
+        appSecret: source.appSecret,
+        source: source.name,
+        skippedSources,
+      };
+    }
+
+    if (source.appId || source.appSecret) {
+      skippedSources.push(describeMissingKeys(source));
+    }
  }

+  const hint = account?.alias ? ` (account: ${account.alias})` : "";
+  const partialHint = skippedSources.length > 0
+    ? `\nIncomplete credential sources skipped:\n- ${skippedSources.join("\n- ")}`
+    : "";
+
+  throw new Error(
+    `Missing WECHAT_APP_ID or WECHAT_APP_SECRET${hint}.\n` +
+    "Set via EXTEND.md account config, environment variables, or .baoyu-skills/.env file." +
+    partialHint
+  );
+}
+
+export function loadCredentials(account?: ResolvedAccount): LoadedCredentials {
  const cwdEnvPath = path.join(process.cwd(), ".baoyu-skills", ".env");
  const homeEnvPath = path.join(os.homedir(), ".baoyu-skills", ".env");
  const cwdEnv = loadEnvFile(cwdEnvPath);
  const homeEnv = loadEnvFile(homeEnvPath);

+  const sources: CredentialSource[] = [];
+
+  if (account?.app_id || account?.app_secret) {
+    sources.push({
+      name: account.alias ? `EXTEND.md account "${account.alias}"` : "EXTEND.md account config",
+      appIdKey: "app_id",
+      appSecretKey: "app_secret",
+      appId: normalizeCredentialValue(account.app_id),
+      appSecret: normalizeCredentialValue(account.app_secret),
+    });
+  }
+
  const prefix = account?.alias ? `WECHAT_${aliasToEnvKey(account.alias)}_` : "";
-
-  let appId = "";
-  let appSecret = "";
-
  if (prefix) {
-    appId = process.env[`${prefix}APP_ID`]
-      || cwdEnv[`${prefix}APP_ID`]
-      || homeEnv[`${prefix}APP_ID`]
-      || "";
-    appSecret = process.env[`${prefix}APP_SECRET`]
-      || cwdEnv[`${prefix}APP_SECRET`]
-      || homeEnv[`${prefix}APP_SECRET`]
-      || "";
-  }
-
-  if (!appId) {
-    appId = process.env.WECHAT_APP_ID || cwdEnv.WECHAT_APP_ID || homeEnv.WECHAT_APP_ID || "";
-  }
-  if (!appSecret) {
-    appSecret = process.env.WECHAT_APP_SECRET || cwdEnv.WECHAT_APP_SECRET || homeEnv.WECHAT_APP_SECRET || "";
-  }
-
-  if (!appId || !appSecret) {
-    const hint = account?.alias ? ` (account: ${account.alias})` : "";
-    throw new Error(
-      `Missing WECHAT_APP_ID or WECHAT_APP_SECRET${hint}.\n` +
-      "Set via EXTEND.md account config, environment variables, or .baoyu-skills/.env file."
+    const prefixedKeyLabel = `${prefix}APP_ID/${prefix}APP_SECRET`;
+    sources.push(
+      buildCredentialSource(`process.env (${prefixedKeyLabel})`, process.env, `${prefix}APP_ID`, `${prefix}APP_SECRET`),
+      buildCredentialSource(`<cwd>/.baoyu-skills/.env (${prefixedKeyLabel})`, cwdEnv, `${prefix}APP_ID`, `${prefix}APP_SECRET`),
+      buildCredentialSource(`~/.baoyu-skills/.env (${prefixedKeyLabel})`, homeEnv, `${prefix}APP_ID`, `${prefix}APP_SECRET`),
    );
  }

-  return { appId, appSecret };
+  sources.push(
+    buildCredentialSource("process.env", process.env, "WECHAT_APP_ID", "WECHAT_APP_SECRET"),
+    buildCredentialSource("<cwd>/.baoyu-skills/.env", cwdEnv, "WECHAT_APP_ID", "WECHAT_APP_SECRET"),
+    buildCredentialSource("~/.baoyu-skills/.env", homeEnv, "WECHAT_APP_ID", "WECHAT_APP_SECRET"),
+  );
+
+  return resolveCredentialSource(sources, account);
 }

 export function listAccounts(config: WechatExtendConfig): string[] {
@@ -0,0 +1,252 @@
+import fs from "node:fs/promises";
+import path from "node:path";
+import { fileURLToPath } from "node:url";
+import { Jimp, JimpMime } from "jimp";
+import decodeWebp, { init as initWebpDecode } from "@jsquash/webp/decode.js";
+
+export interface WechatUploadAsset {
+  buffer: Buffer;
+  filename: string;
+  contentType: string;
+  fileExt: string;
+  fileSize: number;
+}
+
+export interface PreparedWechatUploadAsset {
+  buffer: Buffer;
+  filename: string;
+  contentType: string;
+  wasProcessed: boolean;
+  processingNotes: string[];
+}
+
+export const WECHAT_BODY_IMAGE_MAX_SIZE = 1024 * 1024; // 1MB
+export const WECHAT_BODY_IMAGE_UNSUPPORTED_FORMATS = new Set([
+  ".gif",
+  ".webp",
+  ".bmp",
+  ".tiff",
+  ".tif",
+  ".svg",
+  ".ico",
+]);
+
+const BODY_UPLOAD_ALLOWED_MIME_TYPES = new Set([
+  JimpMime.jpeg,
+  JimpMime.png,
+]);
+
+const MIME_TO_EXT: Record<string, string> = {
+  "image/jpeg": ".jpg",
+  "image/png": ".png",
+  "image/gif": ".gif",
+  "image/webp": ".webp",
+  "image/bmp": ".bmp",
+  "image/x-ms-bmp": ".bmp",
+  "image/tiff": ".tiff",
+  "image/svg+xml": ".svg",
+  "image/x-icon": ".ico",
+  "image/vnd.microsoft.icon": ".ico",
+};
+
+const JPEG_QUALITY_STEPS = [82, 74, 66, 58, 50, 42, 34];
+const MAX_WIDTH_STEPS = [2560, 2048, 1600, 1280, 1024, 800, 640, 480];
+
+let webpDecoderReady: Promise<void> | undefined;
+
+type JimpImage = Awaited<ReturnType<typeof Jimp.read>>;
+
+function normalizeMimeType(contentType: string): string {
+  return contentType.split(";")[0]!.trim().toLowerCase();
+}
+
+function extFromMimeType(contentType: string): string {
+  return MIME_TO_EXT[normalizeMimeType(contentType)] || "";
+}
+
+function ensureFileExt(asset: WechatUploadAsset): string {
+  return asset.fileExt || extFromMimeType(asset.contentType);
+}
+
+function basenameWithoutExt(filename: string): string {
+  const base = path.basename(filename, path.extname(filename));
+  return base || "image";
+}
+
+function renameWithExt(filename: string, ext: string): string {
+  return `${basenameWithoutExt(filename)}${ext}`;
+}
+
+export function needsWechatBodyImageProcessing(asset: WechatUploadAsset): boolean {
+  if (asset.fileSize > WECHAT_BODY_IMAGE_MAX_SIZE) {
+    return true;
+  }
+
+  const normalizedMimeType = normalizeMimeType(asset.contentType);
+  if (BODY_UPLOAD_ALLOWED_MIME_TYPES.has(normalizedMimeType)) {
+    return false;
+  }
+
+  const fileExt = ensureFileExt(asset);
+  return WECHAT_BODY_IMAGE_UNSUPPORTED_FORMATS.has(fileExt) || !fileExt;
+}
+
+async function ensureWebpDecoder(): Promise<void> {
+  if (!webpDecoderReady) {
+    webpDecoderReady = (async () => {
+      const __filename = fileURLToPath(import.meta.url);
+      const __dirname = path.dirname(__filename);
+      const wasmPath = path.resolve(__dirname, "node_modules/@jsquash/webp/codec/dec/webp_dec.wasm");
+      const wasmModule = await WebAssembly.compile(await fs.readFile(wasmPath));
+      await initWebpDecode(wasmModule, {});
+    })();
+  }
+
+  await webpDecoderReady;
+}
+
+async function loadImageForProcessing(asset: WechatUploadAsset): Promise<JimpImage> {
+  const fileExt = ensureFileExt(asset);
+  const normalizedMimeType = normalizeMimeType(asset.contentType);
+
+  if (fileExt === ".webp" || normalizedMimeType === "image/webp") {
+    await ensureWebpDecoder();
+    const decoded = await decodeWebp(asset.buffer);
+    return new Jimp({
+      data: Buffer.from(decoded.data.buffer, decoded.data.byteOffset, decoded.data.byteLength),
+      width: decoded.width,
+      height: decoded.height,
+    });
+  }
+
+  if (fileExt === ".svg" || fileExt === ".ico") {
+    throw new Error(`Cannot convert ${fileExt} image for WeChat body upload; provide a PNG or JPG instead.`);
+  }
+
+  return Jimp.read(asset.buffer);
+}
+
+function imageHasTransparency(image: JimpImage): boolean {
+  const { data } = image.bitmap;
+  for (let i = 3; i < data.length; i += 4) {
+    if (data[i] !== 255) {
+      return true;
+    }
+  }
+  return false;
+}
+
+function buildCandidateWidths(width: number): number[] {
+  const candidates = new Set<number>([width]);
+
+  for (const maxWidth of MAX_WIDTH_STEPS) {
+    if (width > maxWidth) {
+      candidates.add(maxWidth);
+    }
+  }
+
+  return [...candidates].sort((a, b) => b - a);
+}
+
+function resizeToWidth(image: JimpImage, width: number): JimpImage {
+  const cloned = image.clone();
+  if (width < image.bitmap.width) {
+    cloned.resize({ w: width });
+  }
+  return cloned;
+}
+
+function flattenOnWhite(image: JimpImage): JimpImage {
+  const flattened = new Jimp({
+    width: image.bitmap.width,
+    height: image.bitmap.height,
+    color: 0xffffffff,
+  });
+  flattened.composite(image, 0, 0);
+  return flattened;
+}
+
+async function encodePng(image: JimpImage): Promise<Buffer> {
+  return image.getBuffer(JimpMime.png);
+}
+
+async function encodeJpeg(image: JimpImage, quality: number): Promise<Buffer> {
+  const jpegSource = imageHasTransparency(image) ? flattenOnWhite(image) : image;
+  return jpegSource.getBuffer(JimpMime.jpeg, { quality });
+}
+
+function buildProcessingNotes(asset: WechatUploadAsset): string[] {
+  const notes: string[] = [];
+  const fileExt = ensureFileExt(asset);
+
+  if (fileExt && WECHAT_BODY_IMAGE_UNSUPPORTED_FORMATS.has(fileExt)) {
+    notes.push(`converted unsupported ${fileExt} source`);
+  }
+
+  if (asset.fileSize > WECHAT_BODY_IMAGE_MAX_SIZE) {
+    notes.push(`compressed ${(asset.fileSize / 1024 / 1024).toFixed(2)}MB source below 1MB`);
+  }
+
+  if (notes.length === 0) {
+    notes.push("re-encoded for WeChat body upload");
+  }
+
+  return notes;
+}
+
+export async function prepareWechatBodyImageUpload(
+  asset: WechatUploadAsset,
+): Promise<PreparedWechatUploadAsset> {
+  if (!needsWechatBodyImageProcessing(asset)) {
+    return {
+      buffer: asset.buffer,
+      filename: asset.filename,
+      contentType: asset.contentType,
+      wasProcessed: false,
+      processingNotes: [],
+    };
+  }
+
+  const image = await loadImageForProcessing(asset);
+  const widths = buildCandidateWidths(image.bitmap.width);
+  const preferPng = imageHasTransparency(image) || ensureFileExt(asset) === ".png";
+  const processingNotes = buildProcessingNotes(asset);
+
+  for (const width of widths) {
+    const resized = resizeToWidth(image, width);
+
+    if (preferPng) {
+      const pngBuffer = await encodePng(resized);
+      if (pngBuffer.length <= WECHAT_BODY_IMAGE_MAX_SIZE) {
+        return {
+          buffer: pngBuffer,
+          filename: renameWithExt(asset.filename, ".png"),
+          contentType: JimpMime.png,
+          wasProcessed: true,
+          processingNotes: width < image.bitmap.width
+            ? [...processingNotes, `resized to ${width}px wide`]
+            : processingNotes,
+        };
+      }
+    }
+
+    for (const quality of JPEG_QUALITY_STEPS) {
+      const jpegBuffer = await encodeJpeg(resized, quality);
+      if (jpegBuffer.length <= WECHAT_BODY_IMAGE_MAX_SIZE) {
+        const notes = [...processingNotes, `encoded as JPEG (${quality} quality)`];
+        if (width < image.bitmap.width) {
+          notes.push(`resized to ${width}px wide`);
+        }
+        return {
+          buffer: jpegBuffer,
+          filename: renameWithExt(asset.filename, ".jpg"),
+          contentType: JimpMime.jpeg,
+          wasProcessed: true,
+          processingNotes: notes,
+        };
+      }
+    }
+  }
+
+  throw new Error(`Unable to reduce ${asset.filename} below 1MB for WeChat body upload.`);
+}
@@ -123,6 +123,8 @@ ${BUN_X} {baseDir}/scripts/weibo-article.ts article.md --cover ./cover.jpg
 - Title: 32 characters max (truncated with warning if longer)
 - Summary/导语: 44 characters max (auto-regenerated from content if longer)

+**Markdown-to-HTML**: Do NOT pass any `--theme` parameter when converting markdown to HTML. Use the default theme (no theme argument).
+
 **Article Workflow**:
 1. Opens `https://card.weibo.com/article/v3/editor`
 2. Clicks "写文章" button, waits for editor to become editable
@@ -9,12 +9,26 @@ import { COLOR_PRESETS, FONT_FAMILY_MAP } from "./constants.ts";
 import {
  buildMarkdownDocumentMeta,
  formatTimestamp,
+  renderMarkdownDocument,
  resolveColorToken,
  resolveFontFamilyToken,
  resolveMarkdownStyle,
  resolveRenderOptions,
 } from "./document.ts";

+function escapeRegExp(value: string): string {
+  return value.replace(/[.*+?^${}()|[\]\\]/g, `\\$&`);
+}
+
+function findInlineStyle(html: string, tagName: string, text: string): string {
+  const pattern = new RegExp(
+    `<${tagName}[^>]*style="([^"]*)"[^>]*>${escapeRegExp(text)}</${tagName}>`,
+  );
+  const match = html.match(pattern);
+  assert.ok(match, `Expected inline style for <${tagName}>${text}</${tagName}>`);
+  return match![1]!;
+}
+
 function useCwd(t: TestContext, cwd: string): void {
  const previous = process.cwd();
  process.chdir(cwd);
@@ -138,3 +152,23 @@ keep_title: true
  assert.equal(explicit.fontSize, "18px");
  assert.equal(explicit.keepTitle, false);
 });
+
+test("renderMarkdownDocument layers default rules into grace theme before CSS inlining", async () => {
+  const { html } = await renderMarkdownDocument(
+    `## Section\n\nParagraph with **bold** text.`,
+    { keepTitle: true, theme: "grace" },
+  );
+
+  const h2Style = findInlineStyle(html, "h2", "Section");
+  assert.match(h2Style, /background: #92617E/);
+  assert.match(h2Style, /box-shadow: 0 4px 6px rgba\(0, 0, 0, 0\.1\)/);
+
+  const pMatch = html.match(/<p[^>]*style="([^"]*)"[^>]*>/);
+  assert.ok(pMatch, "Expected inline style on <p> tag");
+  assert.match(pMatch![1]!, /color:/);
+
+  const strongPattern = /<strong[^>]*style="([^"]*)"[^>]*>bold<\/strong>/;
+  const strongMatch = html.match(strongPattern);
+  assert.ok(strongMatch, "Expected inline style for <strong>bold</strong>");
+  assert.match(strongMatch![1]!, /font-weight:/);
+});
@@ -59,6 +59,17 @@ test("normalizeCssText and normalizeInlineCss replace variables and strip declar
  assert.doesNotMatch(normalizedHtml, /var\(--md-primary-color\)/);
 });

