mirror of
https://github.com/JimLiu/baoyu-skills.git
synced 2026-07-12 13:59:47 +08:00
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@@ -6,48 +6,33 @@
|
||||
},
|
||||
"metadata": {
|
||||
"description": "Skills shared by Baoyu for improving daily work efficiency",
|
||||
"version": "1.74.0"
|
||||
"version": "1.81.0"
|
||||
},
|
||||
"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-image-gen",
|
||||
"./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 +2,91 @@
|
||||
|
||||
English | [中文](./CHANGELOG.zh.md)
|
||||
|
||||
## 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
|
||||
|
||||
@@ -2,6 +2,91 @@
|
||||
|
||||
[English](./CHANGELOG.md) | 中文
|
||||
|
||||
## 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
|
||||
|
||||
@@ -1,16 +1,16 @@
|
||||
# CLAUDE.md
|
||||
|
||||
Claude Code marketplace plugin providing AI-powered content generation skills. Version: **1.74.0**.
|
||||
Claude Code marketplace plugin providing AI-powered content generation skills. Version: **1.81.0**.
|
||||
|
||||
## 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-image-gen` 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
|
||||
|
||||
@@ -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
|
||||
|
||||
@@ -665,7 +663,7 @@ AI-powered generation backends.
|
||||
|
||||
#### baoyu-image-gen
|
||||
|
||||
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), Jimeng (即梦), Seedream (豆包), and Replicate APIs. Supports text-to-image, reference images, aspect ratios, and quality presets.
|
||||
|
||||
```bash
|
||||
# Basic generation (auto-detect provider)
|
||||
@@ -680,6 +678,9 @@ AI SDK-based image generation using OpenAI, Google, OpenRouter, DashScope (Aliyu
|
||||
# Specific provider
|
||||
/baoyu-image-gen --prompt "A cat" --image cat.png --provider openai
|
||||
|
||||
# Azure OpenAI (model = deployment name)
|
||||
/baoyu-image-gen --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
|
||||
|
||||
@@ -695,7 +696,7 @@ AI SDK-based image generation using OpenAI, Google, OpenRouter, DashScope (Aliyu
|
||||
# Seedream (豆包)
|
||||
/baoyu-image-gen --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)
|
||||
# With reference images (Google, OpenAI, Azure OpenAI, OpenRouter, Replicate, or Seedream 5.0/4.5/4.0)
|
||||
/baoyu-image-gen --prompt "Make it blue" --image out.png --ref source.png
|
||||
```
|
||||
|
||||
@@ -766,6 +767,40 @@ 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.
|
||||
|
||||
+46
-11
@@ -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
|
||||
|
||||
@@ -665,7 +663,7 @@ AI 驱动的生成后端。
|
||||
|
||||
#### baoyu-image-gen
|
||||
|
||||
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)、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。
|
||||
|
||||
```bash
|
||||
# 基础生成(自动检测服务商)
|
||||
@@ -680,6 +678,9 @@ AI 驱动的生成后端。
|
||||
# 指定服务商
|
||||
/baoyu-image-gen --prompt "一只猫" --image cat.png --provider openai
|
||||
|
||||
# Azure OpenAI(model 为部署名称)
|
||||
/baoyu-image-gen --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
|
||||
|
||||
@@ -695,7 +696,7 @@ AI 驱动的生成后端。
|
||||
# 豆包(Seedream)
|
||||
/baoyu-image-gen --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)
|
||||
# 带参考图(Google、OpenAI、Azure 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
|
||||
```
|
||||
|
||||
@@ -766,6 +767,40 @@ 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。
|
||||
|
||||
+11
-9
@@ -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
|
||||
|
||||
|
||||
Generated
+105
-1
@@ -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"
|
||||
}
|
||||
}
|
||||
|
||||
+24
-11
@@ -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) {
|
||||
|
||||
@@ -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].
|
||||
```
|
||||
|
||||
@@ -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.
|
||||
```
|
||||
@@ -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 |
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
---
|
||||
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.
|
||||
description: AI image generation with OpenAI, Azure 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
|
||||
metadata:
|
||||
openclaw:
|
||||
@@ -13,7 +13,7 @@ metadata:
|
||||
|
||||
# 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), Jimeng (即梦), Seedream (豆包) and Replicate providers.
|
||||
|
||||
## Script Directory
|
||||
|
||||
@@ -74,12 +74,15 @@ ${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, 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
|
||||
|
||||
@@ -147,13 +150,13 @@ 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\|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`) |
|
||||
| `--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, 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 |
|
||||
|
||||
@@ -162,6 +165,7 @@ 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) |
|
||||
@@ -170,6 +174,8 @@ Paths in `promptFiles`, `image`, and `ref` are resolved relative to the batch fi
|
||||
| `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`) |
|
||||
@@ -177,6 +183,8 @@ Paths in `promptFiles`, `image`, and `ref` are resolved relative to the batch fi
|
||||
| `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 |
|
||||
@@ -201,6 +209,8 @@ 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:
|
||||
|
||||
@@ -47,6 +47,8 @@ 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"
|
||||
@@ -87,6 +89,20 @@ 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 3: Default Quality
|
||||
|
||||
```yaml
|
||||
@@ -130,6 +146,7 @@ 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
|
||||
replicate: null
|
||||
@@ -166,6 +183,23 @@ 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
|
||||
@@ -230,6 +264,7 @@ 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]
|
||||
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|replicate|null (null = auto-detect)
|
||||
|
||||
default_quality: null # normal|2k|null (null = use default: 2k)
|
||||
|
||||
@@ -22,6 +22,7 @@ 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"
|
||||
replicate: null # e.g., "google/nano-banana-pro"
|
||||
@@ -38,6 +39,9 @@ batch:
|
||||
openai:
|
||||
concurrency: 3
|
||||
start_interval_ms: 1100
|
||||
azure:
|
||||
concurrency: 3
|
||||
start_interval_ms: 1100
|
||||
openrouter:
|
||||
concurrency: 3
|
||||
start_interval_ms: 1100
|
||||
@@ -58,6 +62,7 @@ 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.replicate` | string\|null | null | Replicate default model |
|
||||
@@ -87,6 +92,7 @@ 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"
|
||||
replicate: "google/nano-banana-pro"
|
||||
@@ -96,6 +102,9 @@ batch:
|
||||
replicate:
|
||||
concurrency: 5
|
||||
start_interval_ms: 700
|
||||
azure:
|
||||
concurrency: 3
|
||||
start_interval_ms: 1100
|
||||
openrouter:
|
||||
concurrency: 3
|
||||
start_interval_ms: 1100
|
||||
|
||||
@@ -123,6 +123,7 @@ default_image_size: 2K
|
||||
default_model:
|
||||
google: gemini-3-pro-image-preview
|
||||
openai: gpt-image-1.5
|
||||
azure: image-prod
|
||||
batch:
|
||||
max_workers: 8
|
||||
provider_limits:
|
||||
@@ -131,6 +132,9 @@ batch:
|
||||
start_interval_ms: 900
|
||||
openai:
|
||||
concurrency: 4
|
||||
azure:
|
||||
concurrency: 1
|
||||
start_interval_ms: 1500
|
||||
`;
|
||||
|
||||
const config = parseSimpleYaml(yaml);
|
||||
@@ -142,6 +146,7 @@ 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.batch?.max_workers, 8);
|
||||
assert.deepEqual(config.batch?.provider_limits?.google, {
|
||||
concurrency: 2,
|
||||
@@ -150,6 +155,10 @@ batch:
|
||||
assert.deepEqual(config.batch?.provider_limits?.openai, {
|
||||
concurrency: 4,
|
||||
});
|
||||
assert.deepEqual(config.batch?.provider_limits?.azure, {
|
||||
concurrency: 1,
|
||||
start_interval_ms: 1500,
|
||||
});
|
||||
});
|
||||
|
||||
test("mergeConfig only fills values missing from CLI args", () => {
|
||||
@@ -203,6 +212,8 @@ 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,
|
||||
REPLICATE_API_TOKEN: null,
|
||||
@@ -216,6 +227,27 @@ 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,
|
||||
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,
|
||||
|
||||
@@ -60,6 +60,7 @@ const DEFAULT_PROVIDER_RATE_LIMITS: Record<Provider, ProviderRateLimit> = {
|
||||
dashscope: { concurrency: 3, startIntervalMs: 1100 },
|
||||
jimeng: { concurrency: 3, startIntervalMs: 1100 },
|
||||
seedream: { concurrency: 3, startIntervalMs: 1100 },
|
||||
azure: { concurrency: 3, startIntervalMs: 1100 },
|
||||
};
|
||||
|
||||
function printUsage(): void {
|
||||
@@ -74,13 +75,13 @@ 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|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, 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
|
||||
@@ -131,6 +132,11 @@ Environment variables:
|
||||
DASHSCOPE_BASE_URL Custom DashScope 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
|
||||
@@ -231,7 +237,8 @@ export function parseArgs(argv: string[]): CliArgs {
|
||||
v !== "dashscope" &&
|
||||
v !== "replicate" &&
|
||||
v !== "jimeng" &&
|
||||
v !== "seedream"
|
||||
v !== "seedream" &&
|
||||
v !== "azure"
|
||||
) {
|
||||
throw new Error(`Invalid provider: ${v}`);
|
||||
}
|
||||
@@ -386,6 +393,7 @@ export function parseSimpleYaml(yaml: string): Partial<ExtendConfig> {
|
||||
replicate: null,
|
||||
jimeng: null,
|
||||
seedream: null,
|
||||
azure: null,
|
||||
};
|
||||
currentKey = "default_model";
|
||||
currentProvider = null;
|
||||
@@ -411,7 +419,8 @@ export function parseSimpleYaml(yaml: string): Partial<ExtendConfig> {
|
||||
key === "dashscope" ||
|
||||
key === "replicate" ||
|
||||
key === "jimeng" ||
|
||||
key === "seedream"
|
||||
key === "seedream" ||
|
||||
key === "azure"
|
||||
)
|
||||
) {
|
||||
config.batch ??= {};
|
||||
@@ -427,7 +436,8 @@ export function parseSimpleYaml(yaml: string): Partial<ExtendConfig> {
|
||||
key === "dashscope" ||
|
||||
key === "replicate" ||
|
||||
key === "jimeng" ||
|
||||
key === "seedream"
|
||||
key === "seedream" ||
|
||||
key === "azure"
|
||||
)
|
||||
) {
|
||||
const cleaned = value.replace(/['"]/g, "");
|
||||
@@ -520,9 +530,10 @@ export function getConfiguredProviderRateLimits(
|
||||
dashscope: { ...DEFAULT_PROVIDER_RATE_LIMITS.dashscope },
|
||||
jimeng: { ...DEFAULT_PROVIDER_RATE_LIMITS.jimeng },
|
||||
seedream: { ...DEFAULT_PROVIDER_RATE_LIMITS.seedream },
|
||||
azure: { ...DEFAULT_PROVIDER_RATE_LIMITS.azure },
|
||||
};
|
||||
|
||||
for (const provider of ["replicate", "google", "openai", "openrouter", "dashscope", "jimeng", "seedream"] as Provider[]) {
|
||||
for (const provider of ["replicate", "google", "openai", "openrouter", "dashscope", "jimeng", "seedream", "azure"] as Provider[]) {
|
||||
const envPrefix = `BAOYU_IMAGE_GEN_${provider.toUpperCase()}`;
|
||||
const extendLimit = extendConfig.batch?.provider_limits?.[provider];
|
||||
configured[provider] = {
|
||||
@@ -581,18 +592,20 @@ 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"
|
||||
) {
|
||||
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, or --provider seedream for supported Seedream models."
|
||||
);
|
||||
}
|
||||
|
||||
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;
|
||||
@@ -611,17 +624,19 @@ export function detectProvider(args: CliArgs): Provider {
|
||||
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";
|
||||
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, or supported Seedream models. Set GOOGLE_API_KEY/GEMINI_API_KEY, OPENAI_API_KEY, AZURE_OPENAI_API_KEY+AZURE_OPENAI_BASE_URL, OPENROUTER_API_KEY, REPLICATE_API_TOKEN, or ARK_API_KEY, or remove --ref."
|
||||
);
|
||||
}
|
||||
|
||||
const available = [
|
||||
hasGoogle && "google",
|
||||
hasOpenai && "openai",
|
||||
hasAzure && "azure",
|
||||
hasOpenrouter && "openrouter",
|
||||
hasDashscope && "dashscope",
|
||||
hasReplicate && "replicate",
|
||||
@@ -633,7 +648,7 @@ 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, REPLICATE_API_TOKEN, JIMENG keys, or ARK_API_KEY.\n" +
|
||||
"Create ~/.baoyu-skills/.env or <cwd>/.baoyu-skills/.env with your keys."
|
||||
);
|
||||
}
|
||||
@@ -676,6 +691,7 @@ async function loadProviderModule(provider: Provider): Promise<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;
|
||||
}
|
||||
|
||||
@@ -704,6 +720,7 @@ function getModelForProvider(
|
||||
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 +940,7 @@ 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 +1,168 @@
|
||||
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 +1,4 @@
|
||||
export type Provider = "google" | "openai" | "openrouter" | "dashscope" | "replicate" | "jimeng" | "seedream";
|
||||
export type Provider = "google" | "openai" | "openrouter" | "dashscope" | "replicate" | "jimeng" | "seedream" | "azure";
|
||||
export type Quality = "normal" | "2k";
|
||||
|
||||
export type CliArgs = {
|
||||
@@ -55,6 +55,7 @@ export type ExtendConfig = {
|
||||
replicate: string | null;
|
||||
jimeng: string | null;
|
||||
seedream: string | null;
|
||||
azure: string | null;
|
||||
};
|
||||
batch?: {
|
||||
max_workers?: number | null;
|
||||
|
||||
@@ -3,6 +3,7 @@ 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";
|
||||
@@ -21,8 +22,10 @@ test("CLI forwards wrapper title and vendor render options", async () => {
|
||||
await fs.writeFile(markdownPath, "## Section\n\nParagraph with **bold** text.\n", "utf-8");
|
||||
|
||||
const { stdout } = await execFileAsync(
|
||||
"bun",
|
||||
process.execPath,
|
||||
[
|
||||
"--import",
|
||||
"tsx",
|
||||
SCRIPT_PATH,
|
||||
markdownPath,
|
||||
"--theme", "grace",
|
||||
|
||||
@@ -694,6 +694,10 @@ 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);
|
||||
|
||||
|
||||
@@ -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[] {
|
||||
|
||||
@@ -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
|
||||
|
||||
@@ -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 +118,13 @@ Full reference: [references/config/first-time-setup.md](references/config/first-
|
||||
## Features
|
||||
|
||||
- Chrome CDP for full JavaScript rendering
|
||||
- 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
|
||||
@@ -201,14 +207,16 @@ 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 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
|
||||
6. 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
|
||||
|
||||
@@ -12,6 +12,7 @@ import {
|
||||
scoreMarkdownQuality,
|
||||
shouldCompareWithLegacy,
|
||||
} from "./legacy-converter.js";
|
||||
import { tryUrlRuleParsers } from "./parsers/index.js";
|
||||
|
||||
export type { ConversionResult, PageMetadata };
|
||||
export { createMarkdownDocument, formatMetadataYaml };
|
||||
@@ -105,6 +106,11 @@ export async function extractContent(html: string, url: string): Promise<Convers
|
||||
const capturedAt = new Date().toISOString();
|
||||
const baseMetadata = extractMetadataFromHtml(html, url, capturedAt);
|
||||
|
||||
const specializedResult = tryUrlRuleParsers(html, url, baseMetadata);
|
||||
if (specializedResult) {
|
||||
return specializedResult;
|
||||
}
|
||||
|
||||
const defuddleResult = await tryDefuddleConversion(html, url, baseMetadata);
|
||||
if (defuddleResult.ok) {
|
||||
if (shouldPreferDefuddle(defuddleResult.result)) {
|
||||
|
||||
@@ -521,14 +521,18 @@ 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 {
|
||||
@@ -609,7 +613,7 @@ export function shouldCompareWithLegacy(markdown: string): boolean {
|
||||
export function convertWithLegacyExtractor(html: string, baseMetadata: PageMetadata): ConversionResult {
|
||||
const extracted = extractFromHtml(html);
|
||||
|
||||
let markdown = extracted?.html ? convertHtmlToMarkdown(extracted.html) : "";
|
||||
let markdown = extracted?.html ? convertHtmlFragmentToMarkdown(extracted.html) : "";
|
||||
if (!markdown.trim()) {
|
||||
markdown = extracted?.textContent?.trim() || fallbackPlainText(html);
|
||||
}
|
||||
|
||||
@@ -52,15 +52,71 @@ 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}-${pathSlug}` : pathSlug;
|
||||
return combined.slice(0, 50) || "page";
|
||||
}
|
||||
|
||||
function formatTimestamp(): string {
|
||||
@@ -124,18 +180,18 @@ async function fetchDefuddleApiMarkdown(targetUrl: string): Promise<{ markdown:
|
||||
};
|
||||
}
|
||||
|
||||
async function generateOutputPath(url: string, title: string, outputDir?: string): Promise<string> {
|
||||
async function generateOutputPath(url: string, title: string, outputDir?: string, content?: string): Promise<string> {
|
||||
const domain = new URL(url).hostname.replace(/^www\./, "");
|
||||
const slug = generateSlug(title, url);
|
||||
const slug = generateSlug(title, url, content);
|
||||
const dataDir = outputDir ? path.resolve(outputDir) : resolveUrlToMarkdownDataDir();
|
||||
const basePath = path.join(dataDir, domain, `${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> {
|
||||
@@ -249,13 +305,12 @@ 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(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 +320,9 @@ 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 +1,206 @@
|
||||
import assert from "node:assert/strict";
|
||||
import test from "node:test";
|
||||
|
||||
import {
|
||||
createMarkdownDocument,
|
||||
extractMetadataFromHtml,
|
||||
} from "../markdown-conversion-shared.js";
|
||||
import { tryUrlRuleParsers } from "./index.js";
|
||||
|
||||
const CAPTURED_AT = "2026-03-22T06:00:00.000Z";
|
||||
|
||||
const ARTICLE_HTML = `<!doctype html>
|
||||
<html lang="zh-CN">
|
||||
<body>
|
||||
<div data-testid="twitterArticleReadView">
|
||||
<a href="/dotey/article/2035141635713941927/media/1">
|
||||
<div data-testid="tweetPhoto">
|
||||
<img src="https://pbs.twimg.com/media/article-cover.jpg" alt="Image">
|
||||
</div>
|
||||
</a>
|
||||
<div data-testid="twitter-article-title">Karpathy:"写代码"已经不是对的动词了</div>
|
||||
<div data-testid="User-Name">
|
||||
<a href="/dotey">宝玉 Verified account</a>
|
||||
<a href="/dotey">@dotey</a>
|
||||
<time datetime="2026-03-20T23:49:11.000Z">Mar 20</time>
|
||||
</div>
|
||||
<div data-testid="twitterArticleRichTextView">
|
||||
<p>Andrej Karpathy 说他从 2024 年 12 月起就基本没手写过一行代码。</p>
|
||||
<a href="/dotey/article/2035141635713941927/media/2">
|
||||
<div>
|
||||
<div>
|
||||
<div data-testid="tweetPhoto">
|
||||
<img src="https://pbs.twimg.com/media/article-inline.jpg" alt="Image">
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</a>
|
||||
<h2>要点速览</h2>
|
||||
<ul>
|
||||
<li>核心焦虑从 GPU 利用率转向 Token 吞吐量</li>
|
||||
</ul>
|
||||
<blockquote>
|
||||
<p>写代码已经不是对的动词了。</p>
|
||||
</blockquote>
|
||||
</div>
|
||||
</div>
|
||||
</body>
|
||||
</html>`;
|
||||
|
||||
const STATUS_HTML = `<!doctype html>
|
||||
<html lang="en">
|
||||
<body>
|
||||
<article data-testid="tweet">
|
||||
<div data-testid="User-Name">
|
||||
<a href="/dotey">宝玉 Verified account</a>
|
||||
<a href="/dotey">@dotey</a>
|
||||
<time datetime="2026-03-22T05:33:00.000Z">Mar 22</time>
|
||||
</div>
|
||||
<div data-testid="tweetText">
|
||||
<span>转译:把下面这段加到你的 Codex 自定义指令里,体验会好太多:</span>
|
||||
</div>
|
||||
<div data-testid="tweetPhoto">
|
||||
<img src="https://pbs.twimg.com/media/tweet-main.jpg" alt="Image">
|
||||
</div>
|
||||
<div data-testid="User-Name">
|
||||
<a href="/mattshumer_">Matt Shumer Verified account</a>
|
||||
<a href="/mattshumer_">@mattshumer_</a>
|
||||
<time datetime="2026-03-17T00:00:00.000Z">Mar 17</time>
|
||||
</div>
|
||||
<div data-testid="tweetText">
|
||||
<span>Add this to your Codex custom instructions for a way better experience.</span>
|
||||
</div>
|
||||
</article>
|
||||
</body>
|
||||
</html>`;
|
||||
|
||||
const ARCHIVE_HTML = `<!doctype html>
|
||||
<html>
|
||||
<head>
|
||||
<title>archive.ph</title>
|
||||
</head>
|
||||
<body>
|
||||
<form>
|
||||
<input
|
||||
type="text"
|
||||
name="q"
|
||||
value="https://www.newscientist.com/article/2520204-major-leap-towards-reanimation-after-death-as-mammals-brain-preserved/"
|
||||
>
|
||||
</form>
|
||||
<div id="HEADER">
|
||||
Archive shell text that should be ignored when CONTENT exists.
