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[codex] Refactor skills into focused references (#135)

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* docs: add runtime-neutral User Input Tools convention across skills

Introduce docs/user-input-tools.md as the author-side canonical source
and inline the tool-selection rule into every SKILL.md that prompts
the user. Also add Skill Self-Containment and User Input Tools sections
to CLAUDE.md and the copy-verbatim template to docs/creating-skills.md,
so skills stay portable across Claude Code, Codex, Hermes, and other
runtimes.

* feat: runtime-neutral image generation convention across skills

- Introduce inline `## Image Generation Tools` rule in every rendering SKILL.md so skills delegate backend choice instead of hard-coding one; author-side canonical copy lives in docs/image-generation-tools.md.
- Add `## Reference Images` support (`--ref`, frontmatter `references:` with direct/style/palette usage) to all seven image-rendering skills.
- Move build-batch.ts (with ref propagation into batch JSON) from baoyu-article-illustrator to baoyu-imagine so non-backend skills don't own backend-specific scripts; update baoyu-image-gen stub in sync and relax the CLAUDE.md deprecation note accordingly.

* refactor: slim heavy SKILL.md files and move detail to references/

Trim the four largest active skills and move presets, option tables, and
confirmation scripts into per-skill references/ so SKILL.md stays focused
on the decision flow.

- baoyu-slide-deck: 761→258, + styles-gallery.md, confirmation.md
- baoyu-image-cards: 657→280, + gallery.md, confirmation.md
- baoyu-post-to-wechat: 518→267, + multi-account.md, api-setup.md
- baoyu-imagine: 500→230, + providers/, usage-examples.md

Also un-deprecate baoyu-image-gen (drop stub warning) so it stays functional
alongside baoyu-imagine, and update CLAUDE.md to reflect that both
superseded skills are kept in sync rather than stubbed.

* refactor: slim four medium SKILL.md files into references/

Continue the P2 pattern on the next tier of skills — move option catalogs,
per-provider/adapter detail, and repeated EXTEND.md path boilerplate into
their own references so SKILL.md stays focused on the decision flow.

- baoyu-comic: 380→297 (art/tone/preset tables → auto-selection.md;
  Step 7 expanded detail → workflow.md)
- baoyu-infographic: 312→207 (layouts/styles/combinations/keywords →
  gallery.md; ASCII box tables → markdown tables)
- baoyu-format-markdown: 376→296 (title + summary generation →
  title-summary.md; ASCII box tables → markdown tables)
- baoyu-url-to-markdown: 334→169 (quality gate + recovery →
  quality-gate.md; adapters + media download → adapters.md)

* chore: sync deprecated skills with their replacements

Per project policy, baoyu-xhs-images and baoyu-image-gen are kept
functional alongside the active skills they were superseded by. Sync
their SKILL.md bodies and references/ to the slimmed baoyu-image-cards
and baoyu-imagine versions respectively, so cross-cutting fixes stay
consistent. Only the frontmatter (name, description, version, homepage)
differs — content is identical.

- baoyu-xhs-images: 657→281 (synced with baoyu-image-cards + new
  confirmation.md, gallery.md)
- baoyu-image-gen: 408→231 (synced with baoyu-imagine + new
  providers/, usage-examples.md)

* refactor: collapse EXTEND.md boilerplate into priority tables

Replace the dual bash/powershell existence-check blocks and ASCII box
art with a single markdown priority table across nine SKILL.md files.
The runtime-neutral phrasing removes shell-specific snippets without
losing the priority semantics.

* fix: address refactor-skills branch review findings

- image-gen: restore EXTEND.md paths to baoyu-image-gen (were pointing at
  baoyu-imagine) and mark descriptions of both deprecated skills as
  [Deprecated].
- xhs-images: sync neon/warm palettes with image-cards to add the
  "do not render color names/hex as visible text" safety sentence.
- infographic: restore Layout Gallery (21), Style Gallery (21),
  Recommended Combinations, and Keyword Shortcuts inline (previous
  refactor split them out but SKILL.md still depended on them), and add
  the missing references/config/first-time-setup.md + preferences-schema.md.
- image-cards / xhs-images / slide-deck / format-markdown: restore the
  sections that got over-slimmed into references/ (galleries, presets,
  dimensions, auto-selection, style x layout matrix, title/summary flow)
  and drop the now-empty shell files.
- docs/image-generation-tools.md: note that backend skills themselves
  (baoyu-imagine, baoyu-image-gen, baoyu-danger-gemini-web) are exempt
  from the ## Image Generation Tools section requirement.

* feat(image-gen): sync Z.AI GLM-Image provider from baoyu-imagine

Add Z.AI as a full provider in the deprecated baoyu-image-gen skill so
it stays in sync with baoyu-imagine's provider list.

- new scripts/providers/zai.ts + zai.test.ts (verbatim port; test
  factory trimmed to match image-gen's CliArgs shape).
- types.ts: "zai" added to Provider union and default_model.
- main.ts: rate-limit defaults, provider help text, env var help,
  --provider validation, loadProviderModule, detectProvider
  auto-detect chain, getModelForProvider, YAML parser allow-lists.
- references/config: Q2e Z.AI model question + zai slot in the
  preferences schema and batch.provider_limits.

Scope is intentionally limited to the Z.AI chain; unrelated drift
between image-gen and imagine (OpenAI image-API dialect,
aspectRatioSource, imageSizeSource) is left alone.