+test("normalizeInlineCss removes quoted custom property values without leaving fragments behind", () => {
+  const normalizedHtml = normalizeInlineCss(
+    `<html style="--md-font-family: Menlo, Monaco, 'Courier New', monospace; color: var(--md-primary-color)"></html>`,
+    DEFAULT_STYLE,
+  );
+
+  assert.match(normalizedHtml, /style=" color: #0F4C81"/);
+  assert.doesNotMatch(normalizedHtml, /Courier New/);
+  assert.doesNotMatch(normalizedHtml, /--md-font-family/);
+});
+
 test("HTML structure helpers hoist nested lists and remove the first heading", () => {
  const nestedList = `<ul><li>Parent<ul><li>Child</li></ul></li></ul>`;
  assert.equal(
@@ -100,13 +100,13 @@ export function normalizeCssText(cssText: string, style: StyleConfig = DEFAULT_S
    .replace(/var\(--md-accent-color\)/g, style.accentColor)
    .replace(/var\(--md-container-bg\)/g, style.containerBg)
    .replace(/hsl\(var\(--foreground\)\)/g, "#3f3f3f")
-    .replace(/--md-primary-color:\s*[^;"']+;?/g, "")
-    .replace(/--md-font-family:\s*[^;"']+;?/g, "")
-    .replace(/--md-font-size:\s*[^;"']+;?/g, "")
-    .replace(/--blockquote-background:\s*[^;"']+;?/g, "")
-    .replace(/--md-accent-color:\s*[^;"']+;?/g, "")
-    .replace(/--md-container-bg:\s*[^;"']+;?/g, "")
-    .replace(/--foreground:\s*[^;"']+;?/g, "");
+    .replace(/--md-primary-color:\s*[^;]+;?/g, "")
+    .replace(/--md-font-family:\s*[^;]+;?/g, "")
+    .replace(/--md-font-size:\s*[^;]+;?/g, "")
+    .replace(/--blockquote-background:\s*[^;]+;?/g, "")
+    .replace(/--md-accent-color:\s*[^;]+;?/g, "")
+    .replace(/--md-container-bg:\s*[^;]+;?/g, "")
+    .replace(/--foreground:\s*[^;]+;?/g, "");
 }

 export function normalizeInlineCss(html: string, style: StyleConfig = DEFAULT_STYLE): string {
@@ -6,6 +6,7 @@ import type { ThemeName } from "./types.js";
 const SCRIPT_DIR = path.dirname(fileURLToPath(import.meta.url));
 export const THEME_DIR = path.resolve(SCRIPT_DIR, "themes");
 const FALLBACK_THEMES: ThemeName[] = ["default", "grace", "simple"];
+const THEMES_EXTENDING_DEFAULT = new Set<ThemeName>(["grace", "simple"]);

 function stripOutputScope(cssContent: string): string {
  let css = cssContent;
@@ -41,6 +42,7 @@ export function loadThemeCss(theme: ThemeName): {
  themeCss: string;
 } {
  const basePath = path.join(THEME_DIR, "base.css");
+  const defaultThemePath = path.join(THEME_DIR, "default.css");
  const themePath = path.join(THEME_DIR, `${theme}.css`);

  if (!fs.existsSync(basePath)) {
@@ -51,9 +53,18 @@ export function loadThemeCss(theme: ThemeName): {
    throw new Error(`Missing theme CSS for "${theme}": ${themePath}`);
  }

+  const layeredThemeCss: string[] = [];
+  if (theme !== "default" && THEMES_EXTENDING_DEFAULT.has(theme)) {
+    if (!fs.existsSync(defaultThemePath)) {
+      throw new Error(`Missing default theme CSS: ${defaultThemePath}`);
+    }
+    layeredThemeCss.push(fs.readFileSync(defaultThemePath, "utf-8"));
+  }
+  layeredThemeCss.push(fs.readFileSync(themePath, "utf-8"));
+
  return {
    baseCss: fs.readFileSync(basePath, "utf-8"),
-    themeCss: fs.readFileSync(themePath, "utf-8"),
+    themeCss: layeredThemeCss.join("\n"),
  };
 }

@@ -1,7 +1,7 @@
 ---
 name: baoyu-translate
 description: Translates articles and documents between languages with three modes - quick (direct), normal (analyze then translate), and refined (analyze, translate, review, polish). Supports custom glossaries and terminology consistency via EXTEND.md. Use when user asks to "translate", "翻译", "精翻", "translate article", "translate to Chinese/English", "改成中文", "改成英文", "convert to Chinese", "localize", "本地化", or needs any document translation. Also triggers for "refined translation", "精细翻译", "proofread translation", "快速翻译", "快翻", "这篇文章翻译一下", or when a URL or file is provided with translation intent.
-version: 1.56.1
+version: 1.59.0
 metadata:
  openclaw:
    homepage: https://github.com/JimLiu/baoyu-skills#baoyu-translate
@@ -189,12 +189,12 @@ Before translating chunks:
   - Splits at markdown block boundaries to preserve structure
   - If a single block exceeds the threshold, falls back to line splitting, then word splitting
 4. **Assemble translation prompt**:
-   - Main agent reads `01-analysis.md` (if exists) and assembles shared context using Part 1 of [references/subagent-prompt-template.md](references/subagent-prompt-template.md) — inlining the resolved style preset (from `--style` flag, EXTEND.md `style` setting, or default `storytelling`), content background, merged glossary, and comprehension challenges
+   - Main agent reads `01-analysis.md` (if exists) and assembles shared context using Part 1 of [references/subagent-prompt-template.md](references/subagent-prompt-template.md) — inlining: target style, content background, merged glossary, and translation challenges
   - Save as `02-prompt.md` in the output directory (shared context only, no task instructions)
 5. **Draft translation via subagents** (if Agent tool available):
   - Spawn one subagent **per chunk**, all in parallel (Part 2 of the template)
-   - Each subagent reads `02-prompt.md` for shared context, translates its chunk, saves to `chunks/chunk-NN-draft.md`
-   - Terminology consistency is guaranteed by the shared `02-prompt.md` (glossary + comprehension challenges from analysis)
+   - Each subagent reads `02-prompt.md` for shared context, receives chunk position info (chunk N of M + brief context of where it sits in the argument), translates its chunk, saves to `chunks/chunk-NN-draft.md`
+   - Consistency is guaranteed by the shared `02-prompt.md` (glossary, figurative language mapping, comprehension challenges, source voice, and translation challenges from analysis)
   - If no chunks (content under threshold): spawn one subagent for the entire source file
   - If Agent tool is unavailable, translate chunks sequentially inline using `02-prompt.md`
 6. **Merge**: Once all subagents complete, combine translated chunks in order. If `chunks/frontmatter.md` exists, prepend it. Save as `03-draft.md` (refined) or `translation.md` (normal)
@@ -206,26 +206,22 @@ Before translating chunks:

 **Translation principles** (apply to all modes):

+- **Rewrite, not translate**: Rewrite content into natural, engaging target language as if a skilled native writer composed it from scratch. Quality test: "Does this read like it was originally written in the target language?"
 - **Accuracy first**: Facts, data, and logic must match the original exactly
- **Meaning over words**: Translate what the author means, not just what the words say. When a literal translation sounds unnatural or fails to convey the intended effect, restructure freely to express the same meaning in idiomatic target language
- **Figurative language**: Interpret metaphors, idioms, and figurative expressions by their intended meaning rather than translating them word-for-word. When a source-language image does not carry the same connotation in the target language, replace it with a natural expression that conveys the same idea and emotional effect
- **Emotional fidelity**: Preserve the emotional connotations of word choices, not just their dictionary meanings. Words that carry subjective feelings (e.g., "alarming", "haunting") should be rendered to evoke the same response in target-language readers
- **Natural flow**: Use idiomatic target language word order and sentence patterns; break or restructure sentences freely when the source structure doesn't work naturally in the target language
- **Terminology**: Use standard translations; annotate with original term in parentheses on first occurrence
+- **Natural flow**: Use idiomatic target language word order. Break long source sentences into shorter, natural ones. Interpret metaphors and idioms by intended meaning, not word-for-word
+- **Terminology**: Use standard translations consistently. First occurrence of specialized terms: annotate with original in parentheses
 - **Preserve format**: Keep all markdown formatting (headings, bold, italic, images, links, code blocks)
- **Image-language awareness**: Preserve image references exactly during translation, but after the translation is complete, review referenced images and check whether their likely main text language still matches the translated article language
- **Frontmatter transformation**: If the source has YAML frontmatter, preserve it in the translation with these changes: (1) Rename metadata fields that describe the *source* article — `url`→`sourceUrl`, `title`→`sourceTitle`, `description`→`sourceDescription`, `author`→`sourceAuthor`, `date`→`sourceDate`, and any similar origin-metadata fields — by adding a `source` prefix (camelCase). (2) Translate the values of text fields (title, description, etc.) and add them as new top-level fields. (3) Keep other fields (tags, categories, custom fields) as-is, translating their values where appropriate
- **Respect original**: Maintain original meaning and intent; do not add, remove, or editorialize — but sentence structure and imagery may be adapted freely to serve the meaning
- **Translator's notes**: For terms, concepts, or cultural references that target readers may not understand — due to jargon, cultural gaps, or domain-specific knowledge — add a concise explanatory note in parentheses immediately after the term. The note should explain *what it means* in plain language, not just provide the English original. Format: `译文（English original，通俗解释）`. Calibrate annotation depth to the target audience: general readers need more notes than technical readers. For short texts (< 5 sentences), further reduce annotations — only annotate non-common terms that the target audience is unlikely to know; skip terms that are widely recognized or self-explanatory in context. Only add notes where genuinely needed; do not over-annotate obvious terms.
+- **Proactive interpretation**: For jargon or concepts the target audience may lack context for, add concise explanations in **bold parentheses** `（**解释**）`. Keep annotations few — only where genuinely needed for comprehension
+- **Frontmatter**: If source has YAML frontmatter, rename source-metadata fields with `source` prefix (camelCase: `url`→`sourceUrl`, `title`→`sourceTitle`, etc.), add translated values as new top-level fields (skip `title` if body has H1), keep other fields as-is

 #### Quick Mode

-Translate directly → save to `translation.md`. No analysis file, but still apply all translation principles above — especially: interpret figurative language by meaning (not word-for-word), preserve emotional connotations, and restructure sentences for natural target-language flow.
+Translate directly → save to `translation.md`. Apply all translation principles above.

 #### Normal Mode

-1. **Analyze** → `01-analysis.md` (domain, tone, audience, terminology, reader comprehension challenges, figurative language & metaphor mapping)
-2. **Assemble prompt** → `02-prompt.md` (translation instructions with inlined style preset, content background, glossary, and comprehension challenges)
+1. **Analyze** → `01-analysis.md` (domain, tone, terminology, translation challenges)
+2. **Assemble prompt** → `02-prompt.md` (translation instructions with context, glossary, challenges)
 3. **Translate** (following `02-prompt.md`) → `translation.md`

 After completion, prompt user: "Translation saved. To further review and polish, reply **继续润色** or **refine**."
@@ -239,7 +235,7 @@ Full workflow for publication quality. See [references/refined-workflow.md](refe
 The subagent (if used in Step 3.1) only handles the initial draft. All subsequent steps (critical review, revision, polish) are handled by the main agent, which may delegate to subagents at its discretion.

 Steps and saved files (all in output directory):
-1. **Analyze** → `01-analysis.md` (domain, tone, terminology, reader comprehension challenges, figurative language & metaphor mapping)
+1. **Analyze** → `01-analysis.md` (domain, tone, terminology, translation challenges)
 2. **Assemble prompt** → `02-prompt.md` (translation instructions with inlined context)
 3. **Draft** → `03-draft.md` (initial translation with translator's notes; from subagent if chunked)
 4. **Critical review** → `04-critique.md` (diagnosis only: accuracy, Europeanized language, strategy execution, expression issues)
@@ -11,95 +11,38 @@ All intermediate results are saved as files in the output directory.

 ## Step 1: Content Analysis

-Before translating, deeply analyze the source material. Save analysis to `01-analysis.md` in the output directory. Focus on dimensions that directly inform translation quality.
+Before translating, analyze the source material. Save analysis to `01-analysis.md` in the output directory.

-### 1.1 Quick Summary
+### 1.1 Content Summary

-3-5 sentences capturing:
- What is this content about?
- What is the core argument?
- What is the most valuable point?
+- What is this content about? What is the core argument?
+- Author background, stance, and writing context
+- Purpose and intended audience of the original

-### 1.2 Core Content
+### 1.2 Terminology

- **Core argument**: One sentence summary
- **Key concepts**: What key concepts does the author use? How are they defined?
- **Structure**: How is the argument developed? How do sections connect?
- **Evidence**: What specific examples, data, or authoritative citations are used?
-
-### 1.3 Background Context
-
- **Author**: Who is the author? What is their background and stance?
- **Writing context**: What phenomenon, trend, or debate is this responding to?
- **Purpose**: What problem is the author trying to solve? Who are they trying to influence?
- **Implicit assumptions**: What unstated premises underlie the argument?
-
-### 1.4 Terminology Extraction
-
- List all technical terms, proper nouns, brand names, acronyms
+- List technical terms, proper nouns, brand names, acronyms
 - Cross-reference with loaded glossaries
- For terms not in glossary, research standard translations
- Record decisions in a working terminology table
+- For terms not in glossary, determine standard translations
+- Record in a terminology table

-### 1.5 Tone & Style
+### 1.3 Tone & Style

- Is the original formal or conversational?
- Does it use humor, metaphor, or cultural references?
+- Formal or conversational? Humor, metaphor, cultural references?
 - What register is appropriate for the translation given the target audience?