|
||||
</div>
|
||||
<div id="CONTENT">
|
||||
<h1>Major leap towards reanimation after death as mammal brain preserved</h1>
|
||||
<p>
|
||||
Researchers say the preserved structure and activity markers suggest a significant step
|
||||
forward in keeping delicate brain tissue viable after clinical death.
|
||||
</p>
|
||||
<p>
|
||||
The archive wrapper should not take precedence over the actual article body when the
|
||||
CONTENT container is available for parsing.
|
||||
</p>
|
||||
<img src="https://cdn.example.com/brain.jpg" alt="Brain tissue">
|
||||
</div>
|
||||
</body>
|
||||
</html>`;
|
||||
|
||||
const ARCHIVE_FALLBACK_HTML = `<!doctype html>
|
||||
<html>
|
||||
<head>
|
||||
<title>archive.ph</title>
|
||||
</head>
|
||||
<body>
|
||||
<input type="text" name="q" value="https://example.com/fallback-story">
|
||||
<main>
|
||||
<h1>Fallback body parsing still works</h1>
|
||||
<p>
|
||||
When CONTENT is absent, the parser should fall back to the body content instead of
|
||||
returning null or keeping the archive wrapper as the final URL.
|
||||
</p>
|
||||
<p>
|
||||
This ensures archived pages with slightly different layouts still produce usable markdown.
|
||||
</p>
|
||||
</main>
|
||||
</body>
|
||||
</html>`;
|
||||
|
||||
function parse(html: string, url: string) {
|
||||
const baseMetadata = extractMetadataFromHtml(html, url, CAPTURED_AT);
|
||||
return tryUrlRuleParsers(html, url, baseMetadata);
|
||||
}
|
||||
|
||||
test("parses archive.ph pages from CONTENT and restores the original URL", () => {
|
||||
const result = parse(ARCHIVE_HTML, "https://archive.ph/SMcX5");
|
||||
|
||||
assert.ok(result);
|
||||
assert.equal(result.conversionMethod, "parser:archive-ph");
|
||||
assert.equal(
|
||||
result.metadata.url,
|
||||
"https://www.newscientist.com/article/2520204-major-leap-towards-reanimation-after-death-as-mammals-brain-preserved/"
|
||||
);
|
||||
assert.equal(
|
||||
result.metadata.title,
|
||||
"Major leap towards reanimation after death as mammal brain preserved"
|
||||
);
|
||||
assert.equal(result.metadata.coverImage, "https://cdn.example.com/brain.jpg");
|
||||
assert.ok(result.markdown.includes("Researchers say the preserved structure"));
|
||||
assert.ok(result.markdown.includes(""));
|
||||
assert.ok(!result.markdown.includes("Archive shell text that should be ignored"));
|
||||
});
|
||||
|
||||
test("falls back to body when archive.ph CONTENT is missing", () => {
|
||||
const result = parse(ARCHIVE_FALLBACK_HTML, "https://archive.ph/fallback");
|
||||
|
||||
assert.ok(result);
|
||||
assert.equal(result.conversionMethod, "parser:archive-ph");
|
||||
assert.equal(result.metadata.url, "https://example.com/fallback-story");
|
||||
assert.equal(result.metadata.title, "Fallback body parsing still works");
|
||||
assert.ok(result.markdown.includes("When CONTENT is absent"));
|
||||
});
|
||||
|
||||
test("parses X article pages from HTML", () => {
|
||||
const result = parse(
|
||||
ARTICLE_HTML,
|
||||
"https://x.com/dotey/article/2035141635713941927"
|
||||
);
|
||||
|
||||
assert.ok(result);
|
||||
assert.equal(result.conversionMethod, "parser:x-article");
|
||||
assert.equal(result.metadata.title, "Karpathy:\"写代码\"已经不是对的动词了");
|
||||
assert.equal(result.metadata.author, "宝玉 (@dotey)");
|
||||
assert.equal(result.metadata.coverImage, "https://pbs.twimg.com/media/article-cover.jpg");
|
||||
assert.equal(result.metadata.published, "2026-03-20T23:49:11.000Z");
|
||||
assert.equal(result.metadata.language, "zh");
|
||||
assert.ok(result.markdown.includes("## 要点速览"));
|
||||
assert.ok(
|
||||
result.markdown.includes(
|
||||
"[](/dotey/article/2035141635713941927/media/2)"
|
||||
)
|
||||
);
|
||||
assert.ok(result.markdown.includes("写代码已经不是对的动词了。"));
|
||||
|
||||
const document = createMarkdownDocument(result);
|
||||
assert.ok(document.includes("# Karpathy:\"写代码\"已经不是对的动词了"));
|
||||
});
|
||||
|
||||
test("parses X status pages from HTML without duplicating the title heading", () => {
|
||||
const result = parse(
|
||||
STATUS_HTML,
|
||||
"https://x.com/dotey/status/2035590649081196710"
|
||||
);
|
||||
|
||||
assert.ok(result);
|
||||
assert.equal(result.conversionMethod, "parser:x-status");
|
||||
assert.equal(result.metadata.author, "宝玉 (@dotey)");
|
||||
assert.equal(result.metadata.coverImage, "https://pbs.twimg.com/media/tweet-main.jpg");
|
||||
assert.equal(result.metadata.language, "zh");
|
||||
assert.ok(result.markdown.includes("转译:把下面这段加到你的 Codex 自定义指令里"));
|
||||
assert.ok(result.markdown.includes("> Quote from Matt Shumer (@mattshumer_)"));
|
||||
assert.ok(result.markdown.includes("!["));
|
||||
|
||||
const document = createMarkdownDocument(result);
|
||||
assert.ok(
|
||||
!document.includes("\n\n# 转译:把下面这段加到你的 Codex 自定义指令里,体验会好太多:\n\n")
|
||||
);
|
||||
});
|
||||
@@ -0,0 +1,47 @@
|
||||
import {
|
||||
isMarkdownUsable,
|
||||
normalizeMarkdown,
|
||||
parseDocument,
|
||||
type ConversionResult,
|
||||
type PageMetadata,
|
||||
} from "../markdown-conversion-shared.js";
|
||||
import { URL_RULE_PARSERS } from "./rules/index.js";
|
||||
import type { UrlRuleParserContext } from "./types.js";
|
||||
|
||||
export type { UrlRuleParser, UrlRuleParserContext } from "./types.js";
|
||||
|
||||
export function tryUrlRuleParsers(
|
||||
html: string,
|
||||
url: string,
|
||||
baseMetadata: PageMetadata
|
||||
): ConversionResult | null {
|
||||
const document = parseDocument(html);
|
||||
const context: UrlRuleParserContext = {
|
||||
html,
|
||||
url,
|
||||
document,
|
||||
baseMetadata,
|
||||
};
|
||||
|
||||
for (const parser of URL_RULE_PARSERS) {
|
||||
if (!parser.supports(context)) continue;
|
||||
|
||||
try {
|
||||
const result = parser.parse(context);
|
||||
if (!result) continue;
|
||||
|
||||
const markdown = normalizeMarkdown(result.markdown);
|
||||
if (!isMarkdownUsable(markdown, html)) continue;
|
||||
|
||||
return {
|
||||
...result,
|
||||
markdown,
|
||||
};
|
||||
} catch (error) {
|
||||
const message = error instanceof Error ? error.message : String(error);
|
||||
console.warn(`[url-to-markdown] parser ${parser.id} failed: ${message}`);
|
||||
}
|
||||
}
|
||||
|
||||
return null;
|
||||
}
|
||||
@@ -0,0 +1,97 @@
|
||||
import { convertHtmlFragmentToMarkdown } from "../../legacy-converter.js";
|
||||
import {
|
||||
normalizeMarkdown,
|
||||
pickString,
|
||||
type ConversionResult,
|
||||
} from "../../markdown-conversion-shared.js";
|
||||
import type { UrlRuleParser, UrlRuleParserContext } from "../types.js";
|
||||
|
||||
const ARCHIVE_HOSTS = new Set([
|
||||
"archive.ph",
|
||||
"archive.is",
|
||||
"archive.today",
|
||||
"archive.md",
|
||||
"archive.vn",
|
||||
"archive.li",
|
||||
"archive.fo",
|
||||
]);
|
||||
|
||||
function isArchiveHost(url: string): boolean {
|
||||
try {
|
||||
return ARCHIVE_HOSTS.has(new URL(url).hostname.toLowerCase());
|
||||
} catch {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
function readOriginalUrl(document: Document): string | undefined {
|
||||
const value = document.querySelector("input[name='q']")?.getAttribute("value")?.trim();
|
||||
if (!value) return undefined;
|
||||
|
||||
try {
|
||||
return new URL(value).href;
|
||||
} catch {
|
||||
return undefined;
|
||||
}
|
||||
}
|
||||
|
||||
function summarize(text: string, maxLength: number): string | undefined {
|
||||
const normalized = text.replace(/\s+/g, " ").trim();
|
||||
if (!normalized) return undefined;
|
||||
if (normalized.length <= maxLength) return normalized;
|
||||
return `${normalized.slice(0, Math.max(0, maxLength - 1)).trimEnd()}…`;
|
||||
}
|
||||
|
||||
function pickContentRoot(document: Document): Element | null {
|
||||
return (
|
||||
document.querySelector("#CONTENT") ??
|
||||
document.querySelector("#content") ??
|
||||
document.body
|
||||
);
|
||||
}
|
||||
|
||||
function pickContentTitle(root: Element, fallbackTitle: string): string {
|
||||
const contentTitle = pickString(
|
||||
root.querySelector("h1")?.textContent,
|
||||
root.querySelector("[itemprop='headline']")?.textContent,
|
||||
root.querySelector("article h2")?.textContent
|
||||
);
|
||||
if (contentTitle) return contentTitle;
|
||||
if (fallbackTitle && !/^archive\./i.test(fallbackTitle.trim())) return fallbackTitle;
|
||||
return "";
|
||||
}
|
||||
|
||||
function parseArchivePage(context: UrlRuleParserContext): ConversionResult | null {
|
||||
const root = pickContentRoot(context.document);
|
||||
if (!root) return null;
|
||||
|
||||
const markdown = normalizeMarkdown(convertHtmlFragmentToMarkdown(root.innerHTML));
|
||||
if (!markdown) return null;
|
||||
|
||||
const originalUrl = readOriginalUrl(context.document) ?? context.baseMetadata.url;
|
||||
const bodyText = root.textContent?.replace(/\s+/g, " ").trim() ?? "";
|
||||
const published = root.querySelector("time[datetime]")?.getAttribute("datetime") ?? undefined;
|
||||
const coverImage = root.querySelector("img[src]")?.getAttribute("src") ?? undefined;
|
||||
|
||||
return {
|
||||
metadata: {
|
||||
...context.baseMetadata,
|
||||
url: originalUrl,
|
||||
title: pickContentTitle(root, context.baseMetadata.title),
|
||||
description: summarize(bodyText, 220) ?? context.baseMetadata.description,
|
||||
published: pickString(published, context.baseMetadata.published) ?? undefined,
|
||||
coverImage: pickString(coverImage, context.baseMetadata.coverImage) ?? undefined,
|
||||
},
|
||||
markdown,
|
||||
rawHtml: context.html,
|
||||
conversionMethod: "parser:archive-ph",
|
||||
};
|
||||
}
|
||||
|
||||
export const archivePhRuleParser: UrlRuleParser = {
|
||||
id: "archive-ph",
|
||||
supports(context) {
|
||||
return isArchiveHost(context.url);
|
||||
},
|
||||
parse: parseArchivePage,
|
||||
};
|
||||
@@ -0,0 +1,10 @@
|
||||
import { archivePhRuleParser } from "./archive-ph.js";
|
||||
import { xArticleRuleParser } from "./x-article.js";
|
||||
import { xStatusRuleParser } from "./x-status.js";
|
||||
import type { UrlRuleParser } from "../types.js";
|
||||
|
||||
export const URL_RULE_PARSERS: UrlRuleParser[] = [
|
||||
archivePhRuleParser,
|
||||
xArticleRuleParser,
|
||||
xStatusRuleParser,
|
||||
];
|
||||
@@ -0,0 +1,137 @@
|
||||
import {
|
||||
normalizeMarkdown,
|
||||
pickString,
|
||||
type ConversionResult,
|
||||
} from "../../markdown-conversion-shared.js";
|
||||
import type { UrlRuleParser, UrlRuleParserContext } from "../types.js";
|
||||
import {
|
||||
cleanText,
|
||||
collectMediaMarkdown,
|
||||
convertXRichTextElementToMarkdown,
|
||||
extractPublishedForCurrentUrl,
|
||||
inferLanguage,
|
||||
isXArticlePath,
|
||||
isXHost,
|
||||
normalizeXMarkdown,
|
||||
parseUrl,
|
||||
pickFirstValidLinkText,
|
||||
sanitizeCoverImage,
|
||||
summarizeText,
|
||||
} from "./x-shared.js";
|
||||
|
||||
function collectArticleMarkdown(root: Element): { markdown: string; mediaUrls: string[] } {
|
||||
const parts: string[] = [];
|
||||
const seenMedia = new Set<string>();
|
||||
const mediaUrls: string[] = [];
|
||||
|
||||
function pushPart(value: string): void {
|
||||
const normalized = normalizeMarkdown(value);
|
||||
if (!normalized) return;
|
||||
parts.push(normalized);
|
||||
}
|
||||
|
||||
function walk(node: Element): void {
|
||||
const testId = node.getAttribute("data-testid");
|
||||
|
||||
if (testId === "twitterArticleRichTextView" || testId === "longformRichTextComponent") {
|
||||
const bodyMedia = collectMediaMarkdown(node, seenMedia);
|
||||
mediaUrls.push(...bodyMedia.urls.filter((url) => !mediaUrls.includes(url)));
|
||||
pushPart(convertXRichTextElementToMarkdown(node));
|
||||
return;
|
||||
}
|
||||
|
||||
if (testId === "tweetPhoto") {
|
||||
const media = collectMediaMarkdown(node, seenMedia);
|
||||
mediaUrls.push(...media.urls.filter((url) => !mediaUrls.includes(url)));
|
||||
for (const line of media.lines) pushPart(line);
|
||||
return;
|
||||
}
|
||||
|
||||
if (
|
||||
testId === "twitter-article-title" ||
|
||||
testId === "User-Name" ||
|
||||
testId === "Tweet-User-Avatar" ||
|
||||
testId === "reply" ||
|
||||
testId === "retweet" ||
|
||||
testId === "like" ||
|
||||
testId === "bookmark" ||
|
||||
testId === "caret" ||
|
||||
testId === "app-text-transition-container"
|
||||
) {
|
||||
return;
|
||||
}
|
||||
|
||||
if (node.tagName === "TIME" || node.tagName === "BUTTON") {
|
||||
return;
|
||||
}
|
||||
|
||||
for (const child of Array.from(node.children)) {
|
||||
walk(child);
|
||||
}
|
||||
}
|
||||
|
||||
for (const child of Array.from(root.children)) {
|
||||
walk(child);
|
||||
}
|
||||
|
||||
return {
|
||||
markdown: normalizeXMarkdown(parts.join("\n\n")),
|
||||
mediaUrls,
|
||||
};
|
||||
}
|
||||
|
||||
function parseXArticle(context: UrlRuleParserContext): ConversionResult | null {
|
||||
const articleRoot = context.document.querySelector("[data-testid='twitterArticleReadView']") as Element | null;
|
||||
if (!articleRoot) return null;
|
||||
|
||||
const title = cleanText(
|
||||
context.document.querySelector("[data-testid='twitter-article-title']")?.textContent
|
||||
);
|
||||
const identity = pickFirstValidLinkText(
|
||||
context.document.querySelector("[data-testid='User-Name']")
|
||||
);
|
||||
const published = extractPublishedForCurrentUrl(articleRoot, context.url);
|
||||
const { markdown, mediaUrls } = collectArticleMarkdown(articleRoot);
|
||||
if (!markdown) return null;
|
||||
|
||||
const bodyText = cleanText(
|
||||
context.document.querySelector("[data-testid='twitterArticleRichTextView']")?.textContent ??