* docs: align inline-convention wording and note backend-skill exemption

- docs/user-input-tools.md: fix stale "links here" wording so it
  matches the inline convention already enforced everywhere else.
- CLAUDE.md §Image Generation Tools: inline the backend-skill
  exemption so readers don't need to cross-reference
  docs/image-generation-tools.md.
This commit is contained in:
Jim Liu 宝玉
2026-04-19 00:48:44 -05:00
committed by GitHub
parent 5b20f9a746
commit 2c800c670a
58 changed files with 3895 additions and 3269 deletions
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skills/baoyu-imagine/SKILL.md
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@@ -13,154 +13,65 @@ metadata:
# Image Generation (AI SDK)
Official API-based image generation. Supports OpenAI, Azure OpenAI, Google, OpenRouter, DashScope (阿里通义万象), Z.AI GLM-Image, MiniMax, 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), Z.AI GLM-Image, MiniMax, Jimeng (即梦), Seedream (豆包) and Replicate.
## User Input Tools
When this skill prompts the user, follow this tool-selection rule (priority order):
1. **Prefer built-in user-input tools** exposed by the current agent runtime — e.g., `AskUserQuestion`, `request_user_input`, `clarify`, `ask_user`, or any equivalent.
2. **Fallback**: if no such tool exists, emit a numbered plain-text message and ask the user to reply with the chosen number/answer for each question.
3. **Batching**: if the tool supports multiple questions per call, combine all applicable questions into a single call; if only single-question, ask them one at a time in priority order.
Concrete `AskUserQuestion` references below are examples — substitute the local equivalent in other runtimes.
## Script Directory
**Agent Execution**:
1. `{baseDir}` = this SKILL.md file's directory
2. Script path = `{baseDir}/scripts/main.ts`
3. Resolve `${BUN_X}` runtime: if `bun` installed → `bun`; if `npx` available → `npx -y bun`; else suggest installing bun
`{baseDir}` = this SKILL.md's directory. Main script: `{baseDir}/scripts/main.ts`. Resolve `${BUN_X}`: prefer `bun`; else `npx -y bun`; else suggest `brew install oven-sh/bun/bun`.
## Step 0: Load Preferences ⛔ BLOCKING
**CRITICAL**: This step MUST complete BEFORE any image generation. Do NOT skip or defer.
This step MUST complete before any image generation — generation is blocked until EXTEND.md exists.
Check EXTEND.md existence (priority: project → user):
Check these paths in order; first hit wins:
```bash
# macOS, Linux, WSL, Git Bash
test -f .baoyu-skills/baoyu-imagine/EXTEND.md && echo "project"
test -f "${XDG_CONFIG_HOME:-$HOME/.config}/baoyu-skills/baoyu-imagine/EXTEND.md" && echo "xdg"
test -f "$HOME/.baoyu-skills/baoyu-imagine/EXTEND.md" && echo "user"
```
```powershell
# PowerShell (Windows)
if (Test-Path .baoyu-skills/baoyu-imagine/EXTEND.md) { "project" }
$xdg = if ($env:XDG_CONFIG_HOME) { $env:XDG_CONFIG_HOME } else { "$HOME/.config" }
if (Test-Path "$xdg/baoyu-skills/baoyu-imagine/EXTEND.md") { "xdg" }
if (Test-Path "$HOME/.baoyu-skills/baoyu-imagine/EXTEND.md") { "user" }
```
| Result | Action |
|--------|--------|
| Found | Load, parse, apply settings. If `default_model.[provider]` is null → ask model only (Flow 2) |
| Not found | ⛔ Run first-time setup ([references/config/first-time-setup.md](references/config/first-time-setup.md)) → Save EXTEND.md → Then continue |
**CRITICAL**: If not found, complete the full setup (provider + model + quality + save location) using AskUserQuestion BEFORE generating any images. Generation is BLOCKED until EXTEND.md is created.
| Path | Location |
|------|----------|
| `.baoyu-skills/baoyu-imagine/EXTEND.md` | Project directory |
| Path | Scope |
|------|-------|
| `.baoyu-skills/baoyu-imagine/EXTEND.md` | Project |
| `${XDG_CONFIG_HOME:-$HOME/.config}/baoyu-skills/baoyu-imagine/EXTEND.md` | XDG |
| `$HOME/.baoyu-skills/baoyu-imagine/EXTEND.md` | User home |
Legacy compatibility: if `.baoyu-skills/baoyu-image-gen/EXTEND.md` exists and the new path does not, runtime renames it to `baoyu-imagine`. If both files exist, runtime leaves them unchanged and uses the new path.
- **Found** → load, parse, apply. If `default_model.[provider]` is null → ask model only.
- **Not found** → run first-time setup (`references/config/first-time-setup.md`) using AskUserQuestion to collect provider + model + quality + save location. Save EXTEND.md, then continue. Do not generate images before this completes.
**EXTEND.md Supports**: Default provider | Default quality | Default aspect ratio | Default image size | OpenAI image API dialect | Default models | Batch worker cap | Provider-specific batch limits
Legacy compatibility: if `.baoyu-skills/baoyu-image-gen/EXTEND.md` exists and the new path doesn't, the runtime renames it to `baoyu-imagine`. If both exist, the runtime leaves them alone and uses the new path.
Schema: `references/config/preferences-schema.md`
**EXTEND.md keys**: default provider, default quality, default aspect ratio, default image size, OpenAI image API dialect, default models, batch worker cap, provider-specific batch limits. Schema: `references/config/preferences-schema.md`.
## Usage
Minimum working examples — see `references/usage-examples.md` for the full set including per-provider invocations and batch mode.
```bash
# Basic
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image cat.png
# With aspect ratio
${BUN_X} {baseDir}/scripts/main.ts --prompt "A landscape" --image out.png --ar 16:9
# With aspect ratio and high quality
${BUN_X} {baseDir}/scripts/main.ts --prompt "A landscape" --image out.png --ar 16:9 --quality 2k
# High quality
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --quality 2k
# From prompt files
# Prompt from files
${BUN_X} {baseDir}/scripts/main.ts --promptfiles system.md content.md --image out.png
# With reference images (Google, OpenAI, Azure OpenAI, OpenRouter, Replicate supported families, MiniMax, or Seedream 4.0/4.5/5.0)
# With reference image
${BUN_X} {baseDir}/scripts/main.ts --prompt "Make blue" --image out.png --ref source.png
# With reference images (explicit provider/model)
${BUN_X} {baseDir}/scripts/main.ts --prompt "Make blue" --image out.png --provider google --model gemini-3-pro-image-preview --ref source.png