-### 1.6 Reader Comprehension Challenges
+### 1.4 Translation Challenges

-Identify points where target readers may struggle, calibrated to the target audience:
+Identify what may cause difficulty in translation:

- **Domain jargon**: Technical terms that lack widely-known translations or are meaningless when translated literally
- **Cultural references**: Idioms, historical events, pop culture, social norms specific to the source culture
- **Implicit knowledge**: Background context the original author assumes but target readers may lack
- **Wordplay & metaphors**: Figurative language that doesn't carry over across languages
- **Named concepts**: Theories, effects, or phenomena with coined names (e.g., "comb-over effect", "Dunning-Kruger effect")
- **Cognitive gaps**: Counterintuitive claims or expectations vs. reality that need framing for target readers
-
-For each identified challenge, note:
-1. The original term/passage
-2. Why it may confuse target readers
-3. A concise plain-language explanation to use as a translator's note
-
-### 1.7 Figurative Language & Metaphor Mapping
-
-Identify all metaphors, similes, idioms, and figurative expressions in the source. For each:
-
-1. **Original expression**: The exact phrase
-2. **Intended meaning**: What the author is actually communicating (the idea behind the image)
-3. **Literal translation risk**: Would a word-for-word translation sound unnatural, lose the connotation, or confuse target readers?
-4. **Target-language approach**: One of:
-   - **Interpret**: Discard the source image entirely, express the intended meaning directly in natural target language
-   - **Substitute**: Replace with a target-language idiom or image that conveys the same idea and emotional effect
-   - **Retain**: Keep the original image if it works equally well in the target language
-
-Also flag:
- **Emotional connotations carried by word choice**: Words like "alarming" that convey subjective feeling, not just objective description — note the emotional effect to preserve
- **Implied meanings**: Sentences where the surface meaning is simple but the implication is richer — note what the author really means so the translator can convey the full intent
-
-### 1.8 Structural & Creative Challenges
-
- Complex sentence patterns (long subordinate clauses, nested modifiers, participial phrases) that need restructuring for natural target-language flow
- Structural challenges (wordplay, ambiguity, puns that don't translate)
- Content where the author's voice or humor requires creative adaptation
+- **Comprehension gaps**: Terms or references that target readers may not understand — note what explanation is needed
+- **Figurative language**: Metaphors, idioms, expressions that don't translate literally — note intended meaning and target-language approach (interpret / substitute / retain)
+- **Structural challenges**: Long complex sentences, wordplay, puns, or humor that needs creative adaptation

 **Save `01-analysis.md`** with:
 ```
-## Quick Summary
-[3-5 sentences]
-
-## Core Content
-Core argument: [one sentence]
-Key concepts: [list]
-Structure: [outline]
-
-## Background Context
-Author: [who, background, stance]
-Writing context: [what this responds to]
-Purpose: [goal and target audience]
-Implicit assumptions: [unstated premises]
+## Content Summary
+[Core argument, author, context, purpose]

 ## Terminology
 [term → translation, ...]
@@ -107,23 +50,21 @@ Implicit assumptions: [unstated premises]
 ## Tone & Style
 [assessment]

-## Comprehension Challenges
- [term/passage] → [why confusing] → [proposed note]
+## Translation Challenges
+- [term/passage] → [challenge type] → [suggested approach]
 - ...
-
-## Figurative Language & Metaphor Mapping
- [original expression] → [intended meaning] → [approach: interpret/substitute/retain] → [suggested rendering]
- ...
-
-## Structural & Creative Challenges
-[sentence restructuring needs, wordplay, creative adaptation needs]
 ```

 ## Step 2: Assemble Translation Prompt

-Main agent reads `01-analysis.md` and assembles a complete translation prompt using [references/subagent-prompt-template.md](subagent-prompt-template.md). Inline the resolved style preset (from `--style` flag, EXTEND.md `style` setting, or default `storytelling`), content background, merged glossary, and comprehension challenges into the prompt. Save to `02-prompt.md`.
+Main agent reads `01-analysis.md` and assembles a complete translation prompt using [references/subagent-prompt-template.md](subagent-prompt-template.md). Inline the following from analysis:

-This prompt is used by the subagent (chunked) or by the main agent itself (non-chunked).
+- **Target style**: Resolved style preset + source voice assessment from §1.3
+- **Content background**: Summary from §1.1
+- **Glossary**: Merged glossary with analysis-extracted terms from §1.2
+- **Translation challenges**: All challenges from §1.4
+
+Save to `02-prompt.md`. This prompt is used by the subagent (chunked) or by the main agent itself (non-chunked).

 ## Step 3: Initial Draft

@@ -131,111 +72,54 @@ Save to `03-draft.md` in the output directory.

 For chunked content, the subagent produces this draft (merged from chunk translations). For non-chunked content, the main agent produces it directly.

-Translate the full content following `02-prompt.md`. Apply all **Translation principles** from SKILL.md Step 4, plus these step-specific guidelines:
-
- Use the terminology decisions from Step 1 consistently
- Match the identified tone and register
- Follow the metaphor mapping from Step 1 for figurative language handling
- Add translator's notes for comprehension challenges identified in Step 1
+Translate the full content following `02-prompt.md`. Apply all **Translation principles** from SKILL.md.

 ## Step 4: Critical Review

 The main agent critically reviews the draft against the source. Save review findings to `04-critique.md`. This step produces **diagnosis only** — no rewriting yet.

-### 4.1 Accuracy & Completeness
- Compare each paragraph against the original, sentence by sentence
- Verify all facts, numbers, dates, and proper nouns
- Flag any content accidentally added, removed, or altered
- Check that technical terms match glossary consistently throughout
- Verify no paragraphs or sections were skipped
+### 4.1 Accuracy

-### 4.2 Europeanized Language Diagnosis (for CJK targets)
- **Unnecessary connectives**: Overuse of 因此/然而/此外/另外 where context already implies the relationship
- **Passive voice abuse**: Excessive 被/由/受到 where active voice is more natural
- **Noun pile-up**: Long modifier chains that should be broken into shorter clauses
- **Cleft sentences**: Unnatural "是...的" structures calqued from English "It is...that"
- **Over-nominalization**: Abstract nouns where verbs or adjectives would be more natural (e.g., "进行了讨论" → "讨论了")
- **Awkward pronouns**: Overuse of 他/她/它/我们/你 where they can be omitted
+- Compare each paragraph against the original
+- Verify facts, numbers, dates, proper nouns
+- Flag content accidentally added, removed, or altered
+- Check terminology consistency with glossary

-### 4.3 Figurative Language & Emotional Fidelity
- Cross-check against the metaphor mapping in `01-analysis.md`: were all flagged metaphors/idioms handled per the recommended approach (interpret/substitute/retain)?
- Flag any metaphors or figurative expressions that were translated literally and sound unnatural or lose the intended meaning in the target language
- Check emotional connotations: do words that carry subjective feelings in the source (e.g., "alarming", "haunting", "striking") evoke the same response in the translation, or were they flattened into neutral/objective descriptions?
- Flag implied meanings that were lost: sentences where the author's deeper intent was not conveyed because the translator stayed too close to the surface meaning
+### 4.2 Native Voice

-### 4.4 Strategy Execution
- Were the translation strategies from `02-prompt.md` actually followed?
- Did the translator apply the tone and register identified in analysis?
- Were comprehension challenges from `01-analysis.md` addressed with appropriate notes?
- Were glossary terms used consistently?
+- Flag sentences that read as "translated" rather than "written" — unnatural word order, calques, stiff phrasing
+- For CJK targets: check for unnecessary connectives (因此/然而/此外), passive voice abuse (被/由/受到), noun pile-ups, over-nominalization
+- Flag metaphors translated literally that sound unnatural in the target language
+- Check emotional connotations are preserved, not flattened
+- Note where sentence restructuring would improve readability

-### 4.5 Expression & Logic
- Flag sentences that read like "translationese" — unnatural word order, calques, stiff phrasing
- Check logical flow between sentences and paragraphs
- Identify where sentence restructuring would improve readability
- Note where the target language idiom was missed
+### 4.3 Notes & Adaptation

-### 4.6 Translator's Notes Quality
- Are notes accurate, concise, and genuinely helpful?
- Identify missed comprehension challenges that need notes
- Flag over-annotations on terms obvious to the target audience
- Check that cultural references are explained where needed
-
-### 4.7 Cultural Adaptation
- Do metaphors and idioms work in the target language?
- Are any references potentially confusing or offensive in the target culture?
- Could any passage be misinterpreted due to cultural context differences?
+- Are translator's notes accurate, concise, and genuinely helpful?
+- Flag missed comprehension challenges that need notes, and over-annotations on obvious terms
+- Were translation strategies from `02-prompt.md` followed?
+- Do cultural references work in the target language?

 **Save `04-critique.md`** with:
 ```
-## Accuracy & Completeness
+## Accuracy
 - [issue]: [location] — [description]
- ...

-## Europeanized Language Issues
- [issue type]: [example from draft] → [suggested fix]
- ...
+## Native Voice
+- [issue]: [example] → [suggested fix]

-## Figurative Language & Emotional Fidelity
- [literal metaphor]: [original] → [draft rendering] → [suggested interpretation]
- [flattened emotion]: [original word/phrase] → [draft rendering] → [how to restore emotional effect]
- ...
-
-## Strategy Execution
- [strategy]: [followed/missed] — [details]
- ...
-
-## Expression & Logic
- [location]: [problem] → [suggestion]
- ...
-
-## Translator's Notes
- [add/remove/revise]: [term] — [reason]
- ...
-
-## Cultural Adaptation
- [issue]: [description] — [suggestion]
- ...
+## Notes & Adaptation
+- [add/remove/revise]: [term/passage] — [reason]

 ## Summary
-[Overall assessment: X critical issues, Y improvements, Z minor suggestions]
+[Overall assessment: X critical issues, Y improvements]
 ```

 ## Step 5: Revision

 Apply all findings from `04-critique.md` to produce a revised translation. Save to `05-revision.md`.

-The revision reads `03-draft.md` (the original draft) and `04-critique.md` (the review findings), and may also refer back to the source text and `01-analysis.md`:
-
- Fix all accuracy issues identified in the critique
- Rewrite Europeanized expressions into natural target-language patterns
- Re-interpret literally translated metaphors and figurative expressions per the metaphor mapping; replace with natural target-language renderings that convey the intended meaning and emotional effect
- Restore flattened emotional connotations: ensure words carrying subjective feelings evoke the same response as the source
- Apply missed translation strategies
- Restructure stiff or awkward sentences for fluency
- Add, remove, or revise translator's notes per critique recommendations
- Improve transitions between paragraphs
- Adapt cultural references as suggested
+Read `03-draft.md` and `04-critique.md`, fix all accuracy issues, rewrite unnatural expressions, adjust notes, and improve flow.

 ## Step 6: Polish

@@ -244,21 +128,18 @@ Save final version to `translation.md`.
 Final pass on `05-revision.md` for publication quality:

 - Read the entire translation as a standalone piece — does it flow as native content?
- Smooth any remaining rough transitions between paragraphs
- Ensure the narrative voice is consistent throughout
- Apply the selected translation style consistently: storytelling should flow like a narrative, formal should maintain neutral professionalism, humorous should land jokes naturally in the target language, etc.
- Final scan for surviving literal metaphors or flattened emotions: any figurative expression that still reads as "translated" rather than "written" should be recast into natural target-language expression
- Final consistency check on terminology across the full text
- Verify formatting is preserved correctly (headings, bold, links, code blocks)
- Remove any remaining traces of translationese
+- Smooth remaining rough transitions
+- Ensure consistent narrative voice and style throughout
+- Final terminology consistency check
+- Verify formatting is preserved correctly

 ## Subagent Responsibility

-Each subagent (one per chunk) is responsible **only** for producing the initial draft of its chunk (Step 3). The main agent assembles the shared prompt (Step 2), spawns all subagents in parallel, then takes over for critical review (Step 4), revision (Step 5), and polish (Step 6). The main agent may delegate revision or polish to subagents at its own discretion.
+Each subagent (one per chunk) is responsible **only** for producing the initial draft of its chunk (Step 3). The main agent assembles the shared prompt (Step 2), spawns all subagents in parallel, then takes over for critical review (Step 4), revision (Step 5), and polish (Step 6).

 ## Chunked Refined Translation

-When content exceeds the chunk threshold (see Defaults in SKILL.md) and uses refined mode:
+When content exceeds the chunk threshold and uses refined mode:

 1. Main agent runs analysis (Step 1) on the **entire** document first → `01-analysis.md`
 2. Main agent assembles translation prompt → `02-prompt.md`
@@ -267,7 +148,4 @@ When content exceeds the chunk threshold (see Defaults in SKILL.md) and uses ref
 5. Main agent critically reviews the merged draft → `04-critique.md`
 6. Main agent revises based on critique → `05-revision.md`
 7. Main agent polishes → `translation.md`
-7. Final cross-chunk consistency check:
-   - Check terminology consistency across chunk boundaries
-   - Verify narrative flow between chunks
-   - Fix any transition issues at chunk boundaries
+8. Final cross-chunk consistency check: terminology, narrative flow, transitions at chunk boundaries
@@ -15,43 +15,39 @@ Replace `{placeholders}` with actual values. Omit sections marked "if analysis e
 ```markdown
 You are a professional translator. Your task is to translate markdown content from {source_lang} to {target_lang}.

-## Target Audience
+## Target Audience & Style

-{audience description}
+**Audience**: {audience description}

-## Translation Style
+**Target style**: {style description — e.g., "storytelling: engaging narrative flow, smooth transitions, vivid phrasing" or custom style from user}

-{style description — e.g., "storytelling: engaging narrative flow, smooth transitions, vivid phrasing" or custom style from user}
-
-Apply this style consistently: it determines the voice, tone, and sentence-level choices throughout the translation. Style is independent of audience — a technical audience can still get a storytelling-style translation, or a general audience can get a formal one.
+**Source voice** (from analysis, if exists): {Brief description of the original author's voice — formal/conversational, humor, register, sentence rhythm.}

 ## Content Background

-{Inlined from 01-analysis.md if analysis exists: quick summary, core argument, author background, writing context, tone assessment, figurative language & metaphor mapping.}
+{Inlined from 01-analysis.md if analysis exists: content summary, core argument, author background, context.}

 ## Glossary

-Apply these term translations consistently throughout. First occurrence of each term: include the original in parentheses after the translation.
+Apply these term translations consistently. First occurrence: include original in parentheses.

-{Merged glossary — combine built-in glossary + EXTEND.md glossary + terms extracted in analysis. One per line: English → Translation}
+{Merged glossary — one per line: English → Translation}

-## Comprehension Challenges
+## Translation Challenges

-The following terms or references may confuse target readers. Add translator's notes in parentheses where they appear: `译文（English original，通俗解释）`
+{Inlined from 01-analysis.md §1.4 if analysis exists. Comprehension gaps, figurative language, structural challenges with suggested approaches:}

-{Inlined from 01-analysis.md comprehension challenges section if analysis exists. Each entry: term → explanation to use as note.}
+- **{term/passage}**: {challenge type} → {suggested approach}

 ## Translation Principles

+Rewrite the content into natural, engaging {target_lang} — not merely translate it. Every sentence should read as if a skilled native writer composed it from scratch.
+
 - **Accuracy first**: Facts, data, and logic must match the original exactly
- **Meaning over words**: Translate what the author means, not just what the words say. When a literal translation sounds unnatural or fails to convey the intended effect, restructure freely to express the same meaning in idiomatic {target_lang}
- **Figurative language**: Interpret metaphors, idioms, and figurative expressions by their intended meaning. When a source-language image does not carry the same connotation in {target_lang}, replace it with a natural expression that conveys the same idea and emotional effect. Refer to the Figurative Language section in Content Background (if provided) for pre-analyzed metaphor mappings
- **Emotional fidelity**: Preserve the emotional connotations of word choices, not just their dictionary meanings
- **Natural flow**: Use idiomatic {target_lang} word order and sentence patterns; break or restructure sentences freely when the source structure doesn't work naturally
- **Terminology**: Use glossary translations consistently; annotate with original term in parentheses on first occurrence
+- **Natural flow**: Use idiomatic {target_lang} word order. Break long source sentences into shorter, natural ones. Interpret metaphors and idioms by intended meaning, not word-for-word
+- **Terminology**: Use glossary translations consistently. Annotate with original in parentheses on first occurrence of specialized terms
 - **Preserve format**: Keep all markdown formatting (headings, bold, italic, images, links, code blocks)
- **Respect original**: Maintain original meaning and intent; do not add, remove, or editorialize — but sentence structure and imagery may be adapted freely to serve the meaning
- **Translator's notes**: For terms or cultural references listed in Comprehension Challenges above, add a concise explanatory note in parentheses. Only annotate where genuinely needed for the target audience.
+- **Proactive interpretation**: For jargon or concepts the target audience may lack context for, add concise explanations in **bold parentheses** `（**解释**）`. Keep annotations few — only where genuinely needed
 ```