|
||||
context.document.querySelector("[data-testid='longformRichTextComponent']")?.textContent
|
||||
);
|
||||
|
||||
return {
|
||||
metadata: {
|
||||
...context.baseMetadata,
|
||||
title: pickString(title, context.baseMetadata.title) ?? "",
|
||||
description: summarizeText(bodyText, 220) ?? context.baseMetadata.description,
|
||||
author: pickString(identity.author, context.baseMetadata.author) ?? undefined,
|
||||
published: pickString(published, context.baseMetadata.published) ?? undefined,
|
||||
coverImage: sanitizeCoverImage(mediaUrls[0], context.baseMetadata.coverImage),
|
||||
language: inferLanguage(bodyText, context.baseMetadata.language),
|
||||
},
|
||||
markdown,
|
||||
rawHtml: context.html,
|
||||
conversionMethod: "parser:x-article",
|
||||
};
|
||||
}
|
||||
|
||||
export const xArticleRuleParser: UrlRuleParser = {
|
||||
id: "x-article",
|
||||
supports(context) {
|
||||
const parsed = parseUrl(context.url);
|
||||
if (!parsed || !isXHost(parsed.hostname)) {
|
||||
return false;
|
||||
}
|
||||
|
||||
return (
|
||||
isXArticlePath(parsed.pathname) ||
|
||||
Boolean(
|
||||
context.document.querySelector("[data-testid='twitterArticleReadView']") ||
|
||||
context.document.querySelector("[data-testid='twitterArticleRichTextView']")
|
||||
)
|
||||
);
|
||||
},
|
||||
parse(context) {
|
||||
return parseXArticle(context);
|
||||
},
|
||||
};
|
||||
@@ -0,0 +1,249 @@
|
||||
import { convertHtmlFragmentToMarkdown } from "../../legacy-converter.js";
|
||||
import { normalizeMarkdown } from "../../markdown-conversion-shared.js";
|
||||
|
||||
export const DEFAULT_X_OG_IMAGE = "https://abs.twimg.com/rweb/ssr/default/v2/og/image.png";
|
||||
|
||||
export type MediaResult = {
|
||||
lines: string[];
|
||||
urls: string[];
|
||||
};
|
||||
|
||||
export function isXHost(hostname: string): boolean {
|
||||
const normalized = hostname.toLowerCase();
|
||||
return (
|
||||
normalized === "x.com" ||
|
||||
normalized === "twitter.com" ||
|
||||
normalized.endsWith(".x.com") ||
|
||||
normalized.endsWith(".twitter.com")
|
||||
);
|
||||
}
|
||||
|
||||
export function parseUrl(input: string): URL | null {
|
||||
try {
|
||||
return new URL(input);
|
||||
} catch {
|
||||
return null;
|
||||
}
|
||||
}
|
||||
|
||||
export function isXStatusPath(pathname: string): boolean {
|
||||
return /^\/[^/]+\/status(?:es)?\/\d+$/i.test(pathname) || /^\/i\/web\/status\/\d+$/i.test(pathname);
|
||||
}
|
||||
|
||||
export function isXArticlePath(pathname: string): boolean {
|
||||
return /^\/[^/]+\/article\/\d+$/i.test(pathname) || /^\/(?:i\/)?article\/\d+$/i.test(pathname);
|
||||
}
|
||||
|
||||
export function cleanText(value: string | null | undefined): string {
|
||||
return (value ?? "").replace(/\s+/g, " ").trim();
|
||||
}
|
||||
|
||||
export function cleanUserLabel(value: string | null | undefined): string {
|
||||
return cleanText(value).replace(/\bVerified account\b/gi, "").replace(/\s{2,}/g, " ").trim();
|
||||
}
|
||||
|
||||
export function escapeMarkdownAlt(text: string): string {
|
||||
return text.replace(/[\[\]]/g, "\\$&");
|
||||
}
|
||||
|
||||
export function normalizeAlt(text: string | null | undefined): string {
|
||||
const cleaned = cleanText(text);
|
||||
if (!cleaned || /^(image|photo)$/i.test(cleaned)) return "";
|
||||
return escapeMarkdownAlt(cleaned);
|
||||
}
|
||||
|
||||
export function summarizeText(text: string, maxLength: number): string | undefined {
|
||||
const normalized = cleanText(text);
|
||||
if (!normalized) return undefined;
|
||||
return normalized.length > maxLength
|
||||
? `${normalized.slice(0, maxLength - 3)}...`
|
||||
: normalized;
|
||||
}
|
||||
|
||||
export function buildTweetTitle(text: string, fallback: string): string {
|
||||
return summarizeText(text, 80) ?? fallback;
|
||||
}
|
||||
|
||||
export function normalizeXMarkdown(markdown: string): string {
|
||||
return normalizeMarkdown(markdown.replace(/^(#{1,6})\s*\n+([^\n])/gm, "$1 $2"));
|
||||
}
|
||||
|
||||
export function inferLanguage(text: string, fallback?: string): string | undefined {
|
||||
const normalized = cleanText(text);
|
||||
if (!normalized) return fallback;
|
||||
|
||||
const han = (normalized.match(/\p{Script=Han}/gu) || []).length;
|
||||
const hiragana = (normalized.match(/\p{Script=Hiragana}/gu) || []).length;
|
||||
const katakana = (normalized.match(/\p{Script=Katakana}/gu) || []).length;
|
||||
const hangul = (normalized.match(/\p{Script=Hangul}/gu) || []).length;
|
||||
|
||||
if (hangul >= 8) return "ko";
|
||||
if (hiragana + katakana >= 8) return "ja";
|
||||
if (han >= 16) return "zh";
|
||||
return fallback;
|
||||
}
|
||||
|
||||
export function buildQuoteMarkdown(markdown: string, author?: string): string {
|
||||
const normalized = normalizeMarkdown(markdown);
|
||||
if (!normalized) return "";
|
||||
|
||||
const lines = normalized.split("\n");
|
||||
const prefixed = lines.map((line) => (line ? `> ${line}` : ">")).join("\n");
|
||||
const header = author ? `> Quote from ${author}` : "> Quote";
|
||||
return `${header}\n${prefixed}`;
|
||||
}
|
||||
|
||||
export function pickFirstValidLinkText(userNameEl: Element | null | undefined): {
|
||||
name?: string;
|
||||
username?: string;
|
||||
author?: string;
|
||||
} {
|
||||
if (!userNameEl) return {};
|
||||
|
||||
const linkTexts = Array.from(userNameEl.querySelectorAll("a[href]"))
|
||||
.map((link) => cleanUserLabel(link.textContent))
|
||||
.filter(Boolean);
|
||||
|
||||
let username = linkTexts.find((text) => text.startsWith("@"));
|
||||
let name = linkTexts.find((text) => !text.startsWith("@") && !/^(promote|more)$/i.test(text));
|
||||
|
||||
if (!username || !name) {
|
||||
const text = cleanUserLabel(userNameEl.textContent);
|
||||
const fallbackMatch = text.match(/^(.*?)\s*(@[A-Za-z0-9_]+)(?:\s*·.*)?$/);
|
||||
if (fallbackMatch) {
|
||||
name = name ?? cleanText(fallbackMatch[1]);
|
||||
username = username ?? cleanText(fallbackMatch[2]);
|
||||
}
|
||||
}
|
||||
|
||||
const author = name && username ? `${name} (${username})` : username ?? name;
|
||||
return { name, username, author };
|
||||
}
|
||||
|
||||
export function extractPublishedForCurrentUrl(root: ParentNode, url: string): string | undefined {
|
||||
const parsed = parseUrl(url);
|
||||
if (!parsed) return undefined;
|
||||
const currentPath = parsed.pathname.toLowerCase();
|
||||
|
||||
for (const timeElement of root.querySelectorAll("a[href] time[datetime]")) {
|
||||
const href = timeElement.closest("a")?.getAttribute("href");
|
||||
const hrefUrl = href ? parseUrl(href.startsWith("http") ? href : `${parsed.origin}${href}`) : null;
|
||||
if (hrefUrl?.pathname.toLowerCase() === currentPath) {
|
||||
return timeElement.getAttribute("datetime") ?? undefined;
|
||||
}
|
||||
}
|
||||
|
||||
return root.querySelector("time[datetime]")?.getAttribute("datetime") ?? undefined;
|
||||
}
|
||||
|
||||
export function collectMediaMarkdown(root: ParentNode, seen: Set<string>): MediaResult {
|
||||
const lines: string[] = [];
|
||||
const urls: string[] = [];
|
||||
const rootElement = root as Element & {
|
||||
getAttribute?: (name: string) => string | null;
|
||||
};
|
||||
const photoNodes = [
|
||||
...(typeof rootElement.getAttribute === "function" &&
|
||||
rootElement.getAttribute("data-testid") === "tweetPhoto"
|
||||
? [rootElement]
|
||||
: []),
|
||||
...Array.from(root.querySelectorAll("[data-testid='tweetPhoto']")),
|
||||
];
|
||||
|
||||
for (const node of photoNodes) {
|
||||
const img = node.querySelector("img");
|
||||
const imageUrl = img?.getAttribute("src");
|
||||
if (imageUrl && !seen.has(imageUrl)) {
|
||||
seen.add(imageUrl);
|
||||
urls.push(imageUrl);
|
||||
lines.push(``);
|
||||
}
|
||||
|
||||
const video = node.querySelector("video");
|
||||
const posterUrl = video?.getAttribute("poster");
|
||||
if (posterUrl && !seen.has(posterUrl)) {
|
||||
seen.add(posterUrl);
|
||||
urls.push(posterUrl);
|
||||
lines.push(``);
|
||||
}
|
||||
|
||||
const videoUrl = video?.getAttribute("src") ?? video?.querySelector("source")?.getAttribute("src");
|
||||
if (videoUrl && !seen.has(videoUrl)) {
|
||||
seen.add(videoUrl);
|
||||
urls.push(videoUrl);
|
||||
lines.push(`[video](${videoUrl})`);
|
||||
}
|
||||
}
|
||||
|
||||
return { lines, urls };
|
||||
}
|
||||
|
||||
export function materializeTweetPhotoNodes(root: Element): void {
|
||||
for (const photo of Array.from(root.querySelectorAll("[data-testid='tweetPhoto']"))) {
|
||||
const document = photo.ownerDocument;
|
||||
const container = document.createElement("span");
|
||||
|
||||
const img = photo.querySelector("img");
|
||||
const imageUrl = img?.getAttribute("src");
|
||||
if (imageUrl) {
|
||||
const image = document.createElement("img");
|
||||
image.setAttribute("src", imageUrl);
|
||||
const alt = normalizeAlt(img?.getAttribute("alt"));
|
||||
if (alt) {
|
||||
image.setAttribute("alt", alt);
|
||||
}
|
||||
container.appendChild(image);
|
||||
}
|
||||
|
||||
const video = photo.querySelector("video");
|
||||
const posterUrl = video?.getAttribute("poster");
|
||||
if (posterUrl) {
|
||||
const poster = document.createElement("img");
|
||||
poster.setAttribute("src", posterUrl);
|
||||
poster.setAttribute("alt", "video");
|
||||
container.appendChild(poster);
|
||||
}
|
||||
|
||||
const videoUrl = video?.getAttribute("src") ?? video?.querySelector("source")?.getAttribute("src");
|
||||
if (videoUrl) {
|
||||
if (container.childNodes.length > 0) {
|
||||
container.appendChild(document.createTextNode(" "));
|
||||
}
|
||||
const link = document.createElement("a");
|
||||
link.setAttribute("href", videoUrl);
|
||||
link.textContent = "video";
|
||||
container.appendChild(link);
|
||||
}
|
||||
|
||||
if (container.childNodes.length === 0) {
|
||||
photo.remove();
|
||||
continue;
|
||||
}
|
||||
|
||||
photo.replaceWith(container);
|
||||
}
|
||||
}
|
||||
|
||||
function collapseLinkedMediaContainers(root: Element): void {
|
||||
for (const anchor of Array.from(root.querySelectorAll("a[href]"))) {
|
||||
const images = Array.from(anchor.querySelectorAll("img"));
|
||||
if (images.length !== 1) continue;
|
||||
if (cleanText(anchor.textContent)) continue;
|
||||
|
||||
const image = images[0].cloneNode(true);
|
||||
anchor.replaceChildren(image);
|
||||
}
|
||||
}
|
||||
|
||||
export function convertXRichTextElementToMarkdown(node: Element): string {
|
||||
const clone = node.cloneNode(true) as Element;
|
||||
materializeTweetPhotoNodes(clone);
|
||||
collapseLinkedMediaContainers(clone);
|
||||
return normalizeXMarkdown(convertHtmlFragmentToMarkdown(clone.innerHTML));
|
||||
}
|
||||
|
||||
export function sanitizeCoverImage(primary?: string, fallback?: string): string | undefined {
|
||||
if (primary) return primary;
|
||||
if (!fallback || fallback === DEFAULT_X_OG_IMAGE) return undefined;
|
||||
return fallback;
|
||||
}
|
||||
@@ -0,0 +1,82 @@
|
||||
import type { ConversionResult } from "../../markdown-conversion-shared.js";
|
||||
import type { UrlRuleParser, UrlRuleParserContext } from "../types.js";
|
||||
import {
|
||||
buildQuoteMarkdown,
|
||||
buildTweetTitle,
|
||||
cleanText,
|
||||
collectMediaMarkdown,
|
||||
convertXRichTextElementToMarkdown,
|
||||
extractPublishedForCurrentUrl,
|
||||
inferLanguage,
|
||||
isXHost,
|
||||
isXStatusPath,
|
||||
normalizeXMarkdown,
|
||||
parseUrl,
|
||||
pickFirstValidLinkText,
|
||||
sanitizeCoverImage,
|
||||
summarizeText,
|
||||
} from "./x-shared.js";
|
||||
|
||||
function parseXStatus(context: UrlRuleParserContext): ConversionResult | null {
|
||||
const article = context.document.querySelector("article[data-testid='tweet'], article") as Element | null;
|
||||
if (!article) return null;
|
||||
|
||||
const tweetTextElements = Array.from(article.querySelectorAll("[data-testid='tweetText']")) as Element[];
|
||||
if (tweetTextElements.length === 0) return null;
|
||||
|
||||
const userNameElements = Array.from(article.querySelectorAll("[data-testid='User-Name']")) as Element[];
|
||||
const mainTextElement = tweetTextElements[0];
|
||||
const mainIdentity = pickFirstValidLinkText(userNameElements[0]);
|
||||
const published = extractPublishedForCurrentUrl(article, context.url);
|
||||
const mainMarkdown = normalizeXMarkdown(convertXRichTextElementToMarkdown(mainTextElement));
|
||||
if (!mainMarkdown) return null;
|
||||
|
||||
const parts = [mainMarkdown];
|
||||
const quotedTextElements = tweetTextElements.slice(1);
|
||||
const quotedUserNameElements = userNameElements.slice(1);
|
||||
|
||||
quotedTextElements.forEach((element, index) => {
|
||||
const quoteMarkdown = normalizeXMarkdown(convertXRichTextElementToMarkdown(element));
|
||||
if (!quoteMarkdown) return;
|
||||
const quoteIdentity = pickFirstValidLinkText(quotedUserNameElements[index]);
|
||||
parts.push(buildQuoteMarkdown(quoteMarkdown, quoteIdentity.author));
|
||||
});
|
||||
|
||||
const media = collectMediaMarkdown(article, new Set<string>());
|
||||
if (media.lines.length > 0) {
|
||||
parts.push(media.lines.join("\n\n"));
|
||||
}
|
||||
|
||||
const mainText = cleanText(mainTextElement.textContent);
|
||||
const markdown = normalizeXMarkdown(parts.join("\n\n"));
|
||||
|
||||
return {
|
||||
metadata: {
|
||||
...context.baseMetadata,
|
||||
title: buildTweetTitle(mainText, context.baseMetadata.title),
|
||||
description: summarizeText(mainText, 220) ?? context.baseMetadata.description,
|
||||
author: mainIdentity.author ?? context.baseMetadata.author,
|
||||
published: published ?? context.baseMetadata.published,
|
||||
coverImage: sanitizeCoverImage(media.urls[0], context.baseMetadata.coverImage),
|
||||
language: inferLanguage(mainText, context.baseMetadata.language),
|
||||
},
|
||||
markdown,
|
||||
rawHtml: context.html,
|
||||
conversionMethod: "parser:x-status",
|
||||
};
|
||||
}
|
||||
|
||||
export const xStatusRuleParser: UrlRuleParser = {
|
||||
id: "x-status",
|
||||
supports(context) {
|
||||
const parsed = parseUrl(context.url);
|
||||
if (!parsed || !isXHost(parsed.hostname)) {
|
||||
return false;
|
||||
}
|
||||
|
||||
return isXStatusPath(parsed.pathname) && Boolean(context.document.querySelector("[data-testid='tweetText']"));
|
||||
},
|
||||
parse(context): ConversionResult | null {
|
||||
return parseXStatus(context);
|
||||
},
|
||||
};
|
||||
@@ -0,0 +1,14 @@
|
||||
import type { ConversionResult, PageMetadata } from "../markdown-conversion-shared.js";
|
||||
|
||||
export interface UrlRuleParserContext {
|
||||
html: string;
|
||||
url: string;
|
||||
document: Document;
|
||||
baseMetadata: PageMetadata;
|
||||
}
|
||||
|
||||
export interface UrlRuleParser {
|
||||
id: string;
|
||||
supports(context: UrlRuleParserContext): boolean;
|
||||
parse(context: UrlRuleParserContext): ConversionResult | null;
|
||||
}
|
||||
@@ -151,8 +151,7 @@ From outline entry:
|
||||
```markdown
|
||||
## Watermark
|
||||
|
||||
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 main content.