# Azure OpenAI (model means deployment name)
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider azure --model gpt-image-1.5
# OpenRouter (recommended default model)
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider openrouter
# OpenRouter with reference images
${BUN_X} {baseDir}/scripts/main.ts --prompt "Make blue" --image out.png --provider openrouter --model google/gemini-3.1-flash-image-preview --ref source.png
# Specific provider
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider openai
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider dashscope --model qwen-image-2.0-pro
# DashScope (阿里通义万象)
${BUN_X} {baseDir}/scripts/main.ts --prompt "一只可爱的猫" --image out.png --provider dashscope
# DashScope Qwen-Image 2.0 Pro (recommended for custom sizes and text rendering)
${BUN_X} {baseDir}/scripts/main.ts --prompt "为咖啡品牌设计一张 21:9 横幅海报，包含清晰中文标题" --image out.png --provider dashscope --model qwen-image-2.0-pro --size 2048x872
# DashScope legacy Qwen fixed-size model
${BUN_X} {baseDir}/scripts/main.ts --prompt "一张电影感海报" --image out.png --provider dashscope --model qwen-image-max --size 1664x928
# Z.AI GLM-image
${BUN_X} {baseDir}/scripts/main.ts --prompt "一张带清晰中文标题的科技海报" --image out.png --provider zai
# Z.AI GLM-image with explicit custom size
${BUN_X} {baseDir}/scripts/main.ts --prompt "A science illustration with labels" --image out.png --provider zai --model glm-image --size 1472x1088
# MiniMax
${BUN_X} {baseDir}/scripts/main.ts --prompt "A fashion editorial portrait by a bright studio window" --image out.jpg --provider minimax
# MiniMax with subject reference (best for character/portrait consistency)
${BUN_X} {baseDir}/scripts/main.ts --prompt "A girl stands by the library window, cinematic lighting" --image out.jpg --provider minimax --model image-01 --ref portrait.png --ar 16:9
# MiniMax with custom size (documented for image-01)
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cinematic poster" --image out.jpg --provider minimax --model image-01 --size 1536x1024
# Replicate (default: google/nano-banana-2)
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider replicate
# Replicate Seedream 4.5
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cinematic portrait" --image out.png --provider replicate --model bytedance/seedream-4.5 --ar 3:2
# Replicate Wan 2.7 Image Pro
${BUN_X} {baseDir}/scripts/main.ts --prompt "A concept frame" --image out.png --provider replicate --model wan-video/wan-2.7-image-pro --size 2048x1152
# Batch mode with saved prompt files
${BUN_X} {baseDir}/scripts/main.ts --batchfile batch.json
# Batch mode with explicit worker count
${BUN_X} {baseDir}/scripts/main.ts --batchfile batch.json --jobs 4 --json
# Batch mode
${BUN_X} {baseDir}/scripts/main.ts --batchfile batch.json --jobs 4
```
### Batch File Format
```json
{
"jobs": 4,
"tasks": [
{
"id": "hero",
"promptFiles": ["prompts/hero.md"],
"image": "out/hero.png",
"provider": "replicate",
"model": "google/nano-banana-2",
"ar": "16:9",
"quality": "2k"
},
{
"id": "diagram",
"promptFiles": ["prompts/diagram.md"],
"image": "out/diagram.png",
"ref": ["references/original.png"]
}
]
}
```
Paths in `promptFiles`, `image`, and `ref` are resolved relative to the batch file's directory. `jobs` is optional (overridden by CLI `--jobs`). Top-level array format (without `jobs` wrapper) is also accepted.
## Options
| Option | Description |
@@ -171,14 +82,14 @@ Paths in `promptFiles`, `image`, and `ref` are resolved relative to the batch fi
| `--batchfile <path>` | JSON batch file for multi-image generation |
| `--jobs <count>` | Worker count for batch mode (default: auto, max from config, built-in default 10) |
| `--provider google\|openai\|azure\|openrouter\|dashscope\|zai\|minimax\|jimeng\|seedream\|replicate` | Force provider (default: auto-detect) |
| `--model <id>`, `-m` | Model ID (Google: `gemini-3-pro-image-preview`; OpenAI: `gpt-image-1.5`; Azure: deployment name such as `gpt-image-1.5` or `image-prod`; OpenRouter: `google/gemini-3.1-flash-image-preview`; DashScope: `qwen-image-2.0-pro`; Z.AI: `glm-image`; MiniMax: `image-01`) |
| `--ar <ratio>` | Aspect ratio (e.g., `16:9`, `1:1`, `4:3`) |
| `--size <WxH>` | Size (e.g., `1024x1024`) |
| `--model <id>`, `-m` | Model ID — see provider references for defaults and allowed values |
| `--ar <ratio>` | Aspect ratio (`16:9`, `1:1`, `4:3`, …) |
| `--size <WxH>` | Explicit size (e.g., `1024x1024`) |
| `--quality normal\|2k` | Quality preset (default: `2k`) |
| `--imageSize 1K\|2K\|4K` | Image size for Google/OpenRouter (default: from quality) |
| `--imageApiDialect openai-native\|ratio-metadata` | OpenAI-compatible image API dialect. Use `ratio-metadata` when the endpoint is OpenAI-compatible but expects aspect-ratio `size` plus `metadata.resolution` instead of pixel `size` |
| `--ref <files...>` | Reference images. Supported by Google multimodal, OpenAI GPT Image edits, Azure OpenAI edits (PNG/JPG only), OpenRouter multimodal models, Replicate supported families, MiniMax subject-reference, and Seedream 5.0/4.5/4.0. Not supported by Jimeng, Seedream 3.0, or removed SeedEdit 3.0 |
| `--n <count>` | Number of images. Replicate currently supports only `--n 1` because this path saves exactly one output image |
| `--imageApiDialect openai-native\|ratio-metadata` | OpenAI-compatible endpoint dialect — use `ratio-metadata` for gateways that expect aspect-ratio `size` plus `metadata.resolution` |
| `--ref <files...>` | Reference images. Supported by Google multimodal, OpenAI GPT Image edits, Azure OpenAI edits (PNG/JPG only), OpenRouter multimodal models, Replicate supported families, MiniMax subject-reference, Seedream 5.0/4.5/4.0. Not supported by Jimeng, Seedream 3.0, SeedEdit 3.0 |
| `--n <count>` | Number of images. Replicate requires `--n 1` (single-output save semantics) |
| `--json` | JSON output |
## Environment Variables
@@ -189,293 +100,109 @@ Paths in `promptFiles`, `image`, and `ref` are resolved relative to the batch fi
| `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) |
| `ZAI_API_KEY` | Z.AI API key |
| `BIGMODEL_API_KEY` | Backward-compatible alias for Z.AI API key |
| `DASHSCOPE_API_KEY` | DashScope API key |