 ---
@@ -63,6 +59,9 @@ The following terms or references may confuse target readers. Add translator's n
 ```
 Read the translation instructions from: {output_dir}/02-prompt.md

+You are translating chunk {NN} of {total_chunks}.
+Context: {brief description of what this chunk covers and where it sits in the overall argument}
+
 Translate this chunk:
 1. Read `{output_dir}/chunks/chunk-{NN}.md`
 2. Translate following the instructions in 02-prompt.md
@@ -1,7 +1,7 @@
 ---
 name: baoyu-url-to-markdown
 description: Fetch any URL and convert to markdown using Chrome CDP. Saves the rendered HTML snapshot alongside the markdown, uses an upgraded Defuddle pipeline with better web-component handling and YouTube transcript extraction, and automatically falls back to the pre-Defuddle HTML-to-Markdown pipeline when needed. If local browser capture fails entirely, it can fall back to the hosted defuddle.md API. Supports two modes - auto-capture on page load, or wait for user signal (for pages requiring login). Use when user wants to save a webpage as markdown.
-version: 1.58.1
+version: 1.59.0
 metadata:
  openclaw:
    homepage: https://github.com/JimLiu/baoyu-skills#baoyu-url-to-markdown
@@ -30,6 +30,9 @@ Fetches any URL via Chrome CDP, saves the rendered HTML snapshot, and converts i
 |--------|---------|
 | `scripts/main.ts` | CLI entry point for URL fetching |
 | `scripts/html-to-markdown.ts` | Markdown conversion entry point and converter selection |
+| `scripts/parsers/index.ts` | Unified parser entry: dispatches URL-specific rules before generic converters |
+| `scripts/parsers/types.ts` | Unified parser interface shared by all rule files |
+| `scripts/parsers/rules/*.ts` | One file per URL rule, for example X status and X article |
 | `scripts/defuddle-converter.ts` | Defuddle-based conversion |
 | `scripts/legacy-converter.ts` | Pre-Defuddle legacy extraction and markdown conversion |
 | `scripts/markdown-conversion-shared.ts` | Shared metadata parsing and markdown document helpers |
@@ -115,10 +118,14 @@ Full reference: [references/config/first-time-setup.md](references/config/first-
 ## Features

 - Chrome CDP for full JavaScript rendering
+- Browser strategy fallback: default headless first, then visible Chrome on technical failure
+- URL-specific parser layer for sites that need custom HTML rules before generic extraction
 - Two capture modes: auto or wait-for-user
 - Save rendered HTML as a sibling `-captured.html` file
 - Clean markdown output with metadata
 - Upgraded Defuddle-first markdown conversion with automatic fallback to the pre-Defuddle extractor from git history
+- X/Twitter pages can use HTML-specific parsing for Tweets and Articles, which improves title/body/media extraction on `x.com` / `twitter.com`
+- `archive.ph` / related archive mirrors can restore the original URL from `input[name=q]` and prefer `#CONTENT` before falling back to the page body
 - Materializes shadow DOM content before conversion so web-component pages survive serialization better
 - YouTube pages can include transcript/caption text in the markdown when YouTube exposes a caption track
 - If local browser capture fails completely, can fall back to `defuddle.md/<url>` and still save markdown
@@ -131,6 +138,12 @@ Full reference: [references/config/first-time-setup.md](references/config/first-
 # Auto mode (default) - capture when page loads
 ${BUN_X} {baseDir}/scripts/main.ts <url>

+# Force headless only
+${BUN_X} {baseDir}/scripts/main.ts <url> --browser headless
+
+# Force visible browser
+${BUN_X} {baseDir}/scripts/main.ts <url> --browser headed
+
 # Wait mode - wait for user signal before capture
 ${BUN_X} {baseDir}/scripts/main.ts <url> --wait

@@ -152,6 +165,9 @@ ${BUN_X} {baseDir}/scripts/main.ts <url> --download-media
 | `-o <path>` | Output file path — must be a **file** path, not directory (default: auto-generated) |
 | `--output-dir <dir>` | Base output directory — auto-generates `{dir}/{domain}/{slug}.md` (default: `./url-to-markdown/`) |
 | `--wait` | Wait for user signal before capturing |
+| `--browser <mode>` | Browser strategy: `auto` (default), `headless`, or `headed` |
+| `--headless` | Shortcut for `--browser headless` |
+| `--headed` | Shortcut for `--browser headed` |
 | `--timeout <ms>` | Page load timeout (default: 30000) |
 | `--download-media` | Download image/video assets to local `imgs/` and `videos/`, and rewrite markdown links to local relative paths |

@@ -159,7 +175,7 @@ ${BUN_X} {baseDir}/scripts/main.ts <url> --download-media

 | Mode | Behavior | Use When |
 |------|----------|----------|
-| Auto (default) | Capture on network idle | Public pages, static content |
+| Auto (default) | Try headless first, then retry in visible Chrome if needed | Public pages, static content, unknown pages |
 | Wait (`--wait`) | User signals when ready | Login-required, lazy loading, paywalls |

 **Wait mode workflow**:
@@ -167,6 +183,43 @@ ${BUN_X} {baseDir}/scripts/main.ts <url> --download-media
 2. Ask user to confirm page is ready
 3. Send newline to stdin to trigger capture

+**Default browser fallback**:
+1. Auto mode starts with headless Chrome and captures on network idle
+2. If headless capture fails technically, retry with visible Chrome
+3. If a shared Chrome session for this profile already exists, reuse it instead of launching a new browser
+4. The script does not hard-code login or paywall detection; the agent must inspect the captured markdown or HTML and decide whether to rerun with `--browser headed --wait`
+
+## Agent Quality Gate
+
+**CRITICAL**: The agent must treat headless capture as provisional. Some sites render differently in headless mode and can silently return an error shell, partially hydrated page, or low-quality extraction **without** causing the CLI to fail.
+
+After every run that used `--browser auto` or `--browser headless`, the agent **MUST** inspect the saved markdown first, and inspect the saved `-captured.html` when the markdown looks suspicious.
+
+### Quality checks the agent must perform
+
+1. Confirm the markdown title matches the target page, not a generic site shell
+2. Confirm the body contains the expected article or page content, not just navigation, footer, or a generic error
+3. Watch for obvious failure signs such as:
+   - `Application error`
+   - `This page could not be found`
+   - login, signup, subscribe, or verification shells
+   - extremely short markdown for a page that should be long-form
+   - raw framework payloads or mostly boilerplate content
+4. If the result is low quality, incomplete, or clearly wrong, do **not** accept the run as successful just because the CLI exited with code 0
+
+### Recovery workflow the agent must follow
+
+1. First run with default `auto` unless there is already a clear reason to use wait mode
+2. Review markdown quality immediately after the run
+3. If the content is low quality, rerun locally with visible Chrome:
+   - `--browser headed` for ordinary rendering issues
+   - `--browser headed --wait` when the page may need login, anti-bot interaction, cookie acceptance, or extra hydration time
+4. If `--wait` is used, tell the user exactly what to do:
+   - if login is required, ask them to sign in
+   - if the page needs time to hydrate, ask them to wait until the full content is visible
+   - once ready, ask them to press Enter so capture can continue
+5. Only fall back to hosted `defuddle.md` after the local browser strategies have failed or are clearly lower fidelity
+
 ## Output Format

 Each run saves two files side by side:
@@ -201,14 +254,17 @@ When `--download-media` is enabled:

 Conversion order:

-1. Try Defuddle first
-2. For rich pages such as YouTube, prefer Defuddle's extractor-specific output (including transcripts when available) instead of replacing it with the legacy pipeline
-3. If Defuddle throws, cannot load, returns obviously incomplete markdown, or captures lower-quality content than the legacy pipeline, automatically fall back to the pre-Defuddle extractor
-4. If the entire local browser capture flow fails before markdown can be produced, try the hosted `https://defuddle.md/<url>` API and save its markdown output directly
-5. The legacy fallback path uses the older Readability/selector/Next.js-data based HTML-to-Markdown implementation recovered from git history
+1. Try the URL-specific parser layer first when a site rule matches
+2. If no specialized parser matches, try Defuddle
+3. For rich pages such as YouTube, prefer Defuddle's extractor-specific output (including transcripts when available) instead of replacing it with the legacy pipeline
+4. If Defuddle throws, cannot load, returns obviously incomplete markdown, or captures lower-quality content than the legacy pipeline, automatically fall back to the pre-Defuddle extractor
+5. If the agent determines the captured result is a login screen, verification screen, or paywall shell, rerun locally with `--browser headed --wait` and ask the user to complete access before capture
+6. If the entire local browser capture flow still fails before markdown can be produced, try the hosted `https://defuddle.md/<url>` API and save its markdown output directly
+7. The legacy fallback path uses the older Readability/selector/Next.js-data based HTML-to-Markdown implementation recovered from git history

 CLI output will show:

+- `Converter: parser:...` when a URL-specific parser succeeded
 - `Converter: defuddle` when Defuddle succeeds
 - `Converter: legacy:...` plus `Fallback used: ...` when fallback was needed
 - `Converter: defuddle-api` when local browser capture failed and the hosted API was used instead
@@ -6,7 +6,7 @@
      "dependencies": {
        "@mozilla/readability": "^0.6.0",
        "baoyu-chrome-cdp": "file:./vendor/baoyu-chrome-cdp",
-        "defuddle": "^0.12.0",
+        "defuddle": "^0.14.0",
        "jsdom": "^24.1.3",
        "linkedom": "^0.18.12",
        "turndown": "^7.2.2",
@@ -61,7 +61,7 @@

    "decimal.js": ["decimal.js@10.6.0", "", {}, "sha512-YpgQiITW3JXGntzdUmyUR1V812Hn8T1YVXhCu+wO3OpS4eU9l4YdD3qjyiKdV6mvV29zapkMeD390UVEf2lkUg=="],

-    "defuddle": ["defuddle@0.12.0", "", { "dependencies": { "commander": "^12.1.0" }, "optionalDependencies": { "mathml-to-latex": "^1.5.0", "temml": "^0.13.1", "turndown": "^7.2.0" }, "peerDependencies": { "jsdom": "^24.0.0" }, "bin": { "defuddle": "dist/cli.js" } }, "sha512-Y/WgyGKBxwxFir+hWNth4nmWDDDb8BzQi3qASS2NWYPXsKU42Ku49/3M5yFYefnRef9prynnmasfnXjk99EWgA=="],
+    "defuddle": ["defuddle@0.14.0", "", { "dependencies": { "commander": "^12.1.0" }, "optionalDependencies": { "linkedom": "^0.18.12", "mathml-to-latex": "^1.5.0", "temml": "^0.13.1", "turndown": "^7.2.0" }, "bin": { "defuddle": "dist/cli.js" } }, "sha512-btavZGd1WgiVqrVM62WGRXMUi/aU7ckTZiq0xXWLZMHvzIqNZjwIFQEDRx8MarD7fIgsB90NXZ9xHJkKtapt2Q=="],

    "delayed-stream": ["delayed-stream@1.0.0", "", {}, "sha512-ZySD7Nf91aLB0RxL4KGrKHBXl7Eds1DAmEdcoVawXnLD7SDhpNgtuII2aAkg7a7QS41jxPSZ17p4VdGnMHk3MQ=="],

@@ -0,0 +1,55 @@
+import assert from "node:assert/strict";
+import test from "node:test";
+
+import { cleanContent } from "./content-cleaner.js";
+
+const SAMPLE_HTML = `<!doctype html>
+<html>
+  <head>
+    <title>Example Story</title>
+    <style>.cookie-banner { position: fixed; }</style>
+    <script>window.__noise = true;</script>
+  </head>
+  <body>
+    <!-- comment that should be removed -->
+    <header>
+      <nav>
+        <a href="/home">Home</a>
+        <a href="/topics">Topics</a>
+      </nav>
+    </header>
+    <div class="cookie-banner">Accept cookies</div>
+    <aside>Sidebar links</aside>
+    <main>
+      <article class="content">
+        <h1>Actual Story Title</h1>
+        <p>
+          This is the first paragraph of the real story body, and it is intentionally long enough
+          to survive the cleaner's main-content heuristics without being mistaken for navigation.
+        </p>
+        <p>
+          This is the second paragraph with more useful detail, a
+          <a href="/read-more">supporting link</a>, and a normal image.
+        </p>
+        <img src="/images/cover.jpg" alt="Cover">
+        <img src="data:image/png;base64,AAAA" alt="Inline data">
+      </article>
+    </main>
+    <footer>Footer boilerplate</footer>
+  </body>
+</html>`;
+
+test("cleanContent keeps the article body and removes obvious boilerplate", () => {
+  const cleaned = cleanContent(SAMPLE_HTML, "https://example.com/posts/story");
+
+  assert.match(cleaned, /Actual Story Title/);
+  assert.match(cleaned, /https:\/\/example\.com\/read-more/);
+  assert.match(cleaned, /https:\/\/example\.com\/images\/cover\.jpg/);
+
+  assert.doesNotMatch(cleaned, /Accept cookies/);
+  assert.doesNotMatch(cleaned, /Sidebar links/);
+  assert.doesNotMatch(cleaned, /Footer boilerplate/);
+  assert.doesNotMatch(cleaned, /window\.__noise/);
+  assert.doesNotMatch(cleaned, /comment that should be removed/);
+  assert.doesNotMatch(cleaned, /data:image\/png;base64/);
+});
@@ -0,0 +1,432 @@
+import { parseHTML } from "linkedom";
+
+export interface CleaningOptions {
+  removeAds?: boolean;
+  removeBase64Images?: boolean;
+  onlyMainContent?: boolean;
+  includeTags?: string[];
+  excludeTags?: string[];
+}
+
+const ALWAYS_REMOVE_SELECTORS = [
+  "script",
+  "style",
+  "noscript",
+  "link[rel='stylesheet']",
+  "[hidden]",
+  "[aria-hidden='true']",
+  "[style*='display: none']",
+  "[style*='display:none']",
+  "[style*='visibility: hidden']",
+  "[style*='visibility:hidden']",
+  "svg[aria-hidden='true']",
+  "svg.icon",
+  "svg[class*='icon']",
+  "template",
+  "meta",
+  "iframe",
+  "canvas",
+  "object",
+  "embed",
+  "form",
+  "input",
+  "select",
+  "textarea",
+  "button",
+];
+
+const OVERLAY_SELECTORS = [
+  "[class*='modal']",
+  "[class*='popup']",
+  "[class*='overlay']",
+  "[class*='dialog']",
+  "[role='dialog']",
+  "[role='alertdialog']",
+  "[class*='cookie']",
+  "[class*='consent']",
+  "[class*='gdpr']",
+  "[class*='privacy-banner']",
+  "[class*='notification-bar']",
+  "[id*='cookie']",
+  "[id*='consent']",