|
||||
```
|
||||
|
||||
@@ -295,19 +294,19 @@ Create a Xiaohongshu (Little Red Book) style infographic following these guideli
|
||||
## Content
|
||||
|
||||
**Position**: Content (Page 3 of 6)
|
||||
**Core Message**: ChatGPT使用技巧
|
||||
**Core Message**: ChatGPT 使用技巧
|
||||
|
||||
**Text Content**:
|
||||
- Title: 「ChatGPT」
|
||||
- Subtitle: 最强AI助手
|
||||
- Subtitle: 最强 AI 助手
|
||||
- Points:
|
||||
- 写文案:给出框架,秒出初稿
|
||||
- 改文章:润色、翻译、总结
|
||||
- 编程:写代码、找bug
|
||||
- 编程:写代码、找 bug
|
||||
- 学习:解释概念、出题练习
|
||||
|
||||
**Visual Concept**:
|
||||
ChatGPT logo居中,四周放射状展示功能点
|
||||
ChatGPT logo 居中,四周放射状展示功能点
|
||||
深色科技背景,霓虹绿点缀
|
||||
|
||||
---
|
||||
|
||||
@@ -0,0 +1,186 @@
|
||||
---
|
||||
name: baoyu-youtube-transcript
|
||||
description: Downloads YouTube video transcripts/subtitles and cover images by URL or video ID. Supports multiple languages, translation, chapters, and speaker identification. Caches raw data for fast re-formatting. Use when user asks to "get YouTube transcript", "download subtitles", "get captions", "YouTube字幕", "YouTube封面", "视频封面", "video thumbnail", "video cover image", or provides a YouTube URL and wants the transcript/subtitle text or cover image extracted.
|
||||
version: 1.1.0
|
||||
metadata:
|
||||
openclaw:
|
||||
homepage: https://github.com/JimLiu/baoyu-skills#baoyu-youtube-transcript
|
||||
requires:
|
||||
anyBins:
|
||||
- bun
|
||||
- npx
|
||||
---
|
||||
|
||||
# YouTube Transcript
|
||||
|
||||
Downloads transcripts (subtitles/captions) from YouTube videos. Works with both manually created and auto-generated transcripts. No API key or browser required — uses YouTube's InnerTube API directly and automatically falls back to `yt-dlp` when YouTube blocks the direct API path.
|
||||
|
||||
Fetches video metadata and cover image on first run, caches raw data for fast re-formatting.
|
||||
|
||||
## Script Directory
|
||||
|
||||
Scripts in `scripts/` subdirectory. `{baseDir}` = this SKILL.md's directory path. Resolve `${BUN_X}` runtime: if `bun` installed → `bun`; if `npx` available → `npx -y bun`; else suggest installing bun. Replace `{baseDir}` and `${BUN_X}` with actual values.
|
||||
|
||||
| Script | Purpose |
|
||||
|--------|---------|
|
||||
| `scripts/main.ts` | Transcript download CLI |
|
||||
|
||||
## Usage
|
||||
|
||||
```bash
|
||||
# Default: markdown with timestamps (English)
|
||||
${BUN_X} {baseDir}/scripts/main.ts <youtube-url-or-id>
|
||||
|
||||
# Specify languages (priority order)
|
||||
${BUN_X} {baseDir}/scripts/main.ts <url> --languages zh,en,ja
|
||||
|
||||
# Without timestamps
|
||||
${BUN_X} {baseDir}/scripts/main.ts <url> --no-timestamps
|
||||
|
||||
# With chapter segmentation
|
||||
${BUN_X} {baseDir}/scripts/main.ts <url> --chapters
|
||||
|
||||
# With speaker identification (requires AI post-processing)
|
||||
${BUN_X} {baseDir}/scripts/main.ts <url> --speakers
|
||||
|
||||
# SRT subtitle file
|
||||
${BUN_X} {baseDir}/scripts/main.ts <url> --format srt
|
||||
|
||||
# Translate transcript
|
||||
${BUN_X} {baseDir}/scripts/main.ts <url> --translate zh-Hans
|
||||
|
||||
# List available transcripts
|
||||
${BUN_X} {baseDir}/scripts/main.ts <url> --list
|
||||
|
||||
# Force re-fetch (ignore cache)
|
||||
${BUN_X} {baseDir}/scripts/main.ts <url> --refresh
|
||||
```
|
||||
|
||||
## Options
|
||||
|
||||
| Option | Description | Default |
|
||||
|--------|-------------|---------|
|
||||
| `<url-or-id>` | YouTube URL or video ID (multiple allowed) | Required |
|
||||
| `--languages <codes>` | Language codes, comma-separated, in priority order | `en` |
|
||||
| `--format <fmt>` | Output format: `text`, `srt` | `text` |
|
||||
| `--translate <code>` | Translate to specified language code | |
|
||||
| `--list` | List available transcripts instead of fetching | |
|
||||
| `--timestamps` | Include `[HH:MM:SS → HH:MM:SS]` timestamps per paragraph | on |
|
||||
| `--no-timestamps` | Disable timestamps | |
|
||||
| `--chapters` | Chapter segmentation from video description | |
|
||||
| `--speakers` | Raw transcript with metadata for speaker identification | |
|
||||
| `--exclude-generated` | Skip auto-generated transcripts | |
|
||||
| `--exclude-manually-created` | Skip manually created transcripts | |
|
||||
| `--refresh` | Force re-fetch, ignore cached data | |
|
||||
| `-o, --output <path>` | Save to specific file path | auto-generated |
|
||||
| `--output-dir <dir>` | Base output directory | `youtube-transcript` |
|
||||
|
||||
## Optional Environment Variables
|
||||
|
||||
| Variable | Description |
|
||||
|----------|-------------|
|
||||
| `YOUTUBE_TRANSCRIPT_COOKIES_FROM_BROWSER` | Passed to `yt-dlp --cookies-from-browser` during fallback, e.g. `chrome`, `safari`, `firefox`, or `chrome:Profile 1` |
|
||||
|
||||
## Input Formats
|
||||
|
||||
Accepts any of these as video input:
|
||||
- Full URL: `https://www.youtube.com/watch?v=dQw4w9WgXcQ`
|
||||
- Short URL: `https://youtu.be/dQw4w9WgXcQ`
|
||||
- Embed URL: `https://www.youtube.com/embed/dQw4w9WgXcQ`
|
||||
- Shorts URL: `https://www.youtube.com/shorts/dQw4w9WgXcQ`
|
||||
- Video ID: `dQw4w9WgXcQ`
|
||||
|
||||
## Output Formats
|
||||
|
||||
| Format | Extension | Description |
|
||||
|--------|-----------|-------------|
|
||||
| `text` | `.md` | Markdown with frontmatter (incl. `description`), title heading, summary, optional TOC/cover/timestamps/chapters/speakers |
|
||||
| `srt` | `.srt` | SubRip subtitle format for video players |
|
||||
|
||||
## Output Directory
|
||||
|
||||
```
|
||||
youtube-transcript/
|
||||
├── .index.json # Video ID → directory path mapping (for cache lookup)
|
||||
└── {channel-slug}/{title-full-slug}/
|
||||
├── meta.json # Video metadata (title, channel, description, duration, chapters, etc.)
|
||||
├── transcript-raw.json # Raw transcript snippets from YouTube API (cached)
|
||||
├── transcript-sentences.json # Sentence-segmented transcript (split by punctuation, merged across snippets)
|
||||
├── imgs/
|
||||
│ └── cover.jpg # Video thumbnail
|
||||
├── transcript.md # Markdown transcript (generated from sentences)
|
||||
└── transcript.srt # SRT subtitle (generated from raw snippets, if --format srt)
|
||||
```
|
||||
|
||||
- `{channel-slug}`: Channel name in kebab-case
|
||||
- `{title-full-slug}`: Full video title in kebab-case
|
||||
|
||||
The `--list` mode outputs to stdout only (no file saved).
|
||||
|
||||
## Caching
|
||||
|
||||
On first fetch, the script saves:
|
||||
- `meta.json` — video metadata, chapters, cover image path, language info
|
||||
- `transcript-raw.json` — raw transcript snippets from YouTube API (`{ text, start, duration }[]`)
|
||||
- `transcript-sentences.json` — sentence-segmented transcript (`{ text, start: "HH:mm:ss", end: "HH:mm:ss" }[]`), split by sentence-ending punctuation (`.?!…。?!` etc.), timestamps proportionally allocated by character length, CJK-aware text merging
|
||||
- `imgs/cover.jpg` — video thumbnail
|
||||
|
||||
Subsequent runs for the same video use cached data (no network calls). Use `--refresh` to force re-fetch. If a different language is requested, the cache is automatically refreshed.
|
||||
|
||||
When YouTube returns anti-bot / blocked responses on the direct InnerTube path, the script retries with alternate client identities and then falls back to `yt-dlp` if available. If fallback is needed but `yt-dlp` is unavailable, the agent should decide how to make `yt-dlp` available and continue rather than pushing the installation decision to the user.
|
||||
|
||||
SRT output (`--format srt`) is generated from `transcript-raw.json`. Text/markdown output uses `transcript-sentences.json` for natural sentence boundaries.
|
||||
|
||||
## Workflow
|
||||
|
||||
When user provides a YouTube URL and wants the transcript:
|
||||
|
||||
1. Run with `--list` first if the user hasn't specified a language, to show available options
|
||||
2. **Always single-quote the URL** when running the script — zsh treats `?` as a glob wildcard, so an unquoted YouTube URL causes "no matches found": use `'https://www.youtube.com/watch?v=ID'`
|
||||
3. Default: run with `--chapters --speakers` for the richest output (chapters + speaker identification)
|
||||
3. The script auto-saves cached data + output file and prints the file path
|
||||
4. For `--speakers` mode: after the script saves the raw file, follow the speaker identification workflow below to post-process with speaker labels
|
||||
|
||||
When user only wants a cover image or metadata, running the script with any option will also cache `meta.json` and `imgs/cover.jpg`.
|
||||
|
||||
When re-formatting the same video (e.g., first text then SRT), the cached data is reused — no re-fetch needed.
|
||||
|
||||
## Chapter & Speaker Workflow
|
||||
|
||||
### Chapters (`--chapters`)
|
||||
|
||||
The script parses chapter timestamps from the video description (e.g., `0:00 Introduction`), segments the transcript by chapter boundaries, groups snippets into readable paragraphs, and saves as `.md` with a Table of Contents. No further processing needed.
|
||||
|
||||
If no chapter timestamps exist in the description, the transcript is output as grouped paragraphs without chapter headings.
|
||||
|
||||
### Speaker Identification (`--speakers`)
|
||||
|
||||
Speaker identification requires AI processing. The script outputs a raw `.md` file containing:
|
||||
- YAML frontmatter with video metadata (title, channel, date, cover, description, language)
|
||||
- Video description (for speaker name extraction)
|
||||
- Chapter list from description (if available)
|
||||
- Raw transcript in SRT format (pre-computed start/end timestamps, token-efficient)
|
||||
|
||||
After the script saves the raw file, spawn a sub-agent (use a cheaper model like Sonnet for cost efficiency) to process speaker identification:
|
||||
|
||||
1. Read the saved `.md` file
|
||||
2. Read the prompt template at `{baseDir}/prompts/speaker-transcript.md`
|
||||
3. Process the raw transcript following the prompt:
|
||||
- Identify speakers using video metadata (title → guest, channel → host, description → names)
|
||||
- Detect speaker turns from conversation flow, question-answer patterns, and contextual cues
|
||||
- Segment into chapters (use description chapters if available, else create from topic shifts)
|
||||
- Format with `**Speaker Name:**` labels, paragraph grouping (2-4 sentences), and `[HH:MM:SS → HH:MM:SS]` timestamps
|
||||
4. Overwrite the `.md` file with the processed transcript (keep the YAML frontmatter)
|
||||
|
||||
When `--speakers` is used, `--chapters` is implied — the processed output always includes chapter segmentation.
|
||||
|
||||
## Error Cases
|
||||
|
||||
| Error | Meaning |
|
||||
|-------|---------|
|
||||
| Transcripts disabled | Video has no captions at all |
|
||||
| No transcript found | Requested language not available |
|
||||
| Video unavailable | Video deleted, private, or region-locked |
|
||||
| IP blocked | Too many requests, try again later |
|
||||
| Age restricted | Video requires login for age verification |
|
||||
| bot detected | The script retries alternate clients and then `yt-dlp`; if fallback tooling is missing, the agent should resolve that itself, otherwise if it still fails try `YOUTUBE_TRANSCRIPT_COOKIES_FROM_BROWSER=safari` (or your browser) |
|
||||
@@ -0,0 +1,118 @@
|
||||
# Speaker & Chapter Transcript Processing
|
||||
|
||||
You are an expert transcript specialist. Process the raw transcript file (with YAML frontmatter metadata and SRT-formatted transcript) into a structured, verbatim transcript with speaker identification and chapter segmentation.
|
||||
|
||||
## Output Structure
|
||||
|
||||
Produce a single cohesive markdown file containing:
|
||||
1. YAML frontmatter (keep the original frontmatter from the raw file, which includes `description`)
|
||||
2. `# Title` heading (from frontmatter title)
|
||||
3. Description/summary paragraph (from frontmatter `description`)
|
||||
4. Table of Contents
|
||||
5. Cover image (if `cover` exists in frontmatter): `` — right after the ToC
|
||||
6. Full chapter-segmented transcript with speaker labels
|
||||
|
||||
Use the same language as the transcription for the title and ToC.
|
||||
|
||||
## Rules
|
||||
|
||||
### Transcription Fidelity
|
||||
- Preserve every spoken word exactly, including filler words (`um`, `uh`, `like`) and stutters
|
||||
- **NEVER translate.** If the audio mixes languages (e.g., "这个 feature 很酷"), replicate that mix exactly
|
||||
|
||||
### Speaker Identification
|
||||
- **Priority 1: Use metadata.** Analyze the video's title, channel name, and description to identify speakers
|
||||
- **Priority 2: Use transcript content.** Look for introductions, how speakers address each other, contextual cues
|
||||
- **Fallback:** Use consistent generic labels (`**Speaker 1:**`, `**Host:**`, etc.)
|
||||
- **Consistency:** If a speaker's name is revealed later, update ALL previous labels for that speaker
|
||||
|
||||
### Chapter Generation
|
||||
- If the raw file contains a `# Chapters` section, use those as the primary basis for segmenting
|
||||
- Otherwise, create chapters based on significant topic shifts in the conversation
|
||||
|
||||
### Input Format
|
||||
- The `# Transcript` section contains SRT-formatted subtitles with pre-computed start/end timestamps
|
||||
- Each SRT block has: sequence number, `HH:MM:SS,mmm --> HH:MM:SS,mmm` timestamp line, and text
|
||||
- Use the SRT timestamps directly — no need to calculate paragraph start/end times, just merge adjacent blocks
|
||||
|
||||
### Formatting
|
||||
|
||||
**Timestamps:** Use `[HH:MM:SS → HH:MM:SS]` format (start → end) at the end of each paragraph. No milliseconds.
|
||||
|
||||
**Table of Contents:**
|
||||
```
|
||||
## Table of Contents
|
||||
* [HH:MM:SS] Chapter Title
|
||||
```
|
||||
|
||||
**Chapters:**
|
||||
```
|
||||
## Chapter Title [HH:MM:SS]
|
||||
```
|
||||
Two blank lines between chapters.
|
||||
|
||||
**Dialogue Paragraphs:**
|
||||
- First paragraph of a speaker's turn starts with `**Speaker Name:** `
|
||||
- Split long monologues into 2-4 sentence paragraphs separated by blank lines
|
||||
- Subsequent paragraphs from the SAME speaker do NOT repeat the speaker label
|
||||
- Every paragraph ends with exactly ONE timestamp range `[HH:MM:SS → HH:MM:SS]`
|
||||
|
||||
Correct example:
|
||||
```
|
||||
**Jane Doe:** The study focuses on long-term effects of dietary changes. We tracked two groups over five years. [00:00:15 → 00:00:21]
|
||||
|
||||
The first group followed the new regimen, while the second group maintained a traditional diet. [00:00:21 → 00:00:28]
|
||||
|
||||
**Host:** Fascinating. And what did you find? [00:00:28 → 00:00:31]
|
||||
```
|
||||
|
||||
Wrong (multiple timestamps in one paragraph):
|
||||
```
|
||||
**Host:** Welcome back. [00:00:01] Today we have a guest. [00:00:02]
|
||||
```
|
||||
|
||||
**Non-Speech Audio:** On its own line: `[Laughter] [HH:MM:SS]`
|
||||
|
||||
## Example Output
|
||||
|
||||
```markdown
|
||||
---
|
||||
title: "Example Interview"
|
||||
channel: "The Show"
|
||||
date: 2024-04-15
|
||||
url: "https://www.youtube.com/watch?v=xxx"
|
||||
cover: imgs/cover.jpg
|
||||
description: "Jane Doe discusses her groundbreaking five-year study on the long-term effects of dietary changes."