| `ZAI_API_KEY` (alias `BIGMODEL_API_KEY`) | Z.AI API key |
| `MINIMAX_API_KEY` | MiniMax API key |
| `REPLICATE_API_TOKEN` | Replicate API token |
| `JIMENG_ACCESS_KEY_ID` | Jimeng (即梦) Volcengine access key |
| `JIMENG_SECRET_ACCESS_KEY` | Jimeng (即梦) Volcengine secret key |
| `JIMENG_ACCESS_KEY_ID`, `JIMENG_SECRET_ACCESS_KEY` | Jimeng (即梦) Volcengine credentials |
| `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`) |
| `ZAI_IMAGE_MODEL` | Z.AI model override (default: `glm-image`) |
| `BIGMODEL_IMAGE_MODEL` | Backward-compatible alias for Z.AI model override |
| `MINIMAX_IMAGE_MODEL` | MiniMax model override (default: `image-01`) |
| `REPLICATE_IMAGE_MODEL` | Replicate model override (default: google/nano-banana-2) |
| `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 |
| `OPENAI_IMAGE_API_DIALECT` | OpenAI-compatible image API dialect override (`openai-native` or `ratio-metadata`) |
| `AZURE_OPENAI_BASE_URL` | Azure resource endpoint or deployment endpoint |
| `AZURE_API_VERSION` | Azure image API version (default: `2025-04-01-preview`) |
| `OPENROUTER_BASE_URL` | Custom OpenRouter endpoint (default: `https://openrouter.ai/api/v1`) |
| `OPENROUTER_HTTP_REFERER` | Optional app/site URL for OpenRouter attribution |
| `OPENROUTER_TITLE` | Optional app name for OpenRouter attribution |
| `GOOGLE_BASE_URL` | Custom Google endpoint |
| `DASHSCOPE_BASE_URL` | Custom DashScope endpoint |
| `ZAI_BASE_URL` | Custom Z.AI endpoint (default: `https://api.z.ai/api/paas/v4`) |
| `BIGMODEL_BASE_URL` | Backward-compatible alias for Z.AI endpoint |
| `MINIMAX_BASE_URL` | Custom MiniMax endpoint (default: `https://api.minimax.io`) |
| `REPLICATE_BASE_URL` | Custom Replicate endpoint |
| `JIMENG_BASE_URL` | Custom Jimeng endpoint (default: `https://visual.volcengineapi.com`) |
| `JIMENG_REGION` | Jimeng region (default: `cn-north-1`) |
| `SEEDREAM_BASE_URL` | Custom Seedream endpoint (default: `https://ark.cn-beijing.volces.com/api/v3`) |
| `<PROVIDER>_IMAGE_MODEL` | Per-provider model override (`OPENAI_IMAGE_MODEL`, `GOOGLE_IMAGE_MODEL`, `DASHSCOPE_IMAGE_MODEL`, `ZAI_IMAGE_MODEL`/`BIGMODEL_IMAGE_MODEL`, `MINIMAX_IMAGE_MODEL`, `OPENROUTER_IMAGE_MODEL`, `REPLICATE_IMAGE_MODEL`, `JIMENG_IMAGE_MODEL`, `SEEDREAM_IMAGE_MODEL`) |
| `AZURE_OPENAI_DEPLOYMENT` (alias `AZURE_OPENAI_IMAGE_MODEL`) | Azure default deployment |
| `<PROVIDER>_BASE_URL` | Per-provider endpoint override |
| `AZURE_API_VERSION` | Azure image API version (default `2025-04-01-preview`) |
| `JIMENG_REGION` | Jimeng region (default `cn-north-1`) |
| `OPENAI_IMAGE_API_DIALECT` | `openai-native` \| `ratio-metadata` |
| `OPENROUTER_HTTP_REFERER`, `OPENROUTER_TITLE` | Optional OpenRouter attribution |
| `BAOYU_IMAGE_GEN_MAX_WORKERS` | Override batch worker cap |
| `BAOYU_IMAGE_GEN_<PROVIDER>_CONCURRENCY` | Override provider concurrency, e.g. `BAOYU_IMAGE_GEN_REPLICATE_CONCURRENCY` |
| `BAOYU_IMAGE_GEN_<PROVIDER>_START_INTERVAL_MS` | Override provider start gap, e.g. `BAOYU_IMAGE_GEN_REPLICATE_START_INTERVAL_MS` |
| `BAOYU_IMAGE_GEN_<PROVIDER>_CONCURRENCY` | Per-provider concurrency (e.g., `BAOYU_IMAGE_GEN_REPLICATE_CONCURRENCY`) |
| `BAOYU_IMAGE_GEN_<PROVIDER>_START_INTERVAL_MS` | Per-provider start-gap |
**Load Priority**: CLI args > EXTEND.md > env vars > `<cwd>/.baoyu-skills/.env` > `~/.baoyu-skills/.env`
**Load priority**: CLI args > EXTEND.md > env vars > `<cwd>/.baoyu-skills/.env` > `~/.baoyu-skills/.env`
## Model Resolution
Model priority (highest → lowest), applies to all providers:
Priority (highest → lowest) applies to every provider:
1. CLI flag: `--model <id>`
2. EXTEND.md: `default_model.[provider]`
3. Env var: `<PROVIDER>_IMAGE_MODEL` (e.g., `GOOGLE_IMAGE_MODEL`)
1. CLI flag `--model <id>`
2. EXTEND.md `default_model.[provider]`
3. Env var `<PROVIDER>_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.
For Azure, `--model` / `default_model.azure` is the Azure deployment name. `AZURE_OPENAI_DEPLOYMENT` is the preferred env var; `AZURE_OPENAI_IMAGE_MODEL` is kept 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.
EXTEND.md overrides env vars: if EXTEND.md sets `default_model.google: "gemini-3-pro-image-preview"` and the env var sets `GOOGLE_IMAGE_MODEL=gemini-3.1-flash-image-preview`, EXTEND.md wins.
### OpenAI-Compatible Gateway Dialects
**Display model info before each generation**:
`provider=openai` means the auth and routing entrypoint is OpenAI-compatible. It does **not** guarantee that the upstream image API uses OpenAI native image-request semantics.
- `Using [provider] / [model]`
- `Switch model: --model <id> | EXTEND.md default_model.[provider] | env <PROVIDER>_IMAGE_MODEL`
Use `default_image_api_dialect` in `EXTEND.md`, `OPENAI_IMAGE_API_DIALECT`, or `--imageApiDialect` when the endpoint expects a different wire format:
## OpenAI-Compatible Gateway Dialects
- `openai-native`: Sends pixel `size` such as `1536x1024` and native OpenAI quality fields when supported
- `ratio-metadata`: Sends aspect-ratio `size` such as `16:9` and maps quality/size intent into `metadata.resolution` (`1K|2K|4K`) plus `metadata.orientation`
`provider=openai` means the auth and routing entrypoint is OpenAI-compatible. It does **not** guarantee the upstream image API uses OpenAI native semantics. When a gateway expects a different wire format, set `default_image_api_dialect` in EXTEND.md, `OPENAI_IMAGE_API_DIALECT`, or `--imageApiDialect`:
Recommended use:
- `openai-native`: pixel `size` (`1536x1024`) and native OpenAI quality fields
- `ratio-metadata`: aspect-ratio `size` (`16:9`) plus `metadata.resolution` (`1K|2K|4K`) and `metadata.orientation`
- OpenAI native Images API or strict clones: keep `openai-native`
- OpenAI-compatible gateways in front of Gemini or similar models: try `ratio-metadata`
Use `openai-native` for the OpenAI native API or strict clones; try `ratio-metadata` for compatibility gateways in front of Gemini or similar models. Current limitation: `ratio-metadata` applies only to text-to-image; reference-image edits still need `openai-native` or a provider with first-class edit support.
Current limitation: `ratio-metadata` only applies to text-to-image generation. Reference-image edit flows still require `openai-native` or another provider with first-class edit support.