+  "[id*='gdpr']",
+  "[style*='position: fixed']",
+  "[style*='position:fixed']",
+  "[style*='position: sticky']",
+  "[style*='position:sticky']",
+];
+
+const NAVIGATION_SELECTORS = [
+  "header",
+  "footer",
+  "nav",
+  "aside",
+  ".header",
+  ".top",
+  ".navbar",
+  "#header",
+  ".footer",
+  ".bottom",
+  "#footer",
+  ".sidebar",
+  ".side",
+  ".aside",
+  "#sidebar",
+  ".modal",
+  ".popup",
+  "#modal",
+  ".overlay",
+  ".ad",
+  ".ads",
+  ".advert",
+  "#ad",
+  ".lang-selector",
+  ".language",
+  "#language-selector",
+  ".social",
+  ".social-media",
+  ".social-links",
+  "#social",
+  ".menu",
+  ".navigation",
+  "#nav",
+  ".breadcrumbs",
+  "#breadcrumbs",
+  ".share",
+  "#share",
+  ".widget",
+  "#widget",
+  ".cookie",
+  "#cookie",
+];
+
+const FORCE_INCLUDE_SELECTORS = [
+  "#main",
+  "#content",
+  "#main-content",
+  "#article",
+  "#post",
+  "#page-content",
+  "main",
+  "article",
+  "[role='main']",
+  ".main-content",
+  ".content",
+  ".post-content",
+  ".article-content",
+  ".entry-content",
+  ".page-content",
+  ".article-body",
+  ".post-body",
+  ".story-content",
+  ".blog-content",
+];
+
+const AD_SELECTORS = [
+  "ins.adsbygoogle",
+  ".google-ad",
+  ".adsense",
+  "[data-ad]",
+  "[data-ads]",
+  "[data-ad-slot]",
+  "[data-ad-client]",
+  ".ad-container",
+  ".ad-wrapper",
+  ".advertisement",
+  ".sponsored-content",
+  "img[width='1'][height='1']",
+  "img[src*='pixel']",
+  "img[src*='tracking']",
+  "img[src*='analytics']",
+];
+
+function getLinkDensity(element: Element): number {
+  const text = element.textContent || "";
+  const textLength = text.trim().length;
+  if (textLength === 0) return 1;
+
+  let linkLength = 0;
+  element.querySelectorAll("a").forEach((link: Element) => {
+    linkLength += (link.textContent || "").trim().length;
+  });
+
+  return linkLength / textLength;
+}
+
+function getContentScore(element: Element): number {
+  let score = 0;
+  const text = element.textContent || "";
+  const textLength = text.trim().length;
+
+  score += Math.min(textLength / 100, 50);
+  score += element.querySelectorAll("p").length * 3;
+  score += element.querySelectorAll("h1, h2, h3, h4, h5, h6").length * 2;
+  score += element.querySelectorAll("img").length;
+
+  score -= element.querySelectorAll("a").length * 0.5;
+  score -= element.querySelectorAll("li").length * 0.2;
+
+  const linkDensity = getLinkDensity(element);
+  if (linkDensity > 0.5) score -= 30;
+  else if (linkDensity > 0.3) score -= 15;
+
+  const className = typeof element.className === "string" ? element.className : "";
+  const classAndId = `${className} ${element.id || ""}`;
+  if (/article|content|post|body|main|entry/i.test(classAndId)) score += 25;
+  if (/comment|sidebar|footer|nav|menu|header|widget|ad/i.test(classAndId)) score -= 25;
+
+  return score;
+}
+
+function looksLikeNavigation(element: Element): boolean {
+  const linkDensity = getLinkDensity(element);
+  if (linkDensity > 0.5) return true;
+
+  const listItems = element.querySelectorAll("li");
+  const links = element.querySelectorAll("a");
+  return listItems.length > 5 && links.length > listItems.length * 0.8;
+}
+
+function removeElements(document: Document, selectors: string[]): void {
+  for (const selector of selectors) {
+    try {
+      document.querySelectorAll(selector).forEach((element: Element) => element.remove());
+    } catch {
+      // Ignore unsupported selectors from linkedom/jsdom differences.
+    }
+  }
+}
+
+function removeWithProtection(
+  document: Document,
+  selectorsToRemove: string[],
+  protectedSelectors: string[]
+): void {
+  for (const selector of selectorsToRemove) {
+    try {
+      document.querySelectorAll(selector).forEach((element: Element) => {
+        const isProtected = protectedSelectors.some((protectedSelector) => {
+          try {
+            return element.matches(protectedSelector);
+          } catch {
+            return false;
+          }
+        });
+        if (isProtected) return;
+
+        const containsProtected = protectedSelectors.some((protectedSelector) => {
+          try {
+            return element.querySelector(protectedSelector) !== null;
+          } catch {
+            return false;
+          }
+        });
+        if (containsProtected) return;
+
+        element.remove();
+      });
+    } catch {
+      // Ignore unsupported selectors from linkedom/jsdom differences.
+    }
+  }
+}
+
+function findMainContent(document: Document): Element | null {
+  const isValidContent = (element: Element | null): element is Element => {
+    if (!element) return false;
+    const text = element.textContent || "";
+    if (text.trim().length < 100) return false;
+    return !looksLikeNavigation(element);
+  };
+
+  const main = document.querySelector("main");
+  if (isValidContent(main) && getLinkDensity(main) < 0.4) return main;
+
+  const roleMain = document.querySelector('[role="main"]');
+  if (isValidContent(roleMain) && getLinkDensity(roleMain) < 0.4) return roleMain;
+
+  const articles = document.querySelectorAll("article");
+  if (articles.length === 1 && isValidContent(articles[0] ?? null)) {
+    return articles[0] ?? null;
+  }
+
+  const contentSelectors = [
+    "#content",
+    "#main-content",
+    "#main",
+    ".content",
+    ".main-content",
+    ".post-content",
+    ".article-content",
+    ".entry-content",
+    ".page-content",
+    ".article-body",
+    ".post-body",
+    ".story-content",
+    ".blog-content",
+  ];
+
+  for (const selector of contentSelectors) {
+    try {
+      const element = document.querySelector(selector);
+      if (isValidContent(element) && getLinkDensity(element) < 0.4) {
+        return element;
+      }
+    } catch {
+      // Ignore invalid selectors.
+    }
+  }
+
+  const candidates: Array<{ element: Element; score: number }> = [];
+  const containers = document.querySelectorAll("div, section, article");
+  containers.forEach((element: Element) => {
+    const text = element.textContent || "";
+    if (text.trim().length < 200) return;
+
+    const score = getContentScore(element);
+    if (score > 0) {
+      candidates.push({ element, score });
+    }
+  });
+
+  candidates.sort((left, right) => right.score - left.score);
+  if ((candidates[0]?.score ?? 0) > 20) {
+    return candidates[0]?.element ?? null;
+  }
+
+  return null;
+}
+
+function removeBase64ImagesFromDocument(document: Document): void {
+  document.querySelectorAll("img[src^='data:']").forEach((element: Element) => {
+    element.remove();
+  });
+
+  document.querySelectorAll("[style*='data:image']").forEach((element: Element) => {
+    const style = element.getAttribute("style");
+    if (!style) return;
+
+    const cleanedStyle = style.replace(
+      /background(-image)?:\s*url\([^)]*data:image[^)]*\)[^;]*;?/gi,
+      ""
+    );
+
+    if (cleanedStyle.trim()) {
+      element.setAttribute("style", cleanedStyle);
+    } else {
+      element.removeAttribute("style");
+    }
+  });
+
+  document.querySelectorAll("source[src^='data:'], source[srcset*='data:']").forEach((element: Element) => {
+    element.remove();
+  });
+}
+
+function makeAbsoluteUrl(value: string, baseUrl: string): string | null {
+  try {
+    return new URL(value, baseUrl).toString();
+  } catch {
+    return null;
+  }
+}
+
+function convertRelativeUrls(document: Document, baseUrl: string): void {
+  document.querySelectorAll("[src]").forEach((element: Element) => {
+    const src = element.getAttribute("src");
+    if (!src || src.startsWith("http") || src.startsWith("//") || src.startsWith("data:")) return;
+
+    const absolute = makeAbsoluteUrl(src, baseUrl);
+    if (absolute) element.setAttribute("src", absolute);
+  });
+
+  document.querySelectorAll("[href]").forEach((element: Element) => {
+    const href = element.getAttribute("href");
+    if (
+      !href ||
+      href.startsWith("http") ||
+      href.startsWith("//") ||
+      href.startsWith("#") ||
+      href.startsWith("mailto:") ||
+      href.startsWith("tel:") ||
+      href.startsWith("javascript:")
+    ) {
+      return;
+    }
+
+    const absolute = makeAbsoluteUrl(href, baseUrl);
+    if (absolute) element.setAttribute("href", absolute);
+  });
+}
+
+export function cleanHtml(html: string, baseUrl: string, options: CleaningOptions = {}): string {
+  const {
+    removeAds = true,
+    removeBase64Images = true,
+    onlyMainContent = true,
+    includeTags,
+    excludeTags,
+  } = options;
+
+  const { document } = parseHTML(html);
+
+  removeElements(document, ALWAYS_REMOVE_SELECTORS);
+  removeElements(document, OVERLAY_SELECTORS);
+
+  if (removeAds) {
+    removeElements(document, AD_SELECTORS);
+  }
+
+  if (excludeTags?.length) {
+    removeElements(document, excludeTags);
+  }
+
+  if (onlyMainContent) {
+    removeWithProtection(document, NAVIGATION_SELECTORS, FORCE_INCLUDE_SELECTORS);
+
+    const mainContent = findMainContent(document);
+    if (mainContent && document.body) {
+      const clone = mainContent.cloneNode(true) as Element;
+      document.body.innerHTML = "";
+      document.body.appendChild(clone);
+    }
+  }
+
+  if (includeTags?.length && document.body) {
+    const matchedElements: Element[] = [];
+
+    for (const selector of includeTags) {
+      try {
+        document.querySelectorAll(selector).forEach((element: Element) => {
+          matchedElements.push(element.cloneNode(true) as Element);
+        });
+      } catch {
+        // Ignore invalid selectors.
+      }
+    }
+
+    if (matchedElements.length > 0) {
+      document.body.innerHTML = "";
+      matchedElements.forEach((element) => document.body?.appendChild(element));
+    }
+  }
+
+  if (removeBase64Images) {
+    removeBase64ImagesFromDocument(document);
+  }
+
+  const walker = document.createTreeWalker(document, 128);
+  const comments: Node[] = [];
+  while (walker.nextNode()) {
+    comments.push(walker.currentNode);
+  }
+  comments.forEach((comment) => comment.parentNode?.removeChild(comment));
+
+  convertRelativeUrls(document, baseUrl);
+
+  return document.documentElement?.outerHTML || html;
+}
+
+export function cleanContent(html: string, baseUrl: string, options: CleaningOptions = {}): string {
+  return cleanHtml(html, baseUrl, options);
+}
@@ -0,0 +1,28 @@
+import assert from "node:assert/strict";
+import test from "node:test";
+
+import { extractContent } from "./html-to-markdown.js";
+
+const EMBEDDED_IMAGE_HTML = `<!doctype html>
+<html>
+  <body>
+    <main>
+      <article>
+        <h1>Embedded Image Story</h1>
+        <p>
+          This paragraph is intentionally long enough to satisfy the extractor thresholds so the
+          resulting markdown keeps the main article body and the embedded image reference.
+        </p>
+        <img src="data:image/png;base64,AAAA" alt="inline">
+      </article>
+    </main>
+  </body>
+</html>`;
+
+test("extractContent preserves base64 images when requested for media download", async () => {
+  const result = await extractContent(EMBEDDED_IMAGE_HTML, "https://example.com/embedded", {
+    preserveBase64Images: true,
+  });
+
+  assert.match(result.markdown, /!\[inline\]\(data:image\/png;base64,AAAA\)/);
+});
@@ -12,10 +12,16 @@ import {
  scoreMarkdownQuality,
  shouldCompareWithLegacy,
 } from "./legacy-converter.js";
+import { tryUrlRuleParsers } from "./parsers/index.js";
+import { cleanContent } from "./content-cleaner.js";

 export type { ConversionResult, PageMetadata };
 export { createMarkdownDocument, formatMetadataYaml };

+export interface ExtractContentOptions {
+  preserveBase64Images?: boolean;
+}
+
 export const absolutizeUrlsScript = String.raw`
 (function() {
  const baseUrl = document.baseURI || location.href;
@@ -84,7 +90,10 @@ export const absolutizeUrlsScript = String.raw`
  absAttr(htmlClone, "video[poster]", "poster");
  absSrcset(htmlClone, "img[srcset], source[srcset]");

-  return { html: "<!doctype html>\n" + htmlClone.outerHTML };
+  return {
+    html: "<!doctype html>\n" + htmlClone.outerHTML,
+    finalUrl: location.href,
+  };
 })()
 `;

@@ -101,18 +110,36 @@ function shouldPreferDefuddle(result: ConversionResult): boolean {
  return /^##?\s+transcript\b/im.test(result.markdown);
 }

-export async function extractContent(html: string, url: string): Promise<ConversionResult> {
+export async function extractContent(
+  html: string,
+  url: string,
+  options: ExtractContentOptions = {}
+): Promise<ConversionResult> {
  const capturedAt = new Date().toISOString();
  const baseMetadata = extractMetadataFromHtml(html, url, capturedAt);

-  const defuddleResult = await tryDefuddleConversion(html, url, baseMetadata);
+  const specializedResult = tryUrlRuleParsers(html, url, baseMetadata);
+  if (specializedResult) {
+    return specializedResult;
+  }
+
+  let cleanedHtml = html;
+  try {
+    cleanedHtml = cleanContent(html, url, {
+      removeBase64Images: !options.preserveBase64Images,
+    });
+  } catch {
+    cleanedHtml = html;
+  }
+
+  const defuddleResult = await tryDefuddleConversion(cleanedHtml, url, baseMetadata);
  if (defuddleResult.ok) {
    if (shouldPreferDefuddle(defuddleResult.result)) {
-      return defuddleResult.result;
+      return { ...defuddleResult.result, rawHtml: html };
    }

    if (shouldCompareWithLegacy(defuddleResult.result.markdown)) {
-      const legacyResult = convertWithLegacyExtractor(html, baseMetadata);
+      const legacyResult = convertWithLegacyExtractor(html, baseMetadata, cleanedHtml);
      const legacyScore = scoreMarkdownQuality(legacyResult.markdown);
      const defuddleScore = scoreMarkdownQuality(defuddleResult.result.markdown);

@@ -124,10 +151,10 @@ export async function extractContent(html: string, url: string): Promise<Convers
      }
    }

-    return defuddleResult.result;
+    return { ...defuddleResult.result, rawHtml: html };
  }

-  const fallbackResult = convertWithLegacyExtractor(html, baseMetadata);
+  const fallbackResult = convertWithLegacyExtractor(html, baseMetadata, cleanedHtml);
  return {
    ...fallbackResult,