|
||||
language: en
|
||||
---
|
||||
|
||||
# Example Interview
|
||||
|
||||
Jane Doe discusses her groundbreaking five-year study on the long-term effects of dietary changes.
|
||||
|
||||
## Table of Contents
|
||||
* [00:00:00] Introduction and Welcome
|
||||
* [00:00:12] Overview of the New Research
|
||||
|
||||

|
||||
|
||||
|
||||
## Introduction and Welcome [00:00:00]
|
||||
|
||||
**Host:** Welcome back to the show. Today, we have a, uh, very special guest, Jane Doe. [00:00:00 → 00:00:03]
|
||||
|
||||
**Jane Doe:** Thank you for having me. I'm excited to be here and discuss the findings. [00:00:03 → 00:00:07]
|
||||
|
||||
|
||||
## Overview of the New Research [00:00:12]
|
||||
|
||||
**Host:** So, Jane, before we get into the nitty-gritty, could you, you know, give us a brief overview for our audience? [00:00:12 → 00:00:16]
|
||||
|
||||
**Jane Doe:** Of course. The study focuses on the long-term effects of specific dietary changes. It's a bit complicated but essentially we tracked two large groups over a five-year period. [00:00:16 → 00:00:23]
|
||||
|
||||
The first group followed the new regimen, while the second group, our control, maintained a traditional diet. This allowed us to isolate variables effectively. [00:00:23 → 00:00:30]
|
||||
|
||||
[Laughter] [00:00:30]
|
||||
|
||||
**Host:** Fascinating. And what did you find? [00:00:31 → 00:00:33]
|
||||
```
|
||||
@@ -0,0 +1,125 @@
|
||||
import test from "node:test";
|
||||
import assert from "node:assert/strict";
|
||||
|
||||
import { findTranscript, parseTranscriptJson3, parseWebVtt } from "./transcript.ts";
|
||||
import { buildTranscriptListFromYtDlp, resolveVideoSource, selectYtDlpTrack } from "./youtube.ts";
|
||||
|
||||
test("selectYtDlpTrack prefers json3 over xml and vtt", () => {
|
||||
const track = selectYtDlpTrack([
|
||||
{ ext: "vtt", url: "https://example.com/subs.vtt" },
|
||||
{ ext: "srv3", url: "https://example.com/subs.srv3" },
|
||||
{ ext: "json3", url: "https://example.com/subs.json3" },
|
||||
]);
|
||||
|
||||
assert.equal(track?.ext, "json3");
|
||||
});
|
||||
|
||||
test("buildTranscriptListFromYtDlp keeps manual and generated tracks separate", () => {
|
||||
const transcripts = buildTranscriptListFromYtDlp({
|
||||
subtitles: {
|
||||
en: [
|
||||
{ ext: "json3", url: "https://example.com/en.json3", name: "English" },
|
||||
],
|
||||
},
|
||||
automatic_captions: {
|
||||
"zh-Hans": [
|
||||
{ ext: "json3", url: "https://example.com/zh.json3", name: "Chinese (Simplified)" },
|
||||
],
|
||||
},
|
||||
});
|
||||
|
||||
assert.equal(transcripts.length, 2);
|
||||
assert.equal(transcripts[0].isGenerated, false);
|
||||
assert.equal(transcripts[1].isGenerated, true);
|
||||
assert.equal(transcripts[0].translationLanguages[0]?.languageCode, "zh-Hans");
|
||||
|
||||
const translated = findTranscript(transcripts, ["zh-Hans"], false, false);
|
||||
assert.equal(translated.languageCode, "zh-Hans");
|
||||
assert.equal(translated.isGenerated, true);
|
||||
});
|
||||
|
||||
test("parseTranscriptJson3 reads youtube timedtext json3 payloads", () => {
|
||||
const snippets = parseTranscriptJson3(JSON.stringify({
|
||||
events: [
|
||||
{
|
||||
tStartMs: 80,
|
||||
dDurationMs: 3120,
|
||||
segs: [{ utf8: "hello\nworld" }],
|
||||
},
|
||||
{
|
||||
tStartMs: 4000,
|
||||
dDurationMs: 1800,
|
||||
segs: [{ utf8: "again" }],
|
||||
},
|
||||
],
|
||||
}));
|
||||
|
||||
assert.deepEqual(snippets, [
|
||||
{ text: "hello world", start: 0.08, duration: 3.12 },
|
||||
{ text: "again", start: 4, duration: 1.8 },
|
||||
]);
|
||||
});
|
||||
|
||||
test("parseWebVtt strips tags and cue settings", () => {
|
||||
const snippets = parseWebVtt(`WEBVTT
|
||||
|
||||
00:00:00.080 --> 00:00:03.200 align:start position:0%
|
||||
<c.colorE5E5E5>Hello</c> world
|
||||
|
||||
00:00:04.000 --> 00:00:05.800
|
||||
Again
|
||||
`);
|
||||
|
||||
assert.equal(snippets.length, 2);
|
||||
assert.equal(snippets[0].text, "Hello world");
|
||||
assert.equal(snippets[0].start, 0.08);
|
||||
assert.equal(snippets[0].duration, 3.12);
|
||||
assert.equal(snippets[1].text, "Again");
|
||||
assert.equal(snippets[1].start, 4);
|
||||
assert.equal(Number(snippets[1].duration.toFixed(1)), 1.8);
|
||||
});
|
||||
|
||||
test("resolveVideoSource prefers primary InnerTube result before fallback", async () => {
|
||||
let fallbackCalled = false;
|
||||
const source = await resolveVideoSource(
|
||||
"video12345ab",
|
||||
async () => ({ kind: "innertube", data: { videoDetails: { title: "Primary" } }, transcripts: [] }),
|
||||
() => {
|
||||
fallbackCalled = true;
|
||||
return {
|
||||
subtitles: {
|
||||
en: [{ ext: "json3", url: "https://example.com/en.json3", name: "English" }],
|
||||
},
|
||||
};
|
||||
},
|
||||
() => {}
|
||||
);
|
||||
|
||||
assert.equal(source.kind, "innertube");
|
||||
assert.equal(fallbackCalled, false);
|
||||
});
|
||||
|
||||
test("resolveVideoSource falls back to yt-dlp only after fallback-eligible errors", async () => {
|
||||
let fallbackCalled = false;
|
||||
const source = await resolveVideoSource(
|
||||
"video12345ab",
|
||||
async () => {
|
||||
const error = new Error("Request blocked for video12345ab: bot detected");
|
||||
(error as Error & { code?: string }).code = "BOT_DETECTED";
|
||||
throw error;
|
||||
},
|
||||
() => {
|
||||
fallbackCalled = true;
|
||||
return {
|
||||
automatic_captions: {
|
||||
en: [{ ext: "json3", url: "https://example.com/en.json3", name: "English (auto-generated)" }],
|
||||
},
|
||||
};
|
||||
},
|
||||
() => {}
|
||||
);
|
||||
|
||||
assert.equal(source.kind, "yt-dlp");
|
||||
assert.equal(fallbackCalled, true);
|
||||
assert.equal(source.transcripts[0].languageCode, "en");
|
||||
});
|
||||
@@ -0,0 +1,251 @@
|
||||
#!/usr/bin/env bun
|
||||
import { writeFileSync } from "fs";
|
||||
import { join, resolve } from "path";
|
||||
|
||||
import { extractVideoId, slugify } from "./shared.ts";
|
||||
import {
|
||||
ensureDir,
|
||||
hasCachedData,
|
||||
loadMeta,
|
||||
loadSentences,
|
||||
loadSnippets,
|
||||
lookupVideoDir,
|
||||
registerVideoDir,
|
||||
resolveBaseDir,
|
||||
} from "./storage.ts";
|
||||
import { findTranscript, formatListOutput, formatMarkdown, formatSrt, segmentIntoSentences } from "./transcript.ts";
|
||||
import type { Options, Sentence, Snippet, VideoMeta, VideoResult } from "./types.ts";
|
||||
import {
|
||||
buildVideoMeta,
|
||||
buildVideoMetaFromYtDlp,
|
||||
downloadCoverImage,
|
||||
fetchTranscriptSnippets,
|
||||
fetchVideoSource,
|
||||
getThumbnailUrls,
|
||||
getYtDlpThumbnailUrls,
|
||||
parseChapters,
|
||||
} from "./youtube.ts";
|
||||
|
||||
async function fetchAndCache(
|
||||
videoId: string,
|
||||
baseDir: string,
|
||||
opts: Options
|
||||
): Promise<{ meta: VideoMeta; snippets: Snippet[]; sentences: Sentence[]; videoDir: string }> {
|
||||
const source = await fetchVideoSource(videoId);
|
||||
const requestedLanguages = source.kind === "yt-dlp" && opts.translate ? [opts.translate] : opts.languages;
|
||||
const transcript = findTranscript(source.transcripts, requestedLanguages, opts.excludeGenerated, opts.excludeManual);
|
||||
const result = await fetchTranscriptSnippets(transcript, source.kind === "yt-dlp" ? undefined : opts.translate || undefined);
|
||||
const description = source.kind === "yt-dlp"
|
||||
? source.info.description || ""
|
||||
: source.data?.videoDetails?.shortDescription || "";
|
||||
const duration = source.kind === "yt-dlp"
|
||||
? Number(source.info.duration || 0)
|
||||
: parseInt(source.data?.videoDetails?.lengthSeconds || "0");
|
||||
const chapters = parseChapters(description, duration);
|
||||
const language = {
|
||||
code: result.languageCode,
|
||||
name: result.language,
|
||||
isGenerated: transcript.isGenerated,
|
||||
};
|
||||
const meta = source.kind === "yt-dlp"
|
||||
? buildVideoMetaFromYtDlp(source.info, videoId, language, chapters)
|
||||
: buildVideoMeta(source.data, videoId, language, chapters);
|
||||
|
||||
const videoDir = registerVideoDir(videoId, slugify(meta.channel), slugify(meta.title), baseDir);
|
||||
ensureDir(join(videoDir, "meta.json"));
|
||||
|
||||
writeFileSync(join(videoDir, "transcript-raw.json"), JSON.stringify(result.snippets, null, 2));
|
||||
|
||||
const sentences = segmentIntoSentences(result.snippets);
|
||||
writeFileSync(join(videoDir, "transcript-sentences.json"), JSON.stringify(sentences, null, 2));
|
||||
|
||||
const imagePath = join(videoDir, "imgs", "cover.jpg");
|
||||
ensureDir(imagePath);
|
||||
const downloaded = await downloadCoverImage(
|
||||
source.kind === "yt-dlp" ? getYtDlpThumbnailUrls(videoId, source.info) : getThumbnailUrls(videoId, source.data),
|
||||
imagePath
|
||||
);
|
||||
meta.coverImage = downloaded ? "imgs/cover.jpg" : "";
|
||||
|
||||
writeFileSync(join(videoDir, "meta.json"), JSON.stringify(meta, null, 2));
|
||||
|
||||
return { meta, snippets: result.snippets, sentences, videoDir };
|
||||
}
|
||||
|
||||
async function processVideo(videoId: string, opts: Options): Promise<VideoResult> {
|
||||
const baseDir = resolveBaseDir(opts.outputDir);
|
||||
|
||||
if (opts.list) {
|
||||
const source = await fetchVideoSource(videoId);
|
||||
const title = source.kind === "yt-dlp" ? source.info.title || "" : source.data?.videoDetails?.title || "";
|
||||
return { videoId, title, content: formatListOutput(videoId, title, source.transcripts) };
|
||||
}
|
||||
|
||||
let videoDir = lookupVideoDir(videoId, baseDir);
|
||||
let meta: VideoMeta;
|
||||
let snippets: Snippet[];
|
||||
let sentences: Sentence[];
|
||||
let needsFetch = opts.refresh || !videoDir || !hasCachedData(videoDir);
|
||||
|
||||
if (!needsFetch && videoDir) {
|
||||
meta = loadMeta(videoDir);
|
||||
snippets = loadSnippets(videoDir);
|
||||
sentences = loadSentences(videoDir);
|
||||
const wantedLanguages = opts.translate ? [opts.translate] : opts.languages;
|
||||
if (!wantedLanguages.includes(meta.language.code)) needsFetch = true;
|
||||
if (!needsFetch && meta.chapters.length > 0 && meta.chapters.some((chapter: any) => chapter.end === undefined)) {
|
||||
for (let i = 0; i < meta.chapters.length; i++) {
|
||||
meta.chapters[i].end = i < meta.chapters.length - 1
|
||||
? meta.chapters[i + 1].start
|
||||
: Math.max(meta.duration, meta.chapters[i].start);
|
||||
}
|
||||
try {
|
||||
writeFileSync(join(videoDir, "meta.json"), JSON.stringify(meta, null, 2));
|
||||
} catch {}
|
||||
}
|
||||
}
|
||||
|
||||
if (needsFetch) {
|
||||
const result = await fetchAndCache(videoId, baseDir, opts);
|
||||
meta = result.meta;
|
||||
snippets = result.snippets;
|
||||
sentences = result.sentences;
|
||||
videoDir = result.videoDir;
|
||||
} else {
|
||||
meta = meta!;
|
||||
snippets = snippets!;
|
||||
sentences = sentences!;
|
||||
}
|
||||
|
||||
const content = opts.format === "srt"
|
||||
? formatSrt(snippets)
|
||||
: formatMarkdown(
|
||||
sentences,
|
||||
meta,
|
||||
{
|
||||
timestamps: opts.timestamps,
|
||||
chapters: opts.chapters,
|
||||
speakers: opts.speakers,
|
||||
},
|
||||
snippets
|
||||
);
|
||||
const ext = opts.format === "srt" ? "srt" : "md";
|
||||
const filePath = opts.output ? resolve(opts.output) : join(videoDir!, `transcript.${ext}`);
|
||||
ensureDir(filePath);
|
||||
writeFileSync(filePath, content);
|
||||
|
||||
return { videoId, title: meta.title, filePath };
|
||||
}
|
||||
|
||||
function printHelp() {
|
||||
console.log(`Usage: bun main.ts <video-url-or-id> [options]
|
||||
|
||||
Options:
|
||||
--languages <codes> Language codes, comma-separated (default: en)
|
||||
--format <fmt> Output format: text, srt (default: text)
|
||||
--translate <code> Translate to language code
|
||||
--list List available transcripts
|
||||
--timestamps Include timestamps (default: on)
|
||||
--no-timestamps Disable timestamps
|
||||
--chapters Chapter segmentation from description
|
||||
--speakers Raw transcript with metadata for speaker identification
|
||||
--exclude-generated Skip auto-generated transcripts
|
||||
--exclude-manually-created Skip manually created transcripts
|
||||
--refresh Force re-fetch (ignore cache)
|
||||
-o, --output <path> Save to specific file path
|
||||
--output-dir <dir> Base output directory (default: youtube-transcript)
|
||||
-h, --help Show help`);
|
||||
}
|
||||
|
||||
function parseArgs(argv: string[]): Options | null {
|
||||
const opts: Options = {
|
||||
videoIds: [],
|
||||
languages: ["en"],
|
||||
format: "text",
|
||||
translate: "",
|
||||
list: false,
|
||||
excludeGenerated: false,
|
||||
excludeManual: false,
|
||||
output: "",
|
||||
outputDir: "",
|
||||
timestamps: true,
|
||||
chapters: false,
|
||||
speakers: false,
|
||||
refresh: false,
|
||||
};
|
||||
|
||||
for (let i = 0; i < argv.length; i++) {
|
||||
const arg = argv[i];
|
||||
if (arg === "-h" || arg === "--help") {
|
||||
printHelp();
|
||||
process.exit(0);
|
||||
} else if (arg === "--languages") {
|
||||
const value = argv[++i];
|
||||
if (value) opts.languages = value.split(",").map((entry) => entry.trim());
|
||||
} else if (arg === "--format") {
|
||||
const value = argv[++i]?.toLowerCase();
|
||||
if (value === "text" || value === "srt") opts.format = value;
|
||||
else {
|
||||
console.error(`Invalid format: ${value}. Use: text, srt`);
|
||||
return null;
|
||||
}
|
||||
} else if (arg === "--translate") {
|
||||
opts.translate = argv[++i] || "";
|
||||
} else if (arg === "--list" || arg === "--list-transcripts") {
|
||||
opts.list = true;
|
||||
} else if (arg === "--timestamps" || arg === "-t") {
|
||||
opts.timestamps = true;
|
||||
} else if (arg === "--no-timestamps") {
|
||||
opts.timestamps = false;
|
||||
} else if (arg === "--chapters") {
|
||||
opts.chapters = true;
|
||||
} else if (arg === "--speakers") {
|
||||
opts.speakers = true;
|
||||
} else if (arg === "--exclude-generated") {
|
||||
opts.excludeGenerated = true;
|
||||
} else if (arg === "--exclude-manually-created") {
|
||||
opts.excludeManual = true;
|
||||
} else if (arg === "--refresh") {
|
||||
opts.refresh = true;
|
||||
} else if (arg === "-o" || arg === "--output") {
|
||||
opts.output = argv[++i] || "";
|
||||
} else if (arg === "--output-dir") {
|
||||
opts.outputDir = argv[++i] || "";
|
||||
} else if (!arg.startsWith("-")) {
|
||||
opts.videoIds.push(extractVideoId(arg));
|
||||
}
|
||||
}
|
||||
|
||||
if (opts.videoIds.length === 0) {
|
||||
console.error("Error: At least one video URL or ID required");
|
||||
printHelp();
|
||||
return null;
|
||||
}
|
||||
|
||||
return opts;
|
||||
}
|
||||
|
||||
async function main() {
|
||||
const opts = parseArgs(process.argv.slice(2));
|
||||
if (!opts) process.exit(1);
|
||||
|
||||
if (opts.excludeGenerated && opts.excludeManual) {
|
||||