## Provider-Specific Guides
**Agent MUST display model info** before each generation:
- Show: `Using [provider] / [model]`
- Show switch hint: `Switch model: --model <id> | EXTEND.md default_model.[provider] | env <PROVIDER>_IMAGE_MODEL`
Each provider has its own quirks (model families, size rules, ref support, limits). Read these when the user picks that provider or asks for non-default behavior:
### DashScope Models
Use `--model qwen-image-2.0-pro` or set `default_model.dashscope` / `DASHSCOPE_IMAGE_MODEL` when the user wants official Qwen-Image behavior.
Official DashScope model families:
- `qwen-image-2.0-pro`, `qwen-image-2.0-pro-2026-03-03`, `qwen-image-2.0`, `qwen-image-2.0-2026-03-03`
- Free-form `size` in `宽*高` format
- Total pixels must stay between `512*512` and `2048*2048`
- Default size is approximately `1024*1024`
- Best choice for custom ratios such as `21:9` and text-heavy Chinese/English layouts
- `qwen-image-max`, `qwen-image-max-2025-12-30`, `qwen-image-plus`, `qwen-image-plus-2026-01-09`, `qwen-image`
- Fixed sizes only: `1664*928`, `1472*1104`, `1328*1328`, `1104*1472`, `928*1664`
- Default size is `1664*928`
- `qwen-image` currently has the same capability as `qwen-image-plus`
- Legacy DashScope models such as `z-image-turbo`, `z-image-ultra`, `wanx-v1`
- Keep using them only when the user explicitly asks for legacy behavior or compatibility
When translating CLI args into DashScope behavior:
- `--size` wins over `--ar`
- For `qwen-image-2.0*`, prefer explicit `--size`; otherwise infer from `--ar` and use the official recommended resolutions below
- For `qwen-image-max/plus/image`, only use the five official fixed sizes; if the requested ratio is not covered, switch to `qwen-image-2.0-pro`
- `--quality` is a baoyu-imagine compatibility preset, not a native DashScope API field. Mapping `normal` / `2k` onto the `qwen-image-2.0*` table below is an implementation inference, not an official API guarantee
Recommended `qwen-image-2.0*` sizes for common aspect ratios:
| Ratio | `normal` | `2k` |
|-------|----------|------|
| `1:1` | `1024*1024` | `1536*1536` |
| `2:3` | `768*1152` | `1024*1536` |
| `3:2` | `1152*768` | `1536*1024` |
| `3:4` | `960*1280` | `1080*1440` |
| `4:3` | `1280*960` | `1440*1080` |
| `9:16` | `720*1280` | `1080*1920` |
| `16:9` | `1280*720` | `1920*1080` |
| `21:9` | `1344*576` | `2048*872` |
DashScope official APIs also expose `negative_prompt`, `prompt_extend`, and `watermark`, but `baoyu-imagine` does not expose them as dedicated CLI flags today.
Official references:
- [Qwen-Image API](https://help.aliyun.com/zh/model-studio/qwen-image-api)
- [Text-to-image guide](https://help.aliyun.com/zh/model-studio/text-to-image)
- [Qwen-Image Edit API](https://help.aliyun.com/zh/model-studio/qwen-image-edit-api)
### Z.AI Models
Use `--model glm-image` or set `default_model.zai` / `ZAI_IMAGE_MODEL` when the user wants GLM-image output.
Official Z.AI image model options currently documented in the sync image API:
- `glm-image` (recommended default)
- Text-to-image only in `baoyu-imagine`
- Native `quality` options are `hd` and `standard`; this skill maps `2k -> hd` and `normal -> standard`
- Recommended sizes: `1280x1280`, `1568x1056`, `1056x1568`, `1472x1088`, `1088x1472`, `1728x960`, `960x1728`
- Custom `--size` requires width and height between `1024` and `2048`, divisible by `32`, with total pixels <= `2^22`
- `cogview-4-250304`
- Legacy Z.AI image model family exposed by the same endpoint
- Custom `--size` requires width and height between `512` and `2048`, divisible by `16`, with total pixels <= `2^21`
Notes:
- The official sync API returns a temporary image URL; `baoyu-imagine` downloads that URL and writes the image locally
- `--ref` is not supported for Z.AI in this skill yet
- The sync API currently returns a single image, so `--n > 1` is rejected
Official references:
- [GLM-Image Guide](https://docs.z.ai/guides/image/glm-image)
- [Generate Image API](https://docs.z.ai/api-reference/image/generate-image)
### MiniMax Models
Use `--model image-01` or set `default_model.minimax` / `MINIMAX_IMAGE_MODEL` when the user wants MiniMax image generation.
Official MiniMax image model options currently documented in the API reference:
- `image-01` (recommended default)
- Supports text-to-image and subject-reference image generation
- Supports official `aspect_ratio` values: `1:1`, `16:9`, `4:3`, `3:2`, `2:3`, `3:4`, `9:16`, `21:9`
- Supports documented custom `width` / `height` output sizes when using `--size <WxH>`
- `width` and `height` must both be between `512` and `2048`, and both must be divisible by `8`
- `image-01-live`
- Lower-latency variant
- Use `--ar` for sizing; MiniMax documents custom `width` / `height` as only effective for `image-01`
MiniMax subject reference notes:
- `--ref` files are sent as MiniMax `subject_reference`
- MiniMax docs currently describe `subject_reference[].type` as `character`
- Official docs say `image_file` supports public URLs or Base64 Data URLs; `baoyu-imagine` sends local refs as Data URLs
- Official docs recommend front-facing portrait references in JPG/JPEG/PNG under 10MB
Official references:
- [MiniMax Image Generation Guide](https://platform.minimax.io/docs/guides/image-generation)
- [MiniMax Text-to-Image API](https://platform.minimax.io/docs/api-reference/image-generation-t2i)
- [MiniMax Image-to-Image API](https://platform.minimax.io/docs/api-reference/image-generation-i2i)
### OpenRouter Models
Use full OpenRouter model IDs, e.g.:
- `google/gemini-3.1-flash-image-preview` (recommended, supports image output and reference-image workflows)
- `google/gemini-2.5-flash-image-preview`
- `black-forest-labs/flux.2-pro`
- Other OpenRouter image-capable model IDs
Notes:
- OpenRouter image generation uses `/chat/completions`, not the OpenAI `/images` endpoints
- If `--ref` is used, choose a multimodal model that supports image input and image output
- `--imageSize` maps to OpenRouter `imageGenerationOptions.size`; `--size <WxH>` is converted to the nearest OpenRouter size and inferred aspect ratio when possible
### Replicate Models
Replicate support in `baoyu-imagine` is intentionally scoped to the model families that the tool can validate locally and save without dropping outputs:
- `google/nano-banana*` (default: `google/nano-banana-2`)
- Supports prompt-only and reference-image generation
- Uses Replicate `aspect_ratio`, `resolution`, and `output_format`
- `--size <WxH>` is accepted only as a shorthand for a documented aspect ratio plus `1K` / `2K`
- `bytedance/seedream-4.5`
- Supports prompt-only and reference-image generation
- Uses Replicate `size`, `aspect_ratio`, and `image_input`
- Local validation blocks unsupported `1K` requests before the API call
- `bytedance/seedream-5-lite`
- Supports prompt-only and reference-image generation
- Uses Replicate `size`, `aspect_ratio`, and `image_input`
- Local validation currently accepts `2K` / `3K` only
- `wan-video/wan-2.7-image`
- Supports prompt-only and reference-image generation
- Uses Replicate `size` and `images`
- Max output size is 2K
- `wan-video/wan-2.7-image-pro`
- Supports prompt-only and reference-image generation
- Uses Replicate `size` and `images`
- 4K is allowed only for text-to-image; local validation blocks `4K + --ref`
Guardrails:
- Replicate currently supports only single-output save semantics in this tool. Keep `--n 1`.
- If a Replicate model is outside the compatibility list above, `baoyu-imagine` only treats it as prompt-only and rejects advanced local options instead of guessing a nano-banana-style schema.
Examples:
```bash
# Use Replicate default model
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider replicate
# Override model explicitly
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider replicate --model google/nano-banana
```
| Provider | Reference |
|----------|-----------|
| DashScope (Qwen-Image families, custom sizes) | `references/providers/dashscope.md` |
| Z.AI (GLM-Image, cogview-4) | `references/providers/zai.md` |
| MiniMax (image-01, subject-reference) | `references/providers/minimax.md` |
| OpenRouter (multimodal models, `/chat/completions` flow) | `references/providers/openrouter.md` |
| Replicate (nano-banana, Seedream, Wan) | `references/providers/replicate.md` |
## Provider Selection
1. `--ref` provided + no `--provider` → auto-select Google first, then OpenAI, then Azure, then OpenRouter, then Replicate, then Seedream, then MiniMax (MiniMax subject reference is more specialized toward character/portrait consistency)
2. `--provider` specified → use it (if `--ref`, must be `google`, `openai`, `azure`, `openrouter`, `replicate`, `seedream`, or `minimax`)
3. Only one API key available → use that provider
4. Multiple available → default to Google, then OpenAI, Azure, OpenRouter, DashScope, Z.AI, MiniMax, Replicate, Jimeng, Seedream
1. `--ref` provided + no `--provider` → auto-select Google → OpenAI → Azure → OpenRouter → Replicate → Seedream → MiniMax (MiniMax's subject reference is more specialized toward character/portrait consistency)
2. `--provider` specified → use it (if `--ref`, must be google/openai/azure/openrouter/replicate/seedream/minimax)
3. Only one API key present → use that provider
4. Multiple keys → default priority: Google → OpenAI → Azure → OpenRouter → DashScope → Z.AI → MiniMax → Replicate → Jimeng → Seedream
## Quality Presets
| Preset | Google imageSize | OpenAI Size | OpenRouter size | Replicate resolution | Use Case |
| Preset | Google imageSize | OpenAI size | OpenRouter size | Replicate resolution | Use case |
|--------|------------------|-------------|-----------------|----------------------|----------|
| `normal` | 1K | 1024px | 1K | 1K | Quick previews |
| `2k` (default) | 2K | 2048px | 2K | 2K | Covers, illustrations, infographics |
**Google/OpenRouter imageSize**: Can be overridden with `--imageSize 1K|2K|4K`
Google/OpenRouter `imageSize` can be overridden with `--imageSize 1K|2K|4K`.
## Aspect Ratios
Supported: `1:1`, `16:9`, `9:16`, `4:3`, `3:4`, `2.35:1`
Supported: `1:1`, `16:9`, `9:16`, `4:3`, `3:4`, `2.35:1`.
- Google multimodal: uses `imageConfig.aspectRatio`
- OpenAI: maps to closest supported size
- OpenRouter: sends `imageGenerationOptions.aspect_ratio`; if only `--size <WxH>` is given, aspect ratio is inferred automatically
- Replicate: behavior is model-family-specific. `google/nano-banana*` uses `aspect_ratio`; `bytedance/seedream-*` uses documented Replicate aspect ratios; Wan 2.7 maps `--ar` to a concrete `size`
- MiniMax: sends official `aspect_ratio` values directly; if `--size <WxH>` is given without `--ar`, `width` / `height` are sent for `image-01`
- Google multimodal: `imageConfig.aspectRatio`
- OpenAI: closest supported size
- OpenRouter: `imageGenerationOptions.aspect_ratio`; if only `--size <WxH>` is given, the ratio is inferred
- Replicate: behavior is model-specific — `google/nano-banana*` uses `aspect_ratio`, `bytedance/seedream-*` uses documented Replicate ratios, Wan 2.7 maps `--ar` to a concrete `size`
- MiniMax: official `aspect_ratio` values; if `--size <WxH>` is given without `--ar`, sends `width`/`height` for `image-01`
## Generation Mode
**Default**: Sequential generation.
**Default**: sequential. **Batch parallel**: enabled automatically when `--batchfile` contains 2+ pending tasks.
**Batch Parallel Generation**: When `--batchfile` contains 2 or more pending tasks, the script automatically enables parallel generation.
| Situation | Prefer | Why |
|-----------|--------|-----|
| One image, or 1-2 simple images | Sequential | Lower coordination overhead, easier debugging |
| Multiple images with saved prompt files | Batch (`--batchfile`) | Reuses finalized prompts, applies shared throttling/retries, predictable throughput |
| Each image still needs its own reasoning / prompt writing / style exploration | Subagents | Work is still exploratory, each needs independent analysis |
| Input is `outline.md` + `prompts/` (e.g. from `baoyu-article-illustrator`) | Batch — use `scripts/build-batch.ts` to assemble the payload | The outline + prompt files already contain everything needed |
| Mode | When to Use |
|------|-------------|
| Sequential (default) | Normal usage, single images, small batches |
| Parallel batch | Batch mode with 2+ tasks |
Rule of thumb: once prompt files are saved and the task is "generate all of these", prefer batch over subagents. Use subagents only when generation is coupled with per-image thinking or divergent creative exploration.