    fallbackReason: defuddleResult.reason,
@@ -0,0 +1,48 @@
+import assert from "node:assert/strict";
+import test from "node:test";
+
+import { cleanContent } from "./content-cleaner.js";
+import { convertWithLegacyExtractor } from "./legacy-converter.js";
+import { extractMetadataFromHtml } from "./markdown-conversion-shared.js";
+
+const CAPTURED_AT = "2026-03-24T03:00:00.000Z";
+
+const NEXT_DATA_HTML = `<!doctype html>
+<html>
+  <head>
+    <title>Hydrated Story</title>
+  </head>
+  <body>
+    <div class="cookie-banner">Accept cookies</div>
+    <main>
+      <p>Short teaser text that should not win over the structured article payload.</p>
+    </main>
+    <script id="__NEXT_DATA__" type="application/json">
+      {
+        "props": {
+          "pageProps": {
+            "article": {
+              "title": "Hydrated Story",
+              "description": "A structured article payload from Next.js",
+              "body": "<p>The full article lives in __NEXT_DATA__ and should still be extracted even when the cleaned HTML removes scripts before the selector and readability passes run.</p><p>A second paragraph keeps the content comfortably above the minimum extraction threshold and proves the legacy extractor still has access to the original structured payload.</p>"
+            }
+          }
+        }
+      }
+    </script>
+  </body>
+</html>`;
+
+test("legacy extractor still uses original __NEXT_DATA__ after HTML cleaning", () => {
+  const url = "https://example.com/posts/hydrated-story";
+  const baseMetadata = extractMetadataFromHtml(NEXT_DATA_HTML, url, CAPTURED_AT);
+  const cleanedHtml = cleanContent(NEXT_DATA_HTML, url);
+
+  const result = convertWithLegacyExtractor(NEXT_DATA_HTML, baseMetadata, cleanedHtml);
+
+  assert.equal(result.conversionMethod, "legacy:next-data");
+  assert.match(result.markdown, /The full article lives in .*NEXT.*DATA/);
+  assert.match(result.markdown, /A second paragraph keeps the content comfortably above the minimum extraction threshold/);
+  assert.doesNotMatch(result.markdown, /Short teaser text that should not win/);
+  assert.equal(result.rawHtml, NEXT_DATA_HTML);
+});
@@ -336,29 +336,32 @@ function tryNextDataExtraction(document: Document): ExtractionCandidate | null {

 function buildReadabilityCandidate(
  article: ReturnType<Readability["parse"]>,
-  document: Document,
+  referenceDocument: Document,
  method: string
 ): ExtractionCandidate | null {
  const textContent = article?.textContent?.trim() ?? "";
  if (textContent.length < MIN_CONTENT_LENGTH) return null;

  return {
-    title: pickString(article?.title, extractTitle(document)),
+    title: pickString(article?.title, extractTitle(referenceDocument)),
    byline: pickString((article as { byline?: string } | null)?.byline),
    excerpt: pickString(article?.excerpt, generateExcerpt(null, textContent)),
-    published: pickString((article as { publishedTime?: string } | null)?.publishedTime, extractPublishedTime(document)),
+    published: pickString(
+      (article as { publishedTime?: string } | null)?.publishedTime,
+      extractPublishedTime(referenceDocument)
+    ),
    html: article?.content ? sanitizeHtml(article.content) : null,
    textContent,
    method,
  };
 }

-function tryReadability(document: Document): ExtractionCandidate | null {
+function tryReadability(document: Document, referenceDocument: Document = document): ExtractionCandidate | null {
  try {
    const strictClone = document.cloneNode(true) as Document;
    const strictResult = buildReadabilityCandidate(
      new Readability(strictClone).parse(),
-      document,
+      referenceDocument,
      "readability"
    );
    if (strictResult) return strictResult;
@@ -366,7 +369,7 @@ function tryReadability(document: Document): ExtractionCandidate | null {
    const relaxedClone = document.cloneNode(true) as Document;
    return buildReadabilityCandidate(
      new Readability(relaxedClone, { charThreshold: 120 }).parse(),
-      document,
+      referenceDocument,
      "readability-relaxed"
    );
  } catch {
@@ -471,14 +474,15 @@ function pickBestCandidate(candidates: ExtractionCandidate[]): ExtractionCandida
  return ranked[0];
 }

-function extractFromHtml(html: string): ExtractionCandidate | null {
-  const document = parseDocument(html);
+function extractFromHtml(html: string, cleanedHtml: string = html): ExtractionCandidate | null {
+  const originalDocument = parseDocument(html);
+  const cleanedDocument = parseDocument(cleanedHtml);

-  const readabilityCandidate = tryReadability(document);
-  const nextDataCandidate = tryNextDataExtraction(document);
-  const jsonLdCandidate = tryJsonLdExtraction(document);
-  const selectorCandidate = trySelectorExtraction(document);
-  const bodyCandidate = tryBodyExtraction(document);
+  const readabilityCandidate = tryReadability(cleanedDocument, originalDocument);
+  const nextDataCandidate = tryNextDataExtraction(originalDocument);
+  const jsonLdCandidate = tryJsonLdExtraction(originalDocument);
+  const selectorCandidate = trySelectorExtraction(cleanedDocument);
+  const bodyCandidate = tryBodyExtraction(cleanedDocument);

  const candidates = [
    readabilityCandidate,
@@ -493,8 +497,8 @@ function extractFromHtml(html: string): ExtractionCandidate | null {

  return {
    ...winner,
-    title: winner.title ?? extractTitle(document),
-    published: winner.published ?? extractPublishedTime(document),
+    title: winner.title ?? extractTitle(originalDocument),
+    published: winner.published ?? extractPublishedTime(originalDocument),
    excerpt: winner.excerpt ?? generateExcerpt(null, winner.textContent),
  };
 }
@@ -521,14 +525,18 @@ turndown.addRule("collapseFigure", {

 turndown.addRule("dropInvisibleAnchors", {
  filter(node) {
-    return node.nodeName === "A" && !(node as Element).textContent?.trim();
+    return (
+      node.nodeName === "A" &&
+      !(node as Element).textContent?.trim() &&
+      !(node as Element).querySelector("img, video, picture, source")
+    );
  },
  replacement() {
    return "";
  },
 });

-function convertHtmlToMarkdown(html: string): string {
+export function convertHtmlFragmentToMarkdown(html: string): string {
  if (!html || !html.trim()) return "";

  try {
@@ -606,12 +614,16 @@ export function shouldCompareWithLegacy(markdown: string): boolean {
  );
 }

-export function convertWithLegacyExtractor(html: string, baseMetadata: PageMetadata): ConversionResult {
-  const extracted = extractFromHtml(html);
+export function convertWithLegacyExtractor(
+  html: string,
+  baseMetadata: PageMetadata,
+  cleanedHtml: string = html
+): ConversionResult {
+  const extracted = extractFromHtml(html, cleanedHtml);

-  let markdown = extracted?.html ? convertHtmlToMarkdown(extracted.html) : "";
+  let markdown = extracted?.html ? convertHtmlFragmentToMarkdown(extracted.html) : "";
  if (!markdown.trim()) {
-    markdown = extracted?.textContent?.trim() || fallbackPlainText(html);
+    markdown = extracted?.textContent?.trim() || fallbackPlainText(cleanedHtml);
  }

  return {
@@ -29,10 +29,33 @@ interface Args {
  wait: boolean;
  timeout: number;
  downloadMedia: boolean;
+  browserMode: BrowserMode;
 }

+type BrowserMode = "auto" | "headless" | "headed";
+
+interface CaptureAttemptOptions {
+  headless: boolean;
+  wait: boolean;
+  existingPort?: number;
+  waitPrompt?: string;
+}
+
+interface CaptureSnapshot {
+  html: string;
+  finalUrl: string;
+}
+
+const BROWSER_MODES = new Set<BrowserMode>(["auto", "headless", "headed"]);
+
 function parseArgs(argv: string[]): Args {
-  const args: Args = { url: "", wait: false, timeout: DEFAULT_TIMEOUT_MS, downloadMedia: false };
+  const args: Args = {
+    url: "",
+    wait: false,
+    timeout: DEFAULT_TIMEOUT_MS,
+    downloadMedia: false,
+    browserMode: "auto",
+  };
  for (let i = 2; i < argv.length; i++) {
    const arg = argv[i];
    if (arg === "--wait" || arg === "-w") {
@@ -45,6 +68,12 @@ function parseArgs(argv: string[]): Args {
      args.outputDir = argv[++i];
    } else if (arg === "--download-media") {
      args.downloadMedia = true;
+    } else if (arg === "--browser") {
+      args.browserMode = (argv[++i] as BrowserMode | undefined) ?? "auto";
+    } else if (arg === "--headless") {
+      args.browserMode = "headless";
+    } else if (arg === "--headed" || arg === "--noheadless" || arg === "--no-headless") {
+      args.browserMode = "headed";
    } else if (!arg.startsWith("-") && !args.url) {
      args.url = arg;
    }
@@ -52,15 +81,71 @@ function parseArgs(argv: string[]): Args {
  return args;
 }

-function generateSlug(title: string, url: string): string {
-  const text = title || new URL(url).pathname.replace(/\//g, "-");
-  return text
+const SLUG_STOP_WORDS = new Set([
+  "the", "a", "an", "is", "are", "was", "were", "be", "been", "being",
+  "have", "has", "had", "do", "does", "did", "will", "would", "shall",
+  "should", "may", "might", "must", "can", "could", "to", "of", "in",
+  "for", "on", "with", "at", "by", "from", "as", "into", "through",
+  "during", "before", "after", "above", "below", "between", "out",
+  "off", "over", "under", "again", "further", "then", "once", "here",
+  "there", "when", "where", "why", "how", "all", "both", "each",
+  "few", "more", "most", "other", "some", "such", "no", "nor", "not",
+  "only", "own", "same", "so", "than", "too", "very", "just", "but",
+  "and", "or", "if", "this", "that", "these", "those", "it", "its",
+  "http", "https", "www", "com", "org", "net", "post", "article",
+]);
+
+function extractSlugFromContent(content: string): string | null {
+  const body = content.replace(/^---\n[\s\S]*?\n---\n?/, "").slice(0, 1000);
+  const words = body
+    .replace(/[^\w\s-]/g, " ")
+    .split(/\s+/)
+    .filter((w) => /^[a-zA-Z]/.test(w) && w.length >= 2 && !SLUG_STOP_WORDS.has(w.toLowerCase()))
+    .map((w) => w.toLowerCase());
+
+  const unique: string[] = [];
+  const seen = new Set<string>();
+  for (const w of words) {
+    if (!seen.has(w)) {
+      seen.add(w);
+      unique.push(w);
+      if (unique.length >= 6) break;
+    }
+  }
+  return unique.length >= 2 ? unique.join("-").slice(0, 50) : null;
+}
+
+function generateSlug(title: string, url: string, content?: string): string {
+  const asciiWords = title
+    .replace(/[^\w\s]/g, " ")
+    .split(/\s+/)
+    .filter((w) => /[a-zA-Z]/.test(w) && w.length >= 2 && !SLUG_STOP_WORDS.has(w.toLowerCase()))
+    .map((w) => w.toLowerCase());
+
+  if (asciiWords.length >= 2) {
+    return asciiWords.slice(0, 6).join("-").slice(0, 50);
+  }
+
+  if (content) {
+    const contentSlug = extractSlugFromContent(content);
+    if (contentSlug) return contentSlug;
+  }
+
+  const GENERIC_PATH_SEGMENTS = new Set(["status", "article", "post", "posts", "p", "blog", "news", "articles"]);
+  const parsed = new URL(url);
+  const pathSlug = parsed.pathname
+    .split("/")
+    .filter((s) => s.length > 0 && !/^\d{10,}$/.test(s) && !GENERIC_PATH_SEGMENTS.has(s.toLowerCase()))
+    .join("-")
    .toLowerCase()
-    .replace(/[^\w\s-]/g, "")
-    .replace(/\s+/g, "-")
+    .replace(/[^\w-]/g, "-")
    .replace(/-+/g, "-")
    .replace(/^-|-$/g, "")
-    .slice(0, 50) || "page";
+    .slice(0, 40);
+
+  const prefix = asciiWords.slice(0, 2).join("-");
+  const combined = prefix ? `${prefix}-${pathSlug}` : pathSlug;
+  return combined.slice(0, 50) || "page";
 }

 function formatTimestamp(): string {
@@ -124,35 +209,42 @@ async function fetchDefuddleApiMarkdown(targetUrl: string): Promise<{ markdown:
  };
 }

-async function generateOutputPath(url: string, title: string, outputDir?: string): Promise<string> {
+async function generateOutputPath(url: string, title: string, outputDir?: string, content?: string): Promise<string> {
  const domain = new URL(url).hostname.replace(/^www\./, "");
-  const slug = generateSlug(title, url);
+  const slug = generateSlug(title, url, content);
  const dataDir = outputDir ? path.resolve(outputDir) : resolveUrlToMarkdownDataDir();
-  const basePath = path.join(dataDir, domain, `${slug}.md`);
+  const basePath = path.join(dataDir, domain, slug, `${slug}.md`);

  if (!(await fileExists(basePath))) {
    return basePath;
  }

  const timestampSlug = `${slug}-${formatTimestamp()}`;
-  return path.join(dataDir, domain, `${timestampSlug}.md`);
+  return path.join(dataDir, domain, timestampSlug, `${timestampSlug}.md`);
 }

-async function waitForUserSignal(): Promise<void> {
-  console.log("Page opened. Press Enter when ready to capture...");
+function defaultWaitPrompt(): string {
+  return "A browser window has been opened. If the page requires login or verification, complete it first, then press Enter to capture.";
+}
+
+async function waitForUserSignal(prompt: string): Promise<void> {
+  console.log(prompt);
  const rl = createInterface({ input: process.stdin, output: process.stdout });
  await new Promise<void>((resolve) => {
    rl.once("line", () => { rl.close(); resolve(); });
  });
 }

-async function captureUrl(args: Args): Promise<ConversionResult> {
-  const existingPort = await findExistingChromePort();
-  const reusing = existingPort !== null;
-  const port = existingPort ?? await getFreePort();
-  const chrome = reusing ? null : await launchChrome(args.url, port, false);
+async function captureUrlOnce(args: Args, options: CaptureAttemptOptions): Promise<ConversionResult> {
+  const reusing = options.existingPort !== undefined;
+  const port = options.existingPort ?? await getFreePort();
+  const chrome = reusing ? null : await launchChrome(args.url, port, options.headless);

-  if (reusing) console.log(`Reusing existing Chrome on port ${port}`);
+  if (reusing) {
+    console.log(`Reusing existing Chrome on port ${port}`);
+  } else {
+    console.log(`Launching Chrome (${options.headless ? "headless" : "headed"})...`);
+  }

  let cdp: CdpConnection | null = null;
  let targetId: string | null = null;
@@ -179,8 +271,8 @@ async function captureUrl(args: Args): Promise<ConversionResult> {
      await cdp.send("Page.enable", {}, { sessionId });