console.error("Error: Cannot exclude both generated and manually created transcripts");
|
||||
process.exit(1);
|
||||
}
|
||||
|
||||
for (const videoId of opts.videoIds) {
|
||||
try {
|
||||
const result = await processVideo(videoId, opts);
|
||||
if (result.error) console.error(`Error (${result.videoId}): ${result.error}`);
|
||||
else if (result.filePath) console.log(result.filePath);
|
||||
else if (result.content) console.log(result.content);
|
||||
} catch (error) {
|
||||
console.error(`Error (${videoId}): ${(error as Error).message}`);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (import.meta.main) {
|
||||
main();
|
||||
}
|
||||
@@ -0,0 +1,83 @@
|
||||
import type { TranscriptError } from "./types.ts";
|
||||
|
||||
export function extractVideoId(input: string): string {
|
||||
input = input.replace(/\\/g, "").trim();
|
||||
const patterns = [
|
||||
/(?:youtube\.com\/watch\?.*v=|youtu\.be\/|youtube\.com\/embed\/|youtube\.com\/v\/|youtube\.com\/shorts\/)([a-zA-Z0-9_-]{11})/,
|
||||
/^([a-zA-Z0-9_-]{11})$/,
|
||||
];
|
||||
for (const pattern of patterns) {
|
||||
const match = input.match(pattern);
|
||||
if (match) return match[1];
|
||||
}
|
||||
return input;
|
||||
}
|
||||
|
||||
export function slugify(value: string): string {
|
||||
return value
|
||||
.toLowerCase()
|
||||
.replace(/[^\w\s-]/g, "")
|
||||
.replace(/\s+/g, "-")
|
||||
.replace(/-+/g, "-")
|
||||
.replace(/^-|-$/g, "") || "untitled";
|
||||
}
|
||||
|
||||
export function htmlUnescape(value: string): string {
|
||||
return value
|
||||
.replace(/&/g, "&")
|
||||
.replace(/</g, "<")
|
||||
.replace(/>/g, ">")
|
||||
.replace(/"/g, '"')
|
||||
.replace(/'/g, "'")
|
||||
.replace(/'/g, "'")
|
||||
.replace(///g, "/")
|
||||
.replace(/'/g, "'")
|
||||
.replace(/&#(\d+);/g, (_, n) => String.fromCharCode(parseInt(n)))
|
||||
.replace(/&#x([0-9a-fA-F]+);/g, (_, n) => String.fromCharCode(parseInt(n, 16)));
|
||||
}
|
||||
|
||||
export function stripTags(value: string): string {
|
||||
return value.replace(/<[^>]*>/g, "");
|
||||
}
|
||||
|
||||
export function makeError(message: string, code?: string): Error {
|
||||
const error = new Error(message) as TranscriptError;
|
||||
if (code) error.code = code;
|
||||
return error;
|
||||
}
|
||||
|
||||
export function normalizeError(error: unknown): TranscriptError {
|
||||
if (error instanceof Error) {
|
||||
const known = error as TranscriptError;
|
||||
if (known.code) return known;
|
||||
const message = known.message || String(error);
|
||||
const lower = message.toLowerCase();
|
||||
if (lower.includes("bot detected")) known.code = "BOT_DETECTED";
|
||||
else if (lower.includes("age restricted")) known.code = "AGE_RESTRICTED";
|
||||
else if (lower.includes("video unavailable")) known.code = "VIDEO_UNAVAILABLE";
|
||||
else if (lower.includes("transcripts disabled")) known.code = "TRANSCRIPTS_DISABLED";
|
||||
else if (lower.includes("no transcript found")) known.code = "NO_TRANSCRIPT";
|
||||
else if (lower.includes("invalid video id")) known.code = "INVALID_VIDEO_ID";
|
||||
else if (lower.includes("ip blocked") || lower.includes("recaptcha") || lower.includes("http 429")) known.code = "IP_BLOCKED";
|
||||
else if (lower.includes("cannot extract api key")) known.code = "PAGE_FETCH_FAILED";
|
||||
else if (lower.includes("innertube api") || lower.includes("http 403")) known.code = "INNERTUBE_REJECTED";
|
||||
else if (lower.includes("yt-dlp fallback failed")) known.code = "YT_DLP_FAILED";
|
||||
return known;
|
||||
}
|
||||
return makeError(String(error), "UNKNOWN") as TranscriptError;
|
||||
}
|
||||
|
||||
export function shouldTryAlternateClient(error: unknown): boolean {
|
||||
const code = normalizeError(error).code;
|
||||
return code === "BOT_DETECTED" || code === "IP_BLOCKED" || code === "INNERTUBE_REJECTED" || code === "AGE_RESTRICTED" || code === "VIDEO_UNAVAILABLE";
|
||||
}
|
||||
|
||||
export function shouldTryYtDlpFallback(error: unknown): boolean {
|
||||
const code = normalizeError(error).code;
|
||||
return code === "BOT_DETECTED" || code === "IP_BLOCKED" || code === "INNERTUBE_REJECTED" || code === "PAGE_FETCH_FAILED" || code === "AGE_RESTRICTED" || code === "VIDEO_UNAVAILABLE";
|
||||
}
|
||||
|
||||
export function normalizePublishDate(uploadDate?: string): string {
|
||||
if (!uploadDate || !/^\d{8}$/.test(uploadDate)) return uploadDate || "";
|
||||
return `${uploadDate.slice(0, 4)}-${uploadDate.slice(4, 6)}-${uploadDate.slice(6, 8)}`;
|
||||
}
|
||||
@@ -0,0 +1,60 @@
|
||||
import { existsSync, mkdirSync, readFileSync, writeFileSync } from "fs";
|
||||
import { dirname, join, resolve } from "path";
|
||||
|
||||
import type { Sentence, Snippet, VideoMeta } from "./types.ts";
|
||||
|
||||
export function ensureDir(path: string) {
|
||||
const dir = dirname(path);
|
||||
if (!existsSync(dir)) mkdirSync(dir, { recursive: true });
|
||||
}
|
||||
|
||||
export function resolveBaseDir(outputDir: string): string {
|
||||
return resolve(outputDir || "youtube-transcript");
|
||||
}
|
||||
|
||||
function loadIndex(baseDir: string): Record<string, string> {
|
||||
try {
|
||||
return JSON.parse(readFileSync(join(baseDir, ".index.json"), "utf-8"));
|
||||
} catch {
|
||||
return {};
|
||||
}
|
||||
}
|
||||
|
||||
function saveIndex(baseDir: string, index: Record<string, string>) {
|
||||
const path = join(baseDir, ".index.json");
|
||||
ensureDir(path);
|
||||
writeFileSync(path, JSON.stringify(index, null, 2));
|
||||
}
|
||||
|
||||
export function lookupVideoDir(videoId: string, baseDir: string): string | null {
|
||||
const rel = loadIndex(baseDir)[videoId];
|
||||
if (rel) {
|
||||
const dir = resolve(baseDir, rel);
|
||||
if (existsSync(dir)) return dir;
|
||||
}
|
||||
return null;
|
||||
}
|
||||
|
||||
export function registerVideoDir(videoId: string, channelSlug: string, titleSlug: string, baseDir: string): string {
|
||||
const rel = join(channelSlug, titleSlug);
|
||||
const index = loadIndex(baseDir);
|
||||
index[videoId] = rel;
|
||||
saveIndex(baseDir, index);
|
||||
return resolve(baseDir, rel);
|
||||
}
|
||||
|
||||
export function hasCachedData(videoDir: string): boolean {
|
||||
return existsSync(join(videoDir, "meta.json")) && existsSync(join(videoDir, "transcript-raw.json"));
|
||||
}
|
||||
|
||||
export function loadMeta(videoDir: string): VideoMeta {
|
||||
return JSON.parse(readFileSync(join(videoDir, "meta.json"), "utf-8"));
|
||||
}
|
||||
|
||||
export function loadSnippets(videoDir: string): Snippet[] {
|
||||
return JSON.parse(readFileSync(join(videoDir, "transcript-raw.json"), "utf-8"));
|
||||
}
|
||||
|
||||
export function loadSentences(videoDir: string): Sentence[] {
|
||||
return JSON.parse(readFileSync(join(videoDir, "transcript-sentences.json"), "utf-8"));
|
||||
}
|
||||
@@ -0,0 +1,349 @@
|
||||
import { htmlUnescape, makeError, stripTags } from "./shared.ts";
|
||||
import type { Sentence, Snippet, TranscriptInfo, VideoMeta } from "./types.ts";
|
||||
|
||||
interface Paragraph {
|
||||
text: string;
|
||||
start: number;
|
||||
end: number;
|
||||
}
|
||||
|
||||
const SENTENCE_END_RE = /[.?!…。?!⁈⁇‼‽.]/;
|
||||
|
||||
export function parseTranscriptXml(xml: string): Snippet[] {
|
||||
const snippets: Snippet[] = [];
|
||||
const pattern = /<text\s+start="([^"]*)"(?:\s+dur="([^"]*)")?[^>]*>([\s\S]*?)<\/text>/g;
|
||||
let match: RegExpExecArray | null;
|
||||
while ((match = pattern.exec(xml)) !== null) {
|
||||
const raw = match[3];
|
||||
if (!raw) continue;
|
||||
snippets.push({
|
||||
text: htmlUnescape(stripTags(raw)),
|
||||
start: parseFloat(match[1]),
|
||||
duration: parseFloat(match[2] || "0"),
|
||||
});
|
||||
}
|
||||
return snippets;
|
||||
}
|
||||
|
||||
export function parseTranscriptJson3(text: string): Snippet[] {
|
||||
const data = JSON.parse(text);
|
||||
const events = Array.isArray(data?.events) ? data.events : [];
|
||||
const snippets: Snippet[] = [];
|
||||
for (const event of events) {
|
||||
const segs = Array.isArray(event?.segs) ? event.segs : [];
|
||||
const textParts = segs
|
||||
.map((seg: any) => htmlUnescape(String(seg?.utf8 || "").replace(/\n+/g, " ").trim()))
|
||||
.filter(Boolean);
|
||||
const merged = mergeTexts(textParts).trim();
|
||||
if (!merged) continue;
|
||||
snippets.push({
|
||||
text: merged,
|
||||
start: Number(event?.tStartMs || 0) / 1000,
|
||||
duration: Number(event?.dDurationMs || 0) / 1000,
|
||||
});
|
||||
}
|
||||
return snippets;
|
||||
}
|
||||
|
||||
function parseSrt(srt: string): Snippet[] {
|
||||
const blocks = srt.trim().split(/\n\n+/);
|
||||
const snippets: Snippet[] = [];
|
||||
for (const block of blocks) {
|
||||
const lines = block.split("\n");
|
||||
if (lines.length < 3) continue;
|
||||
const match = lines[1].match(/(\d{2}):(\d{2}):(\d{2}),(\d{3})\s*-->\s*(\d{2}):(\d{2}):(\d{2}),(\d{3})/);
|
||||
if (!match) continue;
|
||||
const start = parseInt(match[1]) * 3600 + parseInt(match[2]) * 60 + parseInt(match[3]) + parseInt(match[4]) / 1000;
|
||||
const end = parseInt(match[5]) * 3600 + parseInt(match[6]) * 60 + parseInt(match[7]) + parseInt(match[8]) / 1000;
|
||||
snippets.push({ text: lines.slice(2).join(" "), start, duration: end - start });
|
||||
}
|
||||
return snippets;
|
||||
}
|
||||
|
||||
export function parseWebVtt(vtt: string): Snippet[] {
|
||||
const blocks = vtt
|
||||
.replace(/^WEBVTT\s*/m, "")
|
||||
.trim()
|
||||
.split(/\n\n+/);
|
||||
const snippets: Snippet[] = [];
|
||||
for (const block of blocks) {
|
||||
const lines = block.split("\n").map((line) => line.trim()).filter(Boolean);
|
||||
const tsLine = lines.find((line) => line.includes("-->"));
|
||||
if (!tsLine) continue;
|
||||
const match = tsLine.match(
|
||||
/(?:(\d{2}):)?(\d{2}):(\d{2})\.(\d{3})\s*-->\s*(?:(\d{2}):)?(\d{2}):(\d{2})\.(\d{3})/
|
||||
);
|
||||
if (!match) continue;
|
||||
const start =
|
||||
(match[1] ? parseInt(match[1]) : 0) * 3600 +
|
||||
parseInt(match[2]) * 60 +
|
||||
parseInt(match[3]) +
|
||||
parseInt(match[4]) / 1000;
|
||||
const end =
|
||||
(match[5] ? parseInt(match[5]) : 0) * 3600 +
|
||||
parseInt(match[6]) * 60 +
|
||||
parseInt(match[7]) +
|
||||
parseInt(match[8]) / 1000;
|
||||
const text = htmlUnescape(stripTags(lines.slice(lines.indexOf(tsLine) + 1).join(" ").replace(/\s+/g, " ").trim()));
|
||||
if (!text) continue;
|
||||
snippets.push({ text, start, duration: end - start });
|
||||
}
|
||||
return snippets;
|
||||
}
|
||||
|
||||
export function parseTranscriptPayload(payload: string, url: string): Snippet[] {
|
||||
const normalized = payload.trimStart();
|
||||
if (url.includes("fmt=json3") || normalized.startsWith("{")) return parseTranscriptJson3(payload);
|
||||
if (normalized.startsWith("WEBVTT")) return parseWebVtt(payload);
|
||||
if (/^\d+\s*\n\d{2}:\d{2}:\d{2},\d{3}\s*-->/.test(normalized)) return parseSrt(payload);
|
||||
return parseTranscriptXml(payload);
|
||||
}
|
||||
|
||||
function isCJK(ch: string): boolean {
|
||||
const code = ch.charCodeAt(0);
|
||||
return (code >= 0x4E00 && code <= 0x9FFF) ||
|
||||
(code >= 0x3040 && code <= 0x309F) ||
|
||||
(code >= 0x30A0 && code <= 0x30FF) ||
|
||||
(code >= 0xAC00 && code <= 0xD7AF) ||
|
||||
(code >= 0x3400 && code <= 0x4DBF) ||
|
||||
(code >= 0xF900 && code <= 0xFAFF);
|
||||
}
|
||||
|
||||
function splitSnippetAtPunctuation(snippet: Snippet): { text: string; start: number; end: number }[] {
|
||||
const { text, start, duration } = snippet;
|
||||
const end = start + duration;
|
||||
if (!text.length) return [];
|
||||
|
||||
const splitPoints: number[] = [];
|
||||
for (let i = 0; i < text.length; i++) {
|
||||
if (SENTENCE_END_RE.test(text[i])) {
|
||||
while (i + 1 < text.length && SENTENCE_END_RE.test(text[i + 1])) i++;
|
||||
if (i < text.length - 1) splitPoints.push(i);
|
||||
}
|
||||
}
|
||||
|
||||
if (!splitPoints.length) return [{ text, start, end }];
|
||||
|
||||
const parts: { text: string; start: number; end: number }[] = [];
|
||||
let prev = 0;
|
||||
for (const pos of splitPoints) {
|
||||
const partText = text.slice(prev, pos + 1).trim();
|
||||
if (partText) {
|
||||
parts.push({
|
||||
text: partText,
|
||||
start: start + (prev / text.length) * duration,
|
||||
end: start + ((pos + 1) / text.length) * duration,
|
||||
});
|
||||
}
|
||||
prev = pos + 1;
|
||||
}
|
||||
|
||||
const remaining = text.slice(prev).trim();
|
||||
if (remaining) parts.push({ text: remaining, start: start + (prev / text.length) * duration, end });
|
||||
|
||||
return parts;
|
||||
}
|
||||
|
||||
function mergeTexts(texts: string[]): string {
|
||||
if (!texts.length) return "";
|
||||
let result = texts[0];
|
||||
for (let i = 1; i < texts.length; i++) {
|
||||
const next = texts[i];
|
||||
if (!next) continue;
|
||||
const lastChar = result[result.length - 1];
|
||||
const firstChar = next[0];
|
||||
if (isCJK(lastChar) || isCJK(firstChar)) {
|
||||
result += next;
|
||||
} else {
|
||||
result = result.trimEnd() + " " + next.trimStart();
|
||||
}
|
||||
}
|
||||
return result.replace(/ {2,}/g, " ");
|
||||
}
|
||||
|
||||
export function ts(time: number): string {
|
||||
const h = Math.floor(time / 3600);
|
||||
const m = Math.floor((time % 3600) / 60);
|
||||
const s = Math.floor(time % 60);
|
||||
return `${String(h).padStart(2, "0")}:${String(m).padStart(2, "0")}:${String(s).padStart(2, "0")}`;
|
||||
}
|
||||
|
||||
function tsMs(time: number, sep: string): string {
|
||||
const h = Math.floor(time / 3600);
|
||||
const m = Math.floor((time % 3600) / 60);
|
||||
const s = Math.floor(time % 60);
|
||||
const ms = Math.round((time - Math.floor(time)) * 1000);
|
||||
return `${String(h).padStart(2, "0")}:${String(m).padStart(2, "0")}:${String(s).padStart(2, "0")}${sep}${String(ms).padStart(3, "0")}`;
|
||||
}
|
||||
|
||||
function parseTs(time: string): number {
|
||||
const [h, m, s] = time.split(":").map(Number);
|
||||
return h * 3600 + m * 60 + s;
|
||||
}
|
||||
|
||||
export function segmentIntoSentences(snippets: Snippet[]): Sentence[] {
|
||||
const parts: { text: string; start: number; end: number }[] = [];
|
||||
for (const snippet of snippets) parts.push(...splitSnippetAtPunctuation(snippet));
|
||||
|
||||
const sentences: Sentence[] = [];
|
||||
let buffer: { text: string; start: number; end: number }[] = [];
|
||||
|
||||
for (const part of parts) {
|
||||
buffer.push(part);
|
||||
if (SENTENCE_END_RE.test(part.text[part.text.length - 1])) {
|
||||
sentences.push({
|
||||
text: mergeTexts(buffer.map((entry) => entry.text)),
|
||||
start: ts(buffer[0].start),
|
||||
end: ts(buffer[buffer.length - 1].end),
|
||||
});
|
||||
buffer = [];
|
||||
}
|
||||
}
|
||||
|
||||
if (buffer.length) {
|
||||
sentences.push({
|
||||
text: mergeTexts(buffer.map((entry) => entry.text)),
|
||||
start: ts(buffer[0].start),
|
||||