Execution choice:
| Situation | Preferred approach | Why |
|-----------|--------------------|-----|
| One image, or 1-2 simple images | Sequential | Lower coordination overhead and easier debugging |
| Multiple images already have saved prompt files | Batch (`--batchfile`) | Reuses finalized prompts, applies shared throttling/retries, and gives predictable throughput |
| Each image still needs separate reasoning, prompt writing, or style exploration | Subagents | The work is still exploratory, so each image may need independent analysis before generation |
| Output comes from `baoyu-article-illustrator` with `outline.md` + `prompts/` | Batch (`build-batch.ts` -> `--batchfile`) | That workflow already produces prompt files, so direct batch execution is the intended path |
Rule of thumb:
- Prefer batch over subagents once prompt files are already saved and the task is "generate all of these"
- Use subagents only when generation is coupled with per-image thinking, rewriting, or divergent creative exploration
Parallel behavior:
**Parallel behavior**:
- Default worker count is automatic, capped by config, built-in default 10
- Provider-specific throttling is applied only in batch mode, and the built-in defaults are tuned for faster throughput while still avoiding obvious RPM bursts
- You can override worker count with `--jobs <count>`
- Each image retries automatically up to 3 attempts
- Provider-specific throttling applies only in batch mode; defaults are tuned for throughput while avoiding RPM bursts
- Override with `--jobs <count>`
- Each image retries up to 3 attempts
- Final output includes success count, failure count, and per-image failure reasons
## Error Handling
@@ -485,6 +212,19 @@ Parallel behavior:
- Invalid aspect ratio → warning, proceed with default
- Reference images with unsupported provider/model → error with fix hint
## References
| File | Content |
|------|---------|
| `references/usage-examples.md` | Extended CLI examples across providers and batch mode |
| `references/providers/dashscope.md` | DashScope families, sizes, limits |
| `references/providers/zai.md` | Z.AI GLM-image / cogview-4 |
| `references/providers/minimax.md` | MiniMax image-01 + subject reference |
| `references/providers/openrouter.md` | OpenRouter multimodal flow |
| `references/providers/replicate.md` | Replicate supported families + guardrails |
| `references/config/preferences-schema.md` | EXTEND.md schema |
| `references/config/first-time-setup.md` | First-time setup flow |
## Extension Support
Custom configurations via EXTEND.md. See **Preferences** section for paths and supported options.
Custom configurations via EXTEND.md. See Step 0 for paths and schema.
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# DashScope (阿里通义万象)
Read when the user picks `--provider dashscope`, sets `default_model.dashscope`, or asks for Qwen-Image behavior. The SKILL.md only names the default — this file covers model families, sizing rules, and limits.
## Model Families
**`qwen-image-2.0*`** — recommended modern family. Members: `qwen-image-2.0-pro`, `qwen-image-2.0-pro-2026-03-03`, `qwen-image-2.0`, `qwen-image-2.0-2026-03-03`.
- Free-form `size` in `宽*高` format
- Total pixels must be between `512*512` and `2048*2048`
- Default ≈ `1024*1024`
- Best choice for custom ratios (e.g. `21:9`) and text-heavy Chinese/English layouts
**Fixed-size family** — `qwen-image-max`, `qwen-image-max-2025-12-30`, `qwen-image-plus`, `qwen-image-plus-2026-01-09`, `qwen-image`.
- Only five sizes allowed: `1664*928`, `1472*1104`, `1328*1328`, `1104*1472`, `928*1664`
- Default is `1664*928`
- `qwen-image` currently has the same capability as `qwen-image-plus`
**Legacy** — `z-image-turbo`, `z-image-ultra`, `wanx-v1`. Only use when the user explicitly asks for legacy behavior.
## Size Resolution
- `--size` wins over `--ar`
- For `qwen-image-2.0*`: prefer explicit `--size`; otherwise infer from `--ar` using the recommended table below
- For `qwen-image-max/plus/image`: only use the five fixed sizes; if the requested ratio doesn't fit, switch to `qwen-image-2.0-pro`
- `--quality` is a baoyu-imagine preset, not an official DashScope field. The mapping of `normal`/`2k` onto the `qwen-image-2.0*` table is an implementation choice, not an API guarantee
### Recommended `qwen-image-2.0*` sizes
| Ratio | `normal` | `2k` |
|-------|----------|------|
| `1:1` | `1024*1024` | `1536*1536` |
| `2:3` | `768*1152` | `1024*1536` |
| `3:2` | `1152*768` | `1536*1024` |
| `3:4` | `960*1280` | `1080*1440` |
| `4:3` | `1280*960` | `1440*1080` |
| `9:16` | `720*1280` | `1080*1920` |
| `16:9` | `1280*720` | `1920*1080` |
| `21:9` | `1344*576` | `2048*872` |
## Not Exposed
DashScope APIs also support `negative_prompt`, `prompt_extend`, and `watermark`, but `baoyu-imagine` does not expose them as CLI flags today.
## Official References
- [Qwen-Image API](https://help.aliyun.com/zh/model-studio/qwen-image-api)
- [Text-to-image guide](https://help.aliyun.com/zh/model-studio/text-to-image)
- [Qwen-Image Edit API](https://help.aliyun.com/zh/model-studio/qwen-image-edit-api)
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# MiniMax
Read when the user picks `--provider minimax` or sets `default_model.minimax`. Default model is `image-01`.
## Models
**`image-01`** (recommended default)
- Supports text-to-image and subject-reference image generation
- Supports official `aspect_ratio` values: `1:1`, `16:9`, `4:3`, `3:2`, `2:3`, `3:4`, `9:16`, `21:9`
- Supports documented custom `width` / `height` via `--size <WxH>`
- Both width and height must be in `[512, 2048]` and divisible by `8`
**`image-01-live`** — lower-latency variant
- Use `--ar` for sizing; MiniMax documents custom `width`/`height` only for `image-01`
## Subject Reference
- `--ref` files are sent as MiniMax `subject_reference`
- `subject_reference[].type` is currently `character`
- Official docs say `image_file` supports public URLs or Base64 Data URLs; baoyu-imagine sends local refs as Data URLs
- Recommended refs: front-facing portraits, JPG/JPEG/PNG, under 10MB
## Official References
- [Image Generation Guide](https://platform.minimax.io/docs/guides/image-generation)