    }

-    if (args.wait) {
-      await waitForUserSignal();
+    if (options.wait) {
+      await waitForUserSignal(options.waitPrompt ?? defaultWaitPrompt());
    } else {
      console.log("Waiting for page to load...");
      await Promise.race([
@@ -195,11 +287,12 @@ async function captureUrl(args: Args): Promise<ConversionResult> {
    }

    console.log("Capturing page content...");
-    const { html } = await evaluateScript<{ html: string }>(
+    const snapshot = await evaluateScript<CaptureSnapshot>(
      cdp, sessionId, absolutizeUrlsScript, args.timeout
    );
-
-    return await extractContent(html, args.url);
+    return await extractContent(snapshot.html, snapshot.finalUrl || args.url, {
+      preserveBase64Images: args.downloadMedia,
+    });
  } finally {
    if (reusing) {
      if (cdp && targetId) {
@@ -216,10 +309,67 @@ async function captureUrl(args: Args): Promise<ConversionResult> {
  }
 }

+async function runHeadedFlow(
+  args: Args,
+  options: { existingPort?: number; wait: boolean; waitPrompt?: string }
+): Promise<ConversionResult> {
+  return await captureUrlOnce(args, {
+    headless: false,
+    wait: options.wait,
+    existingPort: options.existingPort,
+    waitPrompt: options.waitPrompt,
+  });
+}
+
+async function captureUrl(args: Args): Promise<ConversionResult> {
+  const existingPort = await findExistingChromePort();
+  if (existingPort !== null) {
+    console.log("Found an existing Chrome session for this profile. Reusing it instead of launching a new browser.");
+    return await runHeadedFlow(args, {
+      existingPort,
+      wait: args.wait,
+      waitPrompt: args.wait ? defaultWaitPrompt() : undefined,
+    });
+  }
+
+  if (args.browserMode === "headless") {
+    return await captureUrlOnce(args, { headless: true, wait: false });
+  }
+
+  if (args.browserMode === "headed") {
+    return await runHeadedFlow(args, {
+      wait: args.wait,
+      waitPrompt: args.wait ? defaultWaitPrompt() : undefined,
+    });
+  }
+
+  if (args.wait) {
+    return await runHeadedFlow(args, {
+      wait: true,
+      waitPrompt: defaultWaitPrompt(),
+    });
+  }
+
+  try {
+    return await captureUrlOnce(args, { headless: true, wait: false });
+  } catch (error) {
+    const headlessMessage = error instanceof Error ? error.message : String(error);
+    console.warn(`Headless capture failed: ${headlessMessage}`);
+    console.log("Retrying with a visible browser window...");
+
+    try {
+      return await runHeadedFlow(args, { wait: false });
+    } catch (headedError) {
+      const headedMessage = headedError instanceof Error ? headedError.message : String(headedError);
+      throw new Error(`Headless capture failed (${headlessMessage}); headed retry failed (${headedMessage})`);
+    }
+  }
+}
+
 async function main(): Promise<void> {
  const args = parseArgs(process.argv);
  if (!args.url) {
-    console.error("Usage: bun main.ts <url> [-o output.md] [--output-dir dir] [--wait] [--timeout ms] [--download-media]");
+    console.error("Usage: bun main.ts <url> [-o output.md] [--output-dir dir] [--wait] [--browser auto|headless|headed] [--timeout ms] [--download-media]");
    process.exit(1);
  }

@@ -230,6 +380,16 @@ async function main(): Promise<void> {
    process.exit(1);
  }

+  if (!BROWSER_MODES.has(args.browserMode)) {
+    console.error(`Invalid --browser mode: ${args.browserMode}. Expected auto, headless, or headed.`);
+    process.exit(1);
+  }
+
+  if (args.wait && args.browserMode === "headless") {
+    console.error("Error: --wait requires a visible browser. Use --browser auto or --browser headed.");
+    process.exit(1);
+  }
+
  if (args.output) {
    const stat = await import("node:fs").then(fs => fs.statSync(args.output!, { throwIfNoEntry: false }));
    if (stat?.isDirectory()) {
@@ -240,6 +400,7 @@ async function main(): Promise<void> {

  console.log(`Fetching: ${args.url}`);
  console.log(`Mode: ${args.wait ? "wait" : "auto"}`);
+  console.log(`Browser: ${args.browserMode}`);

  let outputPath: string;
  let htmlSnapshotPath: string | null = null;
@@ -249,13 +410,12 @@ async function main(): Promise<void> {

  try {
    const result = await captureUrl(args);
-    outputPath = args.output || await generateOutputPath(args.url, result.metadata.title, args.outputDir);
+    document = createMarkdownDocument(result);
+    outputPath = args.output || await generateOutputPath(result.metadata.url || args.url, result.metadata.title, args.outputDir, document);
    const outputDir = path.dirname(outputPath);
    htmlSnapshotPath = deriveHtmlSnapshotPath(outputPath);
    await mkdir(outputDir, { recursive: true });
    await writeFile(htmlSnapshotPath, result.rawHtml, "utf-8");
-
-    document = createMarkdownDocument(result);
    conversionMethod = result.conversionMethod;
    fallbackReason = result.fallbackReason;
  } catch (error) {
@@ -265,10 +425,9 @@ async function main(): Promise<void> {

    try {
      const remoteResult = await fetchDefuddleApiMarkdown(args.url);
-      outputPath = args.output || await generateOutputPath(args.url, remoteResult.title, args.outputDir);
-      await mkdir(path.dirname(outputPath), { recursive: true });
-
      document = remoteResult.markdown;
+      outputPath = args.output || await generateOutputPath(args.url, remoteResult.title, args.outputDir, document);
+      await mkdir(path.dirname(outputPath), { recursive: true });
      conversionMethod = "defuddle-api";
      fallbackReason = `Local browser capture failed: ${primaryError}`;
    } catch (remoteError) {
@@ -300,6 +300,24 @@ export function createMarkdownDocument(result: ConversionResult): string {
  const escapedTitle = result.metadata.title.replace(/[.*+?^${}()|[\]\\]/g, "\\$&");
  const titleRegex = new RegExp(`^#\\s+${escapedTitle}\\s*(\\n|$)`, "i");
  const hasTitle = titleRegex.test(result.markdown.trimStart());
-  const title = result.metadata.title && !hasTitle ? `\n\n# ${result.metadata.title}\n\n` : "\n\n";
+  const firstMeaningfulLine = result.markdown
+    .replace(/\r\n/g, "\n")
+    .split("\n")
+    .map((line) => line.trim())
+    .find((line) => line && !/^!?\[[^\]]*\]\([^)]+\)$/.test(line))
+    ?.replace(/^>\s*/, "")
+    ?.replace(/^#+\s+/, "")
+    ?.trim();
+  const comparableTitle = result.metadata.title.toLowerCase().replace(/(?:\.{3}|…)\s*$/, "");
+  const comparableFirstLine = firstMeaningfulLine?.toLowerCase() ?? "";
+  const titleRepeatsContent =
+    comparableTitle !== "" &&
+    comparableFirstLine !== "" &&
+    (comparableFirstLine === comparableTitle ||
+      comparableFirstLine.startsWith(comparableTitle) ||
+      comparableTitle.startsWith(comparableFirstLine));
+  const title = result.metadata.title && !hasTitle && !titleRepeatsContent
+    ? `\n\n# ${result.metadata.title}\n\n`
+    : "\n\n";
  return yaml + title + result.markdown;
 }
@@ -0,0 +1,40 @@
+import assert from "node:assert/strict";
+import { mkdtemp, readFile, readdir } from "node:fs/promises";
+import os from "node:os";
+import path from "node:path";
+import test from "node:test";
+
+import { localizeMarkdownMedia } from "./media-localizer.js";
+
+const PNG_1X1_BASE64 =
+  "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mP8/x8AAwMCAO7Z0ioAAAAASUVORK5CYII=";
+
+test("localizeMarkdownMedia saves embedded base64 images into imgs directory", async () => {
+  const tempDir = await mkdtemp(path.join(os.tmpdir(), "url-to-markdown-media-"));
+  const dataUri = `data:image/png;base64,${PNG_1X1_BASE64}`;
+  const markdown = [
+    "---",
+    `coverImage: "${dataUri}"`,
+    "---",
+    "",
+    "# Embedded Image",
+    "",
+    `![inline](${dataUri})`,
+    "",
+  ].join("\n");
+
+  const result = await localizeMarkdownMedia(markdown, {
+    markdownPath: path.join(tempDir, "post.md"),
+  });
+
+  assert.equal(result.downloadedImages, 1);
+  assert.equal(result.downloadedVideos, 0);
+  assert.match(result.markdown, /coverImage: "imgs\/img-001\.png"/);
+  assert.match(result.markdown, /!\[inline\]\(imgs\/img-001\.png\)/);
+
+  const files = await readdir(path.join(tempDir, "imgs"));
+  assert.deepEqual(files, ["img-001.png"]);
+
+  const bytes = await readFile(path.join(tempDir, "imgs", "img-001.png"));
+  assert.equal(bytes.length, Buffer.from(PNG_1X1_BASE64, "base64").length);
+});
@@ -3,10 +3,12 @@ import { mkdir, writeFile } from "node:fs/promises";

 type MediaKind = "image" | "video";
 type MediaHint = "image" | "unknown";
+type MediaSource = "remote" | "data";

 type MarkdownLinkCandidate = {
  url: string;
  hint: MediaHint;
+  source: MediaSource;
 };

 export type LocalizeMarkdownMediaOptions = {
@@ -22,8 +24,9 @@ export type LocalizeMarkdownMediaResult = {
  videoDir: string | null;
 };

-const MARKDOWN_LINK_RE = /(!?\[[^\]\n]*\])\((<)?(https?:\/\/[^)\s>]+)(>)?\)/g;
-const FRONTMATTER_COVER_RE = /^(coverImage:\s*")(https?:\/\/[^"]+)(")/m;
+const MARKDOWN_LINK_RE =
+  /(!?\[[^\]\n]*\])\((<)?((?:https?:\/\/[^)\s>]+)|(?:data:[^)>\s]+))(>)?\)/g;
+const FRONTMATTER_COVER_RE = /^(coverImage:\s*")((?:https?:\/\/[^"]+)|(?:data:[^"]+))(")/m;

 const IMAGE_EXTENSIONS = new Set([
  "jpg",
@@ -86,6 +89,10 @@ function resolveExtensionFromUrl(rawUrl: string): string | undefined {
  return undefined;
 }

+function resolveExtensionFromContentType(contentType: string): string | undefined {
+  return normalizeExtension(MIME_EXTENSION_MAP[contentType]);
+}
+
 function resolveKindFromContentType(contentType: string): MediaKind | undefined {
  if (!contentType) return undefined;
  if (contentType.startsWith("image/")) return "image";
@@ -124,7 +131,7 @@ function resolveOutputExtension(
  extension: string | undefined,
  kind: MediaKind
 ): string {
-  const extFromMime = normalizeExtension(MIME_EXTENSION_MAP[contentType]);
+  const extFromMime = resolveExtensionFromContentType(contentType);
  if (extFromMime) return extFromMime;

  const normalizedExt = normalizeExtension(extension);
@@ -150,6 +157,10 @@ function sanitizeFileSegment(input: string): string {
 }

 function resolveFileStem(rawUrl: string, extension: string): string {
+  if (isDataUri(rawUrl)) {
+    return "";
+  }
+
  try {
    const parsed = new URL(rawUrl);
    const base = path.posix.basename(parsed.pathname);
@@ -172,6 +183,26 @@ function buildFileName(kind: MediaKind, index: number, sourceUrl: string, extens
  return `${prefix}-${serial}${suffix}.${extension}`;
 }

+function isDataUri(value: string): boolean {
+  return value.startsWith("data:");
+}
+
+function parseBase64DataUri(rawUrl: string): { contentType: string; bytes: Buffer } | null {
+  const match = rawUrl.match(/^data:([^;,]+);base64,([A-Za-z0-9+/=\s]+)$/i);
+  if (!match?.[1] || !match[2]) return null;
+
+  const contentType = normalizeContentType(match[1]);
+  if (!contentType) return null;
+
+  try {
+    const bytes = Buffer.from(match[2].replace(/\s+/g, ""), "base64");
+    if (bytes.length === 0) return null;
+    return { contentType, bytes };
+  } catch {
+    return null;
+  }
+}
+
 function collectMarkdownLinkCandidates(markdown: string): MarkdownLinkCandidate[] {
  const candidates: MarkdownLinkCandidate[] = [];
  const seen = new Set<string>();
@@ -181,7 +212,11 @@ function collectMarkdownLinkCandidates(markdown: string): MarkdownLinkCandidate[
    const coverMatch = fmMatch[1]?.match(FRONTMATTER_COVER_RE);
    if (coverMatch?.[2] && !seen.has(coverMatch[2])) {
      seen.add(coverMatch[2]);
-      candidates.push({ url: coverMatch[2], hint: "image" });
+      candidates.push({
+        url: coverMatch[2],
+        hint: "image",
+        source: isDataUri(coverMatch[2]) ? "data" : "remote",
+      });
    }
  }

@@ -195,6 +230,7 @@ function collectMarkdownLinkCandidates(markdown: string): MarkdownLinkCandidate[
    candidates.push({
      url: rawUrl,
      hint: label.startsWith("![") ? "image" : "unknown",
+      source: isDataUri(rawUrl) ? "data" : "remote",
    });
  }

@@ -244,24 +280,45 @@ export async function localizeMarkdownMedia(

  for (const candidate of candidates) {
    try {
-      const response = await fetch(candidate.url, {
-        method: "GET",
-        redirect: "follow",
-        headers: {
-          "user-agent": DOWNLOAD_USER_AGENT,
-        },
-      });
+      let sourceUrl = candidate.url;
+      let contentType = "";
+      let extension: string | undefined;
+      let kind: MediaKind | undefined;
+      let bytes: Buffer | null = null;

-      if (!response.ok) {
-        log(`[url-to-markdown] Skip media (${response.status}): ${candidate.url}`);
-        continue;
+      if (candidate.source === "data") {
+        const parsed = parseBase64DataUri(candidate.url);
+        if (!parsed) {
+          log("[url-to-markdown] Skip embedded media: unsupported or invalid data URI");
+          continue;
+        }
+
+        contentType = parsed.contentType;
+        extension = resolveExtensionFromContentType(contentType);
+        kind = resolveMediaKind(sourceUrl, contentType, extension, candidate.hint);
+        bytes = parsed.bytes;
+      } else {
+        const response = await fetch(candidate.url, {
+          method: "GET",
+          redirect: "follow",
+          headers: {
+            "user-agent": DOWNLOAD_USER_AGENT,
+          },
+        });
+
+        if (!response.ok) {
+          log(`[url-to-markdown] Skip media (${response.status}): ${candidate.url}`);
+          continue;
+        }
+
+        sourceUrl = response.url || candidate.url;
+        contentType = normalizeContentType(response.headers.get("content-type"));
+        extension = resolveExtensionFromUrl(sourceUrl) ?? resolveExtensionFromUrl(candidate.url);
+        kind = resolveMediaKind(sourceUrl, contentType, extension, candidate.hint);
+        bytes = Buffer.from(await response.arrayBuffer());
      }

-      const sourceUrl = response.url || candidate.url;
-      const contentType = normalizeContentType(response.headers.get("content-type"));
-      const extension = resolveExtensionFromUrl(sourceUrl) ?? resolveExtensionFromUrl(candidate.url);
-      const kind = resolveMediaKind(sourceUrl, contentType, extension, candidate.hint);