end: ts(buffer[buffer.length - 1].end),
|
||||
});
|
||||
}
|
||||
|
||||
return sentences;
|
||||
}
|
||||
|
||||
function groupSentenceParas(sentences: Sentence[]): Paragraph[] {
|
||||
if (!sentences.length) return [];
|
||||
const paragraphs: Paragraph[] = [];
|
||||
let buffer: Sentence[] = [];
|
||||
for (let i = 0; i < sentences.length; i++) {
|
||||
buffer.push(sentences[i]);
|
||||
const last = i === sentences.length - 1;
|
||||
const gap = !last && parseTs(sentences[i + 1].start) - parseTs(sentences[i].end) > 2;
|
||||
if (last || gap || buffer.length >= 5) {
|
||||
paragraphs.push({
|
||||
text: mergeTexts(buffer.map((sentence) => sentence.text)),
|
||||
start: parseTs(buffer[0].start),
|
||||
end: parseTs(buffer[buffer.length - 1].end),
|
||||
});
|
||||
buffer = [];
|
||||
}
|
||||
}
|
||||
return paragraphs;
|
||||
}
|
||||
|
||||
export function formatSrt(snippets: Snippet[]): string {
|
||||
return snippets
|
||||
.map((snippet, index) => {
|
||||
const end = index < snippets.length - 1 && snippets[index + 1].start < snippet.start + snippet.duration
|
||||
? snippets[index + 1].start
|
||||
: snippet.start + snippet.duration;
|
||||
return `${index + 1}\n${tsMs(snippet.start, ",")} --> ${tsMs(end, ",")}\n${snippet.text}`;
|
||||
})
|
||||
.join("\n\n") + "\n";
|
||||
}
|
||||
|
||||
function yamlEscape(value: string): string {
|
||||
if (/[:"'{}\[\]#&*!|>%@`\n]/.test(value) || value.trim() !== value) {
|
||||
return `"${value.replace(/\\/g, "\\\\").replace(/"/g, '\\"')}"`;
|
||||
}
|
||||
return value;
|
||||
}
|
||||
|
||||
function extractSummary(description: string): string {
|
||||
if (!description) return "";
|
||||
const firstPara = description.split(/\n\s*\n/)[0].trim();
|
||||
const lines = firstPara.split("\n").filter((line) => !/^\s*(https?:\/\/|#|@|\d+:\d+)/.test(line) && line.trim());
|
||||
return lines.join(" ").slice(0, 300).trim();
|
||||
}
|
||||
|
||||
export function formatMarkdown(
|
||||
sentences: Sentence[],
|
||||
meta: VideoMeta,
|
||||
opts: { timestamps: boolean; chapters: boolean; speakers: boolean },
|
||||
snippets?: Snippet[]
|
||||
): string {
|
||||
const summary = extractSummary(meta.description);
|
||||
let md = "---\n";
|
||||
md += `title: ${yamlEscape(meta.title)}\n`;
|
||||
md += `channel: ${yamlEscape(meta.channel)}\n`;
|
||||
if (meta.publishDate) md += `date: ${meta.publishDate}\n`;
|
||||
md += `url: ${yamlEscape(meta.url)}\n`;
|
||||
if (meta.coverImage) md += `cover: ${meta.coverImage}\n`;
|
||||
if (summary) md += `description: ${yamlEscape(summary)}\n`;
|
||||
if (meta.language) md += `language: ${meta.language.code}\n`;
|
||||
md += "---\n\n";
|
||||
|
||||
if (opts.speakers) {
|
||||
md += `# ${meta.title}\n\n`;
|
||||
if (summary) md += `${summary}\n\n`;
|
||||
if (meta.description) md += `# Description\n\n${meta.description.trim()}\n\n`;
|
||||
if (meta.chapters.length) {
|
||||
md += "# Chapters\n\n";
|
||||
for (const chapter of meta.chapters) md += `* [${ts(chapter.start)}] ${chapter.title}\n`;
|
||||
md += "\n";
|
||||
}
|
||||
md += "# Transcript\n\n";
|
||||
md += snippets ? formatSrt(snippets) : "";
|
||||
return md;
|
||||
}
|
||||
|
||||
md += `# ${meta.title}\n\n`;
|
||||
if (summary) md += `${summary}\n\n`;
|
||||
|
||||
const chapters = opts.chapters ? meta.chapters : [];
|
||||
if (chapters.length) {
|
||||
md += "## Table of Contents\n\n";
|
||||
for (const chapter of chapters) md += opts.timestamps ? `* [${ts(chapter.start)}] ${chapter.title}\n` : `* ${chapter.title}\n`;
|
||||
md += "\n";
|
||||
if (meta.coverImage) md += `\n\n`;
|
||||
md += "\n";
|
||||
for (let i = 0; i < chapters.length; i++) {
|
||||
const nextStart = i < chapters.length - 1 ? chapters[i + 1].start : Infinity;
|
||||
const chapterSentences = sentences.filter((sentence) => parseTs(sentence.start) >= chapters[i].start && parseTs(sentence.start) < nextStart);
|
||||
const paragraphs = groupSentenceParas(chapterSentences);
|
||||
md += opts.timestamps ? `## [${ts(chapters[i].start)}] ${chapters[i].title}\n\n` : `## ${chapters[i].title}\n\n`;
|
||||
for (const paragraph of paragraphs) {
|
||||
md += opts.timestamps ? `${paragraph.text} [${ts(paragraph.start)} → ${ts(paragraph.end)}]\n\n` : `${paragraph.text}\n\n`;
|
||||
}
|
||||
md += "\n";
|
||||
}
|
||||
} else {
|
||||
const paragraphs = groupSentenceParas(sentences);
|
||||
for (const paragraph of paragraphs) {
|
||||
md += opts.timestamps ? `${paragraph.text} [${ts(paragraph.start)} → ${ts(paragraph.end)}]\n\n` : `${paragraph.text}\n\n`;
|
||||
}
|
||||
}
|
||||
|
||||
return md.trimEnd() + "\n";
|
||||
}
|
||||
|
||||
export function formatListOutput(videoId: string, title: string, transcripts: TranscriptInfo[]): string {
|
||||
const manual = transcripts.filter((transcript) => !transcript.isGenerated);
|
||||
const generated = transcripts.filter((transcript) => transcript.isGenerated);
|
||||
const translationLanguages = transcripts.find((transcript) => transcript.translationLanguages.length > 0)?.translationLanguages || [];
|
||||
const formatList = (list: TranscriptInfo[]) =>
|
||||
list.length
|
||||
? list.map((transcript) => ` - ${transcript.languageCode} ("${transcript.language}")${transcript.isTranslatable ? " [TRANSLATABLE]" : ""}`).join("\n")
|
||||
: "None";
|
||||
const formatTranslations = translationLanguages.length
|
||||
? translationLanguages.map((language) => ` - ${language.languageCode} ("${language.language}")`).join("\n")
|
||||
: "None";
|
||||
return `Transcripts for ${videoId}${title ? ` (${title})` : ""}:\n\n(MANUALLY CREATED)\n${formatList(manual)}\n\n(GENERATED)\n${formatList(generated)}\n\n(TRANSLATION LANGUAGES)\n${formatTranslations}`;
|
||||
}
|
||||
|
||||
export function findTranscript(
|
||||
transcripts: TranscriptInfo[],
|
||||
languages: string[],
|
||||
excludeGenerated: boolean,
|
||||
excludeManual: boolean
|
||||
): TranscriptInfo {
|
||||
let filtered = transcripts;
|
||||
if (excludeGenerated) filtered = filtered.filter((transcript) => !transcript.isGenerated);
|
||||
if (excludeManual) filtered = filtered.filter((transcript) => transcript.isGenerated);
|
||||
for (const language of languages) {
|
||||
const found = filtered.find((transcript) => transcript.languageCode === language);
|
||||
if (found) return found;
|
||||
}
|
||||
const available = filtered.map((transcript) => `${transcript.languageCode} ("${transcript.language}")`).join(", ");
|
||||
throw makeError(`No transcript found for languages [${languages.join(", ")}]. Available: ${available || "none"}`, "NO_TRANSCRIPT");
|
||||
}
|
||||
@@ -0,0 +1,123 @@
|
||||
export type Format = "text" | "srt";
|
||||
|
||||
export interface Options {
|
||||
videoIds: string[];
|
||||
languages: string[];
|
||||
format: Format;
|
||||
translate: string;
|
||||
list: boolean;
|
||||
excludeGenerated: boolean;
|
||||
excludeManual: boolean;
|
||||
output: string;
|
||||
outputDir: string;
|
||||
timestamps: boolean;
|
||||
chapters: boolean;
|
||||
speakers: boolean;
|
||||
refresh: boolean;
|
||||
}
|
||||
|
||||
export interface Snippet {
|
||||
text: string;
|
||||
start: number;
|
||||
duration: number;
|
||||
}
|
||||
|
||||
export interface Sentence {
|
||||
text: string;
|
||||
start: string;
|
||||
end: string;
|
||||
}
|
||||
|
||||
export interface TranscriptLanguage {
|
||||
language: string;
|
||||
languageCode: string;
|
||||
}
|
||||
|
||||
export interface TranscriptInfo {
|
||||
language: string;
|
||||
languageCode: string;
|
||||
isGenerated: boolean;
|
||||
isTranslatable: boolean;
|
||||
baseUrl: string;
|
||||
translationLanguages: TranscriptLanguage[];
|
||||
}
|
||||
|
||||
export interface Chapter {
|
||||
title: string;
|
||||
start: number;
|
||||
end: number;
|
||||
}
|
||||
|
||||
export interface LanguageMeta {
|
||||
code: string;
|
||||
name: string;
|
||||
isGenerated: boolean;
|
||||
}
|
||||
|
||||
export interface VideoMeta {
|
||||
videoId: string;
|
||||
title: string;
|
||||
channel: string;
|
||||
channelId: string;
|
||||
description: string;
|
||||
duration: number;
|
||||
publishDate: string;
|
||||
url: string;
|
||||
coverImage: string;
|
||||
thumbnailUrl: string;
|
||||
language: LanguageMeta;
|
||||
chapters: Chapter[];
|
||||
}
|
||||
|
||||
export interface VideoResult {
|
||||
videoId: string;
|
||||
title?: string;
|
||||
filePath?: string;
|
||||
content?: string;
|
||||
error?: string;
|
||||
}
|
||||
|
||||
export interface InnerTubeSession {
|
||||
apiKey: string;
|
||||
webClientVersion: string;
|
||||
visitorData: string;
|
||||
}
|
||||
|
||||
export interface InnerTubeClient {
|
||||
id: string;
|
||||
clientName: string;
|
||||
clientVersion?: string;
|
||||
clientHeaderName?: string;
|
||||
userAgent: string;
|
||||
extraContext?: Record<string, any>;
|
||||
}
|
||||
|
||||
export interface TranscriptError extends Error {
|
||||
code?: string;
|
||||
}
|
||||
|
||||
export interface YtDlpTrack {
|
||||
ext?: string;
|
||||
url?: string;
|
||||
name?: string;
|
||||
}
|
||||
|
||||
export interface YtDlpInfo {
|
||||
title?: string;
|
||||
channel?: string;
|
||||
channel_id?: string;
|
||||
uploader?: string;
|
||||
uploader_id?: string;
|
||||
description?: string;
|
||||
duration?: number;
|
||||
upload_date?: string;
|
||||
webpage_url?: string;
|
||||
thumbnail?: string;
|
||||
thumbnails?: { url?: string; width?: number; height?: number }[];
|
||||
subtitles?: Record<string, YtDlpTrack[]>;
|
||||
automatic_captions?: Record<string, YtDlpTrack[]>;
|
||||
}
|
||||
|
||||
export type VideoSource =
|
||||
| { kind: "innertube"; data: any; transcripts: TranscriptInfo[] }
|
||||
| { kind: "yt-dlp"; info: YtDlpInfo; transcripts: TranscriptInfo[] };
|
||||
@@ -0,0 +1,477 @@
|
||||
import { spawnSync } from "child_process";
|
||||
import { writeFileSync } from "fs";
|
||||
|
||||
import { makeError, normalizeError, normalizePublishDate, shouldTryAlternateClient, shouldTryYtDlpFallback } from "./shared.ts";
|
||||
import { parseTranscriptPayload } from "./transcript.ts";
|
||||
import type {
|
||||
Chapter,
|
||||
InnerTubeClient,
|
||||
InnerTubeSession,
|
||||
LanguageMeta,
|
||||
Snippet,
|
||||
TranscriptInfo,
|
||||
VideoMeta,
|
||||
VideoSource,
|
||||
YtDlpInfo,
|
||||
YtDlpTrack,
|
||||
} from "./types.ts";
|
||||
|
||||
const WATCH_URL = "https://www.youtube.com/watch?v=";
|
||||
const INNERTUBE_URL = "https://www.youtube.com/youtubei/v1/player";
|
||||
const WATCH_PAGE_USER_AGENT =
|
||||
"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/134.0.0.0 Safari/537.36";
|
||||
const DEFAULT_WEB_CLIENT_VERSION = "2.20260320.08.00";
|
||||
const YT_DLP_MAX_BUFFER = 32 * 1024 * 1024;
|
||||
|
||||
let cachedYtDlpCommand: { command: string; args: string[]; label: string } | null | undefined;
|
||||
|
||||
const INNER_TUBE_CLIENTS: InnerTubeClient[] = [
|
||||
{
|
||||
id: "android",
|
||||
clientName: "ANDROID",
|
||||
clientHeaderName: "3",
|
||||
clientVersion: "20.10.38",
|
||||
userAgent:
|
||||
"com.google.android.youtube/20.10.38 (Linux; U; Android 14; en_US; Pixel 8 Pro; Build/AP1A.240405.002)",
|
||||
extraContext: {
|
||||
clientFormFactor: "SMALL_FORM_FACTOR",
|
||||
androidSdkVersion: 34,
|
||||
osName: "Android",
|
||||
osVersion: "14",
|
||||
platform: "MOBILE",
|
||||
},
|
||||
},
|
||||
{
|
||||
id: "web",
|
||||
clientName: "WEB",
|
||||
clientHeaderName: "1",
|
||||
userAgent: WATCH_PAGE_USER_AGENT,
|
||||
},
|
||||
{
|
||||
id: "ios",
|
||||
clientName: "IOS",
|
||||
clientHeaderName: "5",
|
||||
clientVersion: "20.10.4",
|
||||
userAgent:
|
||||
"com.google.ios.youtube/20.10.4 (iPhone16,2; U; CPU iOS 18_3 like Mac OS X; en_US)",
|
||||
extraContext: {
|
||||
deviceMake: "Apple",
|
||||
deviceModel: "iPhone16,2",
|
||||
osName: "iPhone",
|
||||
osVersion: "18.3.0.22D5054f",
|
||||
platform: "MOBILE",
|
||||
},
|
||||
},
|
||||
];
|
||||
|
||||
async function fetchHtml(videoId: string): Promise<string> {
|
||||
const watchUrl = `${WATCH_URL}${videoId}&hl=en&persist_hl=1&has_verified=1&bpctr=9999999999`;
|
||||
const baseHeaders = {
|
||||
Accept: "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8",
|
||||
"Accept-Language": "en-US,en;q=0.9",
|
||||
"Cache-Control": "no-cache",
|
||||
Pragma: "no-cache",
|
||||
"User-Agent": WATCH_PAGE_USER_AGENT,
|
||||
};
|
||||
const response = await fetch(watchUrl, { headers: baseHeaders });
|
||||
if (!response.ok) throw new Error(`HTTP ${response.status} fetching video page`);
|
||||
let html = await response.text();
|
||||
if (html.includes('action="https://consent.youtube.com/s"')) {
|
||||
const consentValue = html.match(/name="v" value="(.*?)"/);
|
||||
if (!consentValue) throw new Error("Failed to create consent cookie");
|
||||
const consentResponse = await fetch(watchUrl, {
|
||||
headers: {
|
||||
...baseHeaders,
|
||||
Cookie: `CONSENT=YES+${consentValue[1]}`,
|
||||
},
|
||||
});
|
||||
if (!consentResponse.ok) throw new Error(`HTTP ${consentResponse.status} fetching video page (consent)`);
|
||||
html = await consentResponse.text();
|
||||
}
|
||||
return html;
|
||||
}
|
||||
|
||||
function extractSession(html: string, videoId: string): InnerTubeSession {
|
||||
const apiKey = html.match(/"INNERTUBE_API_KEY":\s*"([a-zA-Z0-9_-]+)"/)?.[1];
|
||||
if (!apiKey) {
|
||||
if (html.includes('class="g-recaptcha"')) throw new Error(`IP blocked for ${videoId} (reCAPTCHA)`);
|
||||
throw new Error(`Cannot extract API key for ${videoId}`);
|
||||
}
|
||||
const webClientVersion =
|
||||
html.match(/"INNERTUBE_CLIENT_VERSION":\s*"([^"]+)"/)?.[1] ||
|
||||
html.match(/"clientVersion":"([^"]+)"/)?.[1] ||
|
||||
DEFAULT_WEB_CLIENT_VERSION;
|
||||
const visitorData =
|
||||
html.match(/"VISITOR_DATA":"([^"]+)"/)?.[1] ||
|
||||
html.match(/"visitorData":"([^"]+)"/)?.[1] ||
|
||||
"";
|
||||
return { apiKey, webClientVersion, visitorData };
|
||||
}
|
||||
|
||||
function buildInnerTubeContext(client: InnerTubeClient, session: InnerTubeSession, videoId: string) {
|
||||
return {
|
||||
context: {
|
||||
client: {
|
||||
hl: "en",
|
||||
gl: "US",
|
||||
utcOffsetMinutes: 0,
|
||||
visitorData: session.visitorData,
|
||||
clientName: client.clientName,
|
||||
clientVersion: client.clientVersion || session.webClientVersion,
|
||||
...client.extraContext,
|
||||
},
|
||||
request: { useSsl: true },
|
||||
},
|
||||
videoId,
|
||||
};
|
||||
}
|
||||
|
||||
async function fetchInnertubeData(videoId: string, session: InnerTubeSession, client: InnerTubeClient): Promise<any> {
|
||||
const clientVersion = client.clientVersion || session.webClientVersion;
|
||||
const headers: Record<string, string> = {
|
||||
Accept: "application/json",
|
||||
"Accept-Language": "en-US,en;q=0.9",
|
||||
"Content-Type": "application/json",
|
||||
Origin: "https://www.youtube.com",
|
||||