-      if (!kind) {
+      if (!kind || !bytes) {
        continue;
      }

@@ -274,7 +331,6 @@ export async function localizeMarkdownMedia(
      const fileName = buildFileName(kind, nextIndex, sourceUrl, outputExtension);
      const absolutePath = path.join(targetDir, fileName);
      const relativePath = path.posix.join(dirName, fileName);
-      const bytes = Buffer.from(await response.arrayBuffer());
      await writeFile(absolutePath, bytes);
      replacements.set(candidate.url, relativePath);

@@ -305,6 +361,7 @@ export function countRemoteMedia(markdown: string): { images: number; videos: nu
  let images = 0;
  let videos = 0;
  for (const c of candidates) {
+    if (c.source !== "remote") continue;
    const ext = resolveExtensionFromUrl(c.url);
    const kind = resolveKindFromExtension(ext);
    if (kind === "video") {
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Jim Liu 宝玉	02a4ca498a	chore: release v1.87.2	2026-03-26 23:03:02 -05:00
Jim Liu 宝玉	31994be0e1	refactor(baoyu-translate): simplify translation prompts and workflow Condense 15+ verbose translation principles to 7 concise ones. Consolidate analysis from 8 sections to 4, review from 8 to 3. Simplify subagent prompt template accordingly.	2026-03-26 23:02:28 -05:00
Jim Liu 宝玉	9137c5ab8c	chore: release v1.87.1	2026-03-26 09:52:54 -05:00
Jim Liu 宝玉	e5d8ad91bc	docs: document deprecated skills policy in CLAUDE.md	2026-03-26 09:51:51 -05:00
Jim Liu 宝玉	9c06b92a74	chore(baoyu-image-gen): add deprecation notice redirecting to baoyu-imagine	2026-03-26 09:51:48 -05:00
Jim Liu 宝玉	41a75584b3	chore: release v1.87.0	2026-03-26 09:44:06 -05:00
Jim Liu 宝玉	88843b0276	chore(baoyu-image-gen): remove deprecated skill from plugin manifest	2026-03-26 09:43:05 -05:00
Jim Liu 宝玉	6909c016b2	chore(baoyu-image-gen): remove deprecated redirect skill	2026-03-26 09:41:04 -05:00
Jim Liu 宝玉	bec1f1e2a1	chore: release v1.86.0	2026-03-25 20:09:44 -05:00
Jim Liu 宝玉	39a97678bb	feat(baoyu-translate): enrich translation prompt with full analysis context Restructure 02-prompt.md template to better leverage analysis results: - Add source voice assessment alongside target style preset - Extract figurative language mapping into dedicated structured table - Include reasoning in comprehension challenges for annotation depth - Add translation challenges section for structural/creative issues - Provide chunk position context in subagent spawn prompts	2026-03-25 20:09:30 -05:00
Jim Liu 宝玉	6cd709b9e7	chore: release v1.85.0	2026-03-25 17:37:18 -05:00
Jim Liu 宝玉	aaf0f188dd	feat(baoyu-image-gen): add deprecation redirect skill to guide migration to baoyu-imagine	2026-03-25 17:36:49 -05:00
Jim Liu 宝玉	b6bf8ecd06	feat(baoyu-imagine): auto-migrate legacy baoyu-image-gen EXTEND.md config path	2026-03-25 17:36:46 -05:00
Jim Liu 宝玉	7a0ffd9533	chore: release v1.84.0	2026-03-25 16:29:22 -05:00
Jim Liu 宝玉	69355b4ee1	feat(baoyu-imagine): rename baoyu-image-gen to baoyu-imagine	2026-03-25 16:28:06 -05:00
Jim Liu 宝玉	23b7487321	chore: release v1.83.0	2026-03-25 15:40:22 -05:00
Jim Liu 宝玉	ad8781c1c5	feat(baoyu-image-gen): add MiniMax provider with subject reference and custom sizes	2026-03-25 15:39:40 -05:00
Jim Liu 宝玉	86a3d6521b	chore: release v1.82.0	2026-03-24 22:40:23 -05:00
Jim Liu 宝玉	e99ce744cd	feat(baoyu-url-to-markdown): add browser fallback strategy, content cleaner, and data URI support - Browser strategy: headless first with automatic retry in visible Chrome on failure - New --browser auto\|headless\|headed flag with --headless/--headed shortcuts - Content cleaner module for HTML preprocessing (remove ads, base64 images, scripts) - Media localizer now handles base64 data URIs alongside remote URLs - Capture finalUrl from browser to track redirects for output path - Agent quality gate documentation for post-capture validation - Upgrade defuddle ^0.12.0 → ^0.14.0 - Add unit tests for content-cleaner, html-to-markdown, legacy-converter, media-localizer	2026-03-24 22:39:17 -05:00
Jim Liu 宝玉	40f9f05c22	chore: release v1.81.0	2026-03-24 20:59:56 -05:00
Jim Liu 宝玉	09ce80357f	feat(baoyu-youtube-transcript): add yt-dlp fallback and modularize codebase Retry with alternate InnerTube client identities when YouTube returns anti-bot responses, then fall back to yt-dlp when available. Split monolithic main.ts into typed modules (youtube, transcript, storage, shared, types) and add unit tests.	2026-03-24 20:59:04 -05:00
Jim Liu 宝玉	7c995fcc24	chore: release v1.80.1	2026-03-24 20:06:02 -05:00
Jim Liu 宝玉	151f1ec2a8	fix(baoyu-image-gen): use correct prompt field name for Jimeng API	2026-03-24 20:04:21 -05:00
Jim Liu 宝玉	12e207dc3f	chore: release v1.80.0	2026-03-24 19:27:57 -05:00
Jim Liu 宝玉	00e74ab071	feat(baoyu-image-gen): improve Azure OpenAI provider with flexible endpoint parsing and deployment resolution	2026-03-24 19:19:49 -05:00
优弧	1653b8544b	feat(baoyu-image-gen): add Azure OpenAI as independent image generation provider (#111 ) Azure OpenAI differs from standard OpenAI in two ways: 1. Auth via api-key header instead of Authorization: Bearer 2. URL requires ?api-version query param with deployment path Changes: - New file: scripts/providers/azure.ts (generations + edits, reuses openai utilities) - types.ts: add "azure" to Provider type and default_model - main.ts: register azure across rate limits, CLI args, auto-detection, provider loading, model resolution, help text, ref validation, EXTEND.md parsing, and batch logging Env vars: AZURE_OPENAI_API_KEY, AZURE_OPENAI_BASE_URL (required), AZURE_API_VERSION, AZURE_OPENAI_IMAGE_MODEL (optional) Co-authored-by: CatFly <zw.catfly@gmail.com> Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-03-24 19:04:34 -05:00
Jim Liu 宝玉	dad8f3a800	chore: release v1.79.2	2026-03-23 22:39:14 -05:00
Jim Liu 宝玉	35298d7c9d	fix(baoyu-post-to-weibo): add no-theme rule for article markdown-to-HTML conversion	2026-03-23 22:38:30 -05:00
Jim Liu 宝玉	f22374ab62	fix(baoyu-cover-image): simplify reference image handling based on model capability	2026-03-23 22:38:27 -05:00
Jim Liu 宝玉	d4e80b1bc3	Fix Node-compatible parser tests (#107 ) * Fix Node-compatible parser tests * Add parser test dependencies to root test env	2026-03-23 15:30:42 -05:00
Jim Liu 宝玉	a5761dc71a	chore: release v1.79.1	2026-03-23 12:02:44 -05:00
Jim Liu 宝玉	a5189dff37	fix(baoyu-xhs-images): remove opacity from watermark prompt and fix CJK spacing	2026-03-23 12:01:03 -05:00
Jim Liu 宝玉	39fe872bf3	fix(baoyu-comic): fix Doraemon naming spacing and remove opacity from watermark prompt	2026-03-23 12:01:00 -05:00
Jim Liu 宝玉	52813504f8	fix(baoyu-article-illustrator): remove opacity parameter from watermark prompt	2026-03-23 12:00:53 -05:00
Jim Liu 宝玉	a4d4108cd1	docs(project): update documentation to reflect single-plugin architecture	2026-03-23 12:00:38 -05:00
Yizhou Qian 钱亦舟	d7e763f1f5	fix: consolidate to single plugin to prevent duplicate skill registration (#106 ) Merge the three plugins (content-skills, ai-generation-skills, utility-skills) into one plugin entry. Since all three shared the same source ("./"), Claude Code cached every skill three times. A single plugin with one source keeps the flat skills/ layout while ensuring each skill is registered exactly once.	2026-03-23 09:46:50 -05:00
Jim Liu 宝玉	097c09c59b	chore: release v1.79.0	2026-03-22 15:42:33 -05:00
Jim Liu 宝玉	e4cd8bfefc	feat(baoyu-post-to-wechat): improve credential loading with multi-source resolution and diagnostics	2026-03-22 15:42:08 -05:00
Jim Liu 宝玉	3dc5f2e06f	chore: release v1.78.0	2026-03-22 15:19:29 -05:00
Jim Liu 宝玉	e5d6c8ec68	feat(baoyu-url-to-markdown): add URL-specific parser layer for X/Twitter and archive.ph - New parsers/ module with pluggable rule system for site-specific HTML extraction - X status parser: extract tweet text, media, quotes, author from data-testid elements - X article parser: extract long-form article content with inline media - archive.ph parser: restore original URL and prefer #CONTENT container - Improved slug generation with stop words and content-aware slugs - Output path uses subdirectory structure (domain/slug/slug.md) - Fix: preserve anchor elements containing media in legacy converter - Fix: smarter title deduplication in markdown document builder	2026-03-22 15:18:46 -05:00
Jim Liu 宝玉	6a4b312146	chore: release v1.77.0	2026-03-22 15:17:12 -05:00
Jim Liu 宝玉	2d6fe533eb	Merge pull request #105 from jzOcb/feat/chapter-end-times feat(youtube-transcript): add end times to chapter data	2026-03-22 15:13:44 -05:00
jzocb	f53af25e65	fix: address review feedback on chapter end times - Guard last chapter end against duration=0: use Math.max(duration, ch.start) - Remove unnecessary 'as any' cast in backfill - Check all chapters for missing end (not just first) via .some() - Skip backfill when needsFetch is true (about to refetch anyway) - Wrap backfill writeFileSync in try/catch (best-effort persistence)	2026-03-22 16:07:05 -04:00
jzocb	c7e32b4590	fix: backfill chapter end times for cached videos Videos cached before the chapter end-time change would silently lack the 'end' field when loaded from cache. This adds a migration that detects missing 'end' fields on cache hit, computes them from adjacent chapters, and persists the updated meta.json. This ensures consistent output regardless of whether the data was freshly fetched or loaded from cache.	2026-03-22 15:58:13 -04:00
jzocb	8d973f2bc5	feat(youtube-transcript): add end times to chapter data Add 'end' field to Chapter interface and parseChapters output. Each chapter's end is derived from the next chapter's start time, with the last chapter ending at the video's total duration. This makes chapter data complete and ready for downstream consumers (e.g. video clipping with ffmpeg) without requiring them to compute end times from adjacent chapters. Before: { title: 'Overview', start: 0 } After: { title: 'Overview', start: 0, end: 21 }	2026-03-22 15:52:30 -04:00
Jim Liu 宝玉	ba20cf89f2	fix(sync-clawhub): skip failed skills instead of aborting	2026-03-21 23:25:45 -05:00
Jim Liu 宝玉	1827be9234	chore: release v1.76.1	2026-03-21 23:17:04 -05:00
Jim Liu 宝玉	93c98dfc3c	docs(baoyu-youtube-transcript): fix zsh glob issue for YouTube URLs	2026-03-21 23:16:51 -05:00
Jim Liu 宝玉	fbd9f9b622	chore: release v1.76.0	2026-03-21 23:08:21 -05:00
Jim Liu 宝玉	b6e293d059	fix(baoyu-markdown-to-html): use process.execPath and tsx import in test runner	2026-03-21 23:07:46 -05:00
Jim Liu 宝玉	bb78aab095	feat(baoyu-youtube-transcript): add title heading, description summary, and cover image to markdown output	2026-03-21 23:07:44 -05:00
Jim Liu 宝玉	5071a1d0d0	chore: release v1.75.0	2026-03-21 22:44:00 -05:00
Jim Liu 宝玉	e413ade164	feat(baoyu-youtube-transcript): add sentence segmentation and improve caching	2026-03-21 22:42:43 -05:00
Jim Liu 宝玉	e52f92b193	new skill	2026-03-21 21:03:06 -05:00
Jim Liu 宝玉	603cabaef4	chore: release v1.74.1	2026-03-21 00:03:51 -05:00
Jim Liu 宝玉	7d12526e90	fix(baoyu-image-gen): broaden OpenRouter model detection and aspect ratio validation	2026-03-21 00:03:20 -05:00
Jim Liu 宝玉	e7f9764a49	Merge pull request #101 from cwandev/fix/openrouter fix(baoyu-image-gen): align `OpenRouter` image generation with current API	2026-03-20 23:32:26 -05:00
Jim Liu 宝玉	e55f91b0ea	chore: release v1.74.0	2026-03-20 23:17:14 -05:00
Jim Liu 宝玉	fe3b3d9125	feat(baoyu-markdown-to-html): pass through all rendering options from CLI - CLI now supports --color, --font-family, --font-size, --code-theme, --mac-code-block, --line-number, --count, --legend - convertMarkdown accepts full CliOptions instead of limited subset - Dynamic help text showing available theme/color/font options	2026-03-20 23:17:10 -05:00
Jim Liu 宝玉	105339cf3f	fix(baoyu-md): fix CSS custom property regex for quoted values and add theme layering - Remove quotes from CSS custom property regex character class so values containing quotes are fully stripped - grace/simple themes now layer default CSS before their own rules - Add tests for quoted property stripping and theme layering	2026-03-20 23:17:04 -05:00
Jim Liu 宝玉	dcfd9033ae	chore: release v1.73.3	2026-03-20 18:46:01 -05:00
Jim Liu 宝玉	eb416d174c	fix(baoyu-post-to-wechat): prevent placeholder regex from matching longer numbered variants	2026-03-20 18:45:05 -05:00
Jim Liu 宝玉	83afacb00e	chore: release v1.73.2	2026-03-20 14:46:06 -05:00
Jim Liu 宝玉	0e6bfbcabd	refactor: 提取图片处理模块，本地转换不支持的格式而非回退到material接口将WeChat正文图片的格式转换和压缩逻辑提取到独立的wechat-image-processor模块，使用jimp和@jsquash/webp在本地将WebP/BMP/GIF等不支持的格式转换为JPEG/PNG并压缩到1MB以内，避免依赖material接口的回退策略。同时简化了news类型封面兜底逻辑。	2026-03-20 14:43:49 -05:00
Jim Liu 宝玉	7b67ff44b8	Merge pull request #100 from AICreator-Wind/fix-body-image-upload fix: 正文图片上传使用 media/uploadimg 接口	2026-03-20 14:00:14 -05:00
浪不能停	79c289ca92	fix: 处理WebP等不支持的格式并优化回退逻辑 - 添加WebP、BMP、TIFF等格式到不支持列表 - 对于需要回退到material接口的情况，同时调用两个接口获取URL和media_id - 提取uploadToWechat函数避免代码重复	2026-03-20 18:18:01 +08:00
浪不能停	fc5ad4b762	fix: 处理media/uploadimg接口的文件格式和大小限制 - 添加文件大小和格式检查常量 - GIF图片或大于1MB的图片自动回退使用material接口 - 确保向后兼容现有工作流	2026-03-20 14:25:48 +08:00
浪不能停	747977416d	fix: 修复newspic类型图片上传和news类型封面兜底逻辑 - uploadImagesInHtml 函数添加 articleType 参数支持 - 为 newspic 类型正文图片额外调用 material 接口收集 media_id - 返回 firstImageSource 用于 news 类型封面兜底逻辑 - 恢复 news 类型没有显式封面时使用第一张正文图作为封面的逻辑	2026-03-20 13:57:59 +08:00
cwandev	e43eec260a	fix(baoyu-image-gen): narrow OpenRouter Gemini ratios	2026-03-19 22:09:23 +08:00
cwandev	96ef6e2251	fix(baoyu-image-gen): harden OpenRouter image support	2026-03-19 21:59:41 +08:00
cwandev	efb7a1917a	fix(baoyu-image-gen): require OpenRouter image parameters	2026-03-19 21:26:19 +08:00
cwandev	1af984a64f	fix(baoyu-image-gen): align OpenRouter image generation with current API	2026-03-19 17:36:03 +08:00
Jim Liu 宝玉	60363fc2df	chore: release v1.73.1	2026-03-18 22:00:27 -05:00
Jim Liu 宝玉	28ec1053f6	refactor(baoyu-danger-x-to-markdown): migrate tests from bun:test to node:test	2026-03-18 21:59:49 -05:00
浪不能停	e79a42fd94	fix: 正文图片上传使用 media/uploadimg 接口 - 区分正文图片和封面图片的上传接口 - 正文图片使用 media/uploadimg (返回 URL) - 封面图片使用 material/add_material (返回 media_id) - 添加 uploadType 参数支持两种上传方式 - 优化错误提示,告知用户 news 类型需要封面图	2026-03-18 17:23:48 +08:00