Referer: `${WATCH_URL}${videoId}`,
|
||||
"User-Agent": client.userAgent,
|
||||
"X-YouTube-Client-Name": client.clientHeaderName || "1",
|
||||
"X-YouTube-Client-Version": clientVersion,
|
||||
};
|
||||
if (session.visitorData) headers["X-Goog-Visitor-Id"] = session.visitorData;
|
||||
const response = await fetch(`${INNERTUBE_URL}?key=${session.apiKey}&prettyPrint=false`, {
|
||||
method: "POST",
|
||||
headers,
|
||||
body: JSON.stringify(buildInnerTubeContext(client, session, videoId)),
|
||||
});
|
||||
if (response.status === 429) throw new Error(`IP blocked for ${videoId} (429)`);
|
||||
if (!response.ok) throw new Error(`HTTP ${response.status} from InnerTube API`);
|
||||
return response.json();
|
||||
}
|
||||
|
||||
function assertPlayability(data: any, videoId: string) {
|
||||
const playabilityStatus = data?.playabilityStatus;
|
||||
if (!playabilityStatus) return;
|
||||
const status = playabilityStatus.status;
|
||||
if (status === "OK" || !status) return;
|
||||
const reason = playabilityStatus.reason || "";
|
||||
const reasonLower = reason.toLowerCase();
|
||||
if (status === "LOGIN_REQUIRED") {
|
||||
if (reasonLower.includes("bot")) throw makeError(`Request blocked for ${videoId}: bot detected`, "BOT_DETECTED");
|
||||
if (reasonLower.includes("inappropriate")) throw makeError(`Age restricted: ${videoId}`, "AGE_RESTRICTED");
|
||||
}
|
||||
if (status === "ERROR" && reasonLower.includes("unavailable")) {
|
||||
if (videoId.startsWith("http")) throw makeError("Invalid video ID: pass the ID, not the URL", "INVALID_VIDEO_ID");
|
||||
throw makeError(`Video unavailable: ${videoId}`, "VIDEO_UNAVAILABLE");
|
||||
}
|
||||
const subreasons = playabilityStatus.errorScreen?.playerErrorMessageRenderer?.subreason?.runs?.map((run: any) => run.text).join("") || "";
|
||||
throw new Error(`Video unplayable (${videoId}): ${reason} ${subreasons}`.trim());
|
||||
}
|
||||
|
||||
function extractCaptionsJson(data: any, videoId: string): any {
|
||||
assertPlayability(data, videoId);
|
||||
const captionsJson = data?.captions?.playerCaptionsTracklistRenderer;
|
||||
if (!captionsJson || !captionsJson.captionTracks) throw makeError(`Transcripts disabled for ${videoId}`, "TRANSCRIPTS_DISABLED");
|
||||
return captionsJson;
|
||||
}
|
||||
|
||||
function buildTranscriptList(captionsJson: any): TranscriptInfo[] {
|
||||
const translationLanguages = (captionsJson.translationLanguages || []).map((language: any) => ({
|
||||
language: language.languageName?.runs?.[0]?.text || language.languageName?.simpleText || "",
|
||||
languageCode: language.languageCode,
|
||||
}));
|
||||
return (captionsJson.captionTracks || []).map((track: any) => ({
|
||||
language: track.name?.runs?.[0]?.text || track.name?.simpleText || "",
|
||||
languageCode: track.languageCode,
|
||||
isGenerated: track.kind === "asr",
|
||||
isTranslatable: !!track.isTranslatable,
|
||||
baseUrl: track.baseUrl || "",
|
||||
translationLanguages: track.isTranslatable ? translationLanguages : [],
|
||||
}));
|
||||
}
|
||||
|
||||
export async function fetchTranscriptSnippets(
|
||||
info: TranscriptInfo,
|
||||
translateTo?: string
|
||||
): Promise<{ snippets: Snippet[]; language: string; languageCode: string }> {
|
||||
let url = info.baseUrl;
|
||||
let language = info.language;
|
||||
let languageCode = info.languageCode;
|
||||
if (translateTo) {
|
||||
if (!info.isTranslatable) throw new Error(`Transcript ${info.languageCode} is not translatable`);
|
||||
const translatedLanguage = info.translationLanguages.find((entry) => entry.languageCode === translateTo);
|
||||
if (!translatedLanguage) throw new Error(`Translation language ${translateTo} not available`);
|
||||
url += `&tlang=${translateTo}`;
|
||||
language = translatedLanguage.language;
|
||||
languageCode = translateTo;
|
||||
}
|
||||
const response = await fetch(url, {
|
||||
headers: {
|
||||
"Accept-Language": "en-US,en;q=0.9",
|
||||
"User-Agent": WATCH_PAGE_USER_AGENT,
|
||||
},
|
||||
});
|
||||
if (!response.ok) throw new Error(`HTTP ${response.status} fetching transcript`);
|
||||
return {
|
||||
snippets: parseTranscriptPayload(await response.text(), url),
|
||||
language,
|
||||
languageCode,
|
||||
};
|
||||
}
|
||||
|
||||
export function detectYtDlpCommand(): { command: string; args: string[]; label: string } | null {
|
||||
if (cachedYtDlpCommand !== undefined) return cachedYtDlpCommand;
|
||||
const candidates = [
|
||||
{ command: "yt-dlp", args: [], label: "yt-dlp" },
|
||||
{ command: "uvx", args: ["--from", "yt-dlp", "yt-dlp"], label: "uvx --from yt-dlp yt-dlp" },
|
||||
{ command: "python3", args: ["-m", "yt_dlp"], label: "python3 -m yt_dlp" },
|
||||
];
|
||||
for (const candidate of candidates) {
|
||||
const probe = spawnSync(candidate.command, [...candidate.args, "--version"], {
|
||||
encoding: "utf8",
|
||||
maxBuffer: 1024 * 1024,
|
||||
});
|
||||
if (probe.status !== 0) continue;
|
||||
|
||||
const helpProbe = spawnSync(candidate.command, [...candidate.args, "--help"], {
|
||||
encoding: "utf8",
|
||||
maxBuffer: 2 * 1024 * 1024,
|
||||
});
|
||||
const helpText = `${helpProbe.stdout || ""}\n${helpProbe.stderr || ""}`;
|
||||
const supportsRequiredFlags =
|
||||
helpProbe.status === 0 &&
|
||||
helpText.includes("--js-runtimes") &&
|
||||
helpText.includes("--remote-components");
|
||||
|
||||
if (supportsRequiredFlags) {
|
||||
cachedYtDlpCommand = candidate;
|
||||
return candidate;
|
||||
}
|
||||
}
|
||||
cachedYtDlpCommand = null;
|
||||
return cachedYtDlpCommand;
|
||||
}
|
||||
|
||||
export function selectYtDlpTrack(entries: YtDlpTrack[]): YtDlpTrack | null {
|
||||
const preferredExts = ["json3", "srv3", "srv2", "srv1", "ttml", "vtt"];
|
||||
for (const ext of preferredExts) {
|
||||
const match = entries.find((entry) => entry.url && entry.ext === ext);
|
||||
if (match) return match;
|
||||
}
|
||||
return entries.find((entry) => !!entry.url) || null;
|
||||
}
|
||||
|
||||
export function buildTranscriptListFromYtDlp(info: YtDlpInfo): TranscriptInfo[] {
|
||||
const translationLanguages = Object.entries(info.automatic_captions || {}).map(([languageCode, entries]) => ({
|
||||
language: entries.find((entry) => entry.name)?.name || languageCode,
|
||||
languageCode,
|
||||
}));
|
||||
const manual = Object.entries(info.subtitles || {}).flatMap(([languageCode, entries]) => {
|
||||
const selected = selectYtDlpTrack(entries);
|
||||
if (!selected?.url) return [];
|
||||
return [{
|
||||
language: selected.name || languageCode,
|
||||
languageCode,
|
||||
isGenerated: false,
|
||||
isTranslatable: translationLanguages.length > 0,
|
||||
baseUrl: selected.url,
|
||||
translationLanguages,
|
||||
}];
|
||||
});
|
||||
const generated = Object.entries(info.automatic_captions || {}).flatMap(([languageCode, entries]) => {
|
||||
const selected = selectYtDlpTrack(entries);
|
||||
if (!selected?.url) return [];
|
||||
return [{
|
||||
language: selected.name || languageCode,
|
||||
languageCode,
|
||||
isGenerated: true,
|
||||
isTranslatable: translationLanguages.length > 0,
|
||||
baseUrl: selected.url,
|
||||
translationLanguages,
|
||||
}];
|
||||
});
|
||||
return [...manual, ...generated];
|
||||
}
|
||||
|
||||
function fetchYtDlpInfo(videoId: string): YtDlpInfo {
|
||||
const command = detectYtDlpCommand();
|
||||
if (!command) {
|
||||
throw makeError(
|
||||
`Request blocked for ${videoId}: bot detected. yt-dlp fallback unavailable (install yt-dlp or uv).`,
|
||||
"YT_DLP_UNAVAILABLE"
|
||||
);
|
||||
}
|
||||
|
||||
const args = [
|
||||
...command.args,
|
||||
"-J",
|
||||
"--skip-download",
|
||||
"--js-runtimes",
|
||||
"bun",
|
||||
"--remote-components",
|
||||
"ejs:github",
|
||||
];
|
||||
const cookiesFromBrowser = process.env.YOUTUBE_TRANSCRIPT_COOKIES_FROM_BROWSER?.trim();
|
||||
if (cookiesFromBrowser) args.push("--cookies-from-browser", cookiesFromBrowser);
|
||||
args.push(`${WATCH_URL}${videoId}`);
|
||||
|
||||
const result = spawnSync(command.command, args, {
|
||||
encoding: "utf8",
|
||||
maxBuffer: YT_DLP_MAX_BUFFER,
|
||||
});
|
||||
if (result.status !== 0) {
|
||||
const stderr = (result.stderr || "").trim();
|
||||
const stdout = (result.stdout || "").trim();
|
||||
const detail = stderr || stdout || `exit ${result.status ?? "unknown"}`;
|
||||
throw makeError(`yt-dlp fallback failed for ${videoId} (${command.label}): ${detail}`, "YT_DLP_FAILED");
|
||||
}
|
||||
return JSON.parse(result.stdout);
|
||||
}
|
||||
|
||||
async function fetchInnertubeSource(videoId: string): Promise<VideoSource> {
|
||||
const html = await fetchHtml(videoId);
|
||||
const session = extractSession(html, videoId);
|
||||
const attempts: string[] = [];
|
||||
let lastError: Error | null = null;
|
||||
|
||||
for (const client of INNER_TUBE_CLIENTS) {
|
||||
try {
|
||||
const data = await fetchInnertubeData(videoId, session, client);
|
||||
const captionsJson = extractCaptionsJson(data, videoId);
|
||||
return { kind: "innertube", data, transcripts: buildTranscriptList(captionsJson) };
|
||||
} catch (error) {
|
||||
const normalized = normalizeError(error);
|
||||
attempts.push(`${client.id}: ${normalized.message}`);
|
||||
lastError = normalized;
|
||||
if (!shouldTryAlternateClient(normalized)) break;
|
||||
}
|
||||
}
|
||||
|
||||
if (!lastError) throw makeError(`Unable to fetch transcript metadata for ${videoId}`, "UNKNOWN");
|
||||
if (attempts.length > 1) {
|
||||
throw makeError(`${lastError.message}. Tried clients: ${attempts.join("; ")}`, normalizeError(lastError).code);
|
||||
}
|
||||
throw lastError;
|
||||
}
|
||||
|
||||
export async function resolveVideoSource(
|
||||
videoId: string,
|
||||
fetchPrimary: (videoId: string) => Promise<VideoSource>,
|
||||
fetchFallback: (videoId: string) => YtDlpInfo,
|
||||
logWarning: (message: string) => void = (message) => console.error(message)
|
||||
): Promise<VideoSource> {
|
||||
try {
|
||||
return await fetchPrimary(videoId);
|
||||
} catch (error) {
|
||||
const normalized = normalizeError(error);
|
||||
if (!shouldTryYtDlpFallback(normalized)) throw normalized;
|
||||
logWarning(`Warning (${videoId}): ${normalized.message}. Retrying with yt-dlp fallback.`);
|
||||
const info = fetchFallback(videoId);
|
||||
const transcripts = buildTranscriptListFromYtDlp(info);
|
||||
if (!transcripts.length) throw makeError(`Transcripts disabled for ${videoId}`, "TRANSCRIPTS_DISABLED");
|
||||
return { kind: "yt-dlp", info, transcripts };
|
||||
}
|
||||
}
|
||||
|
||||
export async function fetchVideoSource(videoId: string): Promise<VideoSource> {
|
||||
return resolveVideoSource(videoId, fetchInnertubeSource, fetchYtDlpInfo);
|
||||
}
|
||||
|
||||
export function parseChapters(description: string, duration: number = 0): Chapter[] {
|
||||
const raw: { title: string; start: number }[] = [];
|
||||
for (const line of description.split("\n")) {
|
||||
const match = line.trim().match(/^(?:(\d{1,2}):)?(\d{1,2}):(\d{2})\s+(.+)$/);
|
||||
if (match) {
|
||||
const hours = match[1] ? parseInt(match[1]) : 0;
|
||||
raw.push({ title: match[4].trim(), start: hours * 3600 + parseInt(match[2]) * 60 + parseInt(match[3]) });
|
||||
}
|
||||
}
|
||||
if (raw.length < 2) return [];
|
||||
return raw.map((chapter, index) => ({
|
||||
title: chapter.title,
|
||||
start: chapter.start,
|
||||
end: index < raw.length - 1 ? raw[index + 1].start : Math.max(duration, chapter.start),
|
||||
}));
|
||||
}
|
||||
|
||||
export function getThumbnailUrls(videoId: string, data: any): string[] {
|
||||
const urls = [
|
||||
`https://i.ytimg.com/vi/${videoId}/maxresdefault.jpg`,
|
||||
`https://i.ytimg.com/vi/${videoId}/hqdefault.jpg`,
|
||||
];
|
||||
const thumbnails = data?.videoDetails?.thumbnail?.thumbnails ||
|
||||
data?.microformat?.playerMicroformatRenderer?.thumbnail?.thumbnails ||
|
||||
[];
|
||||
if (thumbnails.length) {
|
||||
const sorted = [...thumbnails].sort((a: any, b: any) => (b.width || 0) - (a.width || 0));
|
||||
for (const thumbnail of sorted) {
|
||||
if (thumbnail.url && !urls.includes(thumbnail.url)) urls.push(thumbnail.url);
|
||||
}
|
||||
}
|
||||
return urls;
|
||||
}
|
||||
|
||||
export function getYtDlpThumbnailUrls(videoId: string, info: YtDlpInfo): string[] {
|
||||
const urls = getThumbnailUrls(videoId, null);
|
||||
const thumbnails = Array.isArray(info.thumbnails) ? info.thumbnails : [];
|
||||
const sorted = [...thumbnails].sort((a, b) => (b?.width || 0) - (a?.width || 0));
|
||||
for (const thumbnail of sorted) {
|
||||
if (thumbnail?.url && !urls.includes(thumbnail.url)) urls.push(thumbnail.url);
|
||||
}
|
||||
if (info.thumbnail && !urls.includes(info.thumbnail)) urls.push(info.thumbnail);
|
||||
return urls;
|
||||
}
|
||||
|
||||
export function buildVideoMeta(data: any, videoId: string, language: LanguageMeta, chapters: Chapter[]): VideoMeta {
|
||||
const videoDetails = data?.videoDetails || {};
|
||||
const microformat = data?.microformat?.playerMicroformatRenderer || {};
|
||||
return {
|
||||
videoId,
|
||||
title: videoDetails.title || microformat.title?.simpleText || "",
|
||||
channel: videoDetails.author || microformat.ownerChannelName || "",
|
||||
channelId: videoDetails.channelId || microformat.externalChannelId || "",
|
||||
description: videoDetails.shortDescription || microformat.description?.simpleText || "",
|
||||
duration: parseInt(videoDetails.lengthSeconds || "0"),
|
||||
publishDate: microformat.publishDate || microformat.uploadDate || "",
|
||||
url: `${WATCH_URL}${videoId}`,
|
||||
coverImage: "",
|
||||
thumbnailUrl: getThumbnailUrls(videoId, data)[0],
|
||||
language,
|
||||
chapters,
|
||||
};
|
||||
}
|
||||
|
||||
export function buildVideoMetaFromYtDlp(
|
||||
info: YtDlpInfo,
|
||||
videoId: string,
|
||||
language: LanguageMeta,
|
||||
chapters: Chapter[]
|
||||
): VideoMeta {
|
||||
return {
|
||||
videoId,
|
||||
title: info.title || "",
|
||||
channel: info.channel || info.uploader || "",
|
||||
channelId: info.channel_id || info.uploader_id || "",
|
||||
description: info.description || "",
|
||||
duration: Number(info.duration || 0),
|
||||
publishDate: normalizePublishDate(info.upload_date),
|
||||
url: info.webpage_url || `${WATCH_URL}${videoId}`,
|
||||
coverImage: "",
|
||||
thumbnailUrl: getYtDlpThumbnailUrls(videoId, info)[0] || "",
|
||||
language,
|
||||
chapters,
|
||||
};
|
||||
}
|
||||
|
||||
export async function downloadCoverImage(urls: string[], outputPath: string): Promise<boolean> {
|
||||
for (const url of urls) {
|
||||
try {
|
||||
const response = await fetch(url);
|
||||
if (response.ok) {
|
||||
writeFileSync(outputPath, Buffer.from(await response.arrayBuffer()));
|
||||
return true;
|
||||
}
|
||||
} catch {}
|
||||
}
|
||||
return false;
|
||||
}
|
||||
Reference in New Issue
Block a user