- [Text-to-Image API](https://platform.minimax.io/docs/api-reference/image-generation-t2i)
- [Image-to-Image API](https://platform.minimax.io/docs/api-reference/image-generation-i2i)
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# OpenRouter
Read when the user picks `--provider openrouter`. Default model is `google/gemini-3.1-flash-image-preview`.
## Common Models
Use full OpenRouter model IDs:
- `google/gemini-3.1-flash-image-preview` (recommended — supports image output and reference-image workflows)
- `google/gemini-2.5-flash-image-preview`
- `black-forest-labs/flux.2-pro`
- Any other OpenRouter image-capable model ID
## Behavior Notes
- OpenRouter image generation uses `/chat/completions`, not the OpenAI `/images` endpoints
- `--ref` requires a multimodal model that supports both image input and image output
- `--imageSize` maps to `imageGenerationOptions.size`
- `--size <WxH>` is converted to the nearest supported OpenRouter size, and the aspect ratio is inferred when possible
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# Replicate
Read when the user picks `--provider replicate`. Replicate support is intentionally scoped to model families baoyu-imagine can validate locally and save without dropping outputs.
## Supported Families
**`google/nano-banana*`** (default: `google/nano-banana-2`)
- Supports prompt-only and reference-image generation
- Uses Replicate `aspect_ratio`, `resolution`, and `output_format`
- `--size <WxH>` is accepted only as a shorthand for a documented `aspect_ratio` plus `1K` / `2K`
**`bytedance/seedream-4.5`**
- Supports prompt-only and reference-image generation
- Uses Replicate `size`, `aspect_ratio`, and `image_input`
- Local validation blocks unsupported `1K` requests before the API call
**`bytedance/seedream-5-lite`**
- Supports prompt-only and reference-image generation
- Uses Replicate `size`, `aspect_ratio`, and `image_input`
- Local validation currently accepts `2K` / `3K` only
**`wan-video/wan-2.7-image`**
- Supports prompt-only and reference-image generation
- Uses Replicate `size` and `images`
- Max output is 2K
**`wan-video/wan-2.7-image-pro`**
- Supports prompt-only and reference-image generation
- Uses Replicate `size` and `images`
- 4K is allowed only for text-to-image; local validation blocks `4K + --ref`
## Guardrails
- Replicate currently supports only single-output save semantics in this tool — keep `--n 1`
- If a model is outside the compatibility list above, baoyu-imagine treats it as prompt-only and rejects advanced local options instead of guessing a nano-banana-style schema
## Examples
```bash
# Default model
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider replicate
# Explicit model
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider replicate --model google/nano-banana
```
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# Z.AI GLM-Image
Read when the user picks `--provider zai` or sets `default_model.zai`. Default model is `glm-image`.
## Models
**`glm-image`** (recommended default)
- Text-to-image only in baoyu-imagine (no `--ref` support yet)
- Native `quality` options are `hd` and `standard`; this skill maps `2k → hd` and `normal → standard`
- Recommended sizes: `1280x1280`, `1568x1056`, `1056x1568`, `1472x1088`, `1088x1472`, `1728x960`, `960x1728`
- Custom `--size` requires width/height in `[1024, 2048]`, divisible by `32`, total pixels ≤ `2^22`
**`cogview-4-250304`** (legacy family, same endpoint)
- Custom `--size` requires width/height in `[512, 2048]`, divisible by `16`, total pixels ≤ `2^21`
## Behavior Notes
- The sync API returns a temporary URL; baoyu-imagine downloads it and writes locally
- `--ref` is not supported for Z.AI in this skill yet
- The sync API returns a single image, so `--n > 1` is rejected
## Official References
- [GLM-Image Guide](https://docs.z.ai/guides/image/glm-image)
- [Generate Image API](https://docs.z.ai/api-reference/image/generate-image)
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# Usage Examples
Extended CLI examples. SKILL.md shows the minimum set; read this file when the user asks about provider-specific invocation, batch generation, or less-common flags.
## Core Patterns
```bash
# Basic text-to-image
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image cat.png
# With aspect ratio
${BUN_X} {baseDir}/scripts/main.ts --prompt "A landscape" --image out.png --ar 16:9
# High quality
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --quality 2k
# Prompt from files
${BUN_X} {baseDir}/scripts/main.ts --promptfiles system.md content.md --image out.png
# With reference images (any provider family that supports refs)
${BUN_X} {baseDir}/scripts/main.ts --prompt "Make blue" --image out.png --ref source.png
```
## Per-Provider
```bash
# OpenAI
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider openai
# Azure OpenAI (model = deployment name)
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider azure --model gpt-image-1.5
# Google with explicit model
${BUN_X} {baseDir}/scripts/main.ts --prompt "Make blue" --image out.png --provider google --model gemini-3-pro-image-preview --ref source.png
# OpenRouter (recommended default)
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider openrouter
# OpenRouter with reference
${BUN_X} {baseDir}/scripts/main.ts --prompt "Make blue" --image out.png --provider openrouter --model google/gemini-3.1-flash-image-preview --ref source.png
# DashScope (default model)
${BUN_X} {baseDir}/scripts/main.ts --prompt "一只可爱的猫" --image out.png --provider dashscope
# DashScope Qwen-Image 2.0 Pro (custom size, Chinese text)
${BUN_X} {baseDir}/scripts/main.ts --prompt "为咖啡品牌设计一张 21:9 横幅海报，包含清晰中文标题" --image out.png --provider dashscope --model qwen-image-2.0-pro --size 2048x872
# DashScope legacy fixed-size
${BUN_X} {baseDir}/scripts/main.ts --prompt "一张电影感海报" --image out.png --provider dashscope --model qwen-image-max --size 1664x928
# Z.AI GLM-image
${BUN_X} {baseDir}/scripts/main.ts --prompt "一张带清晰中文标题的科技海报" --image out.png --provider zai
# Z.AI with custom size
${BUN_X} {baseDir}/scripts/main.ts --prompt "A science illustration with labels" --image out.png --provider zai --model glm-image --size 1472x1088
# MiniMax
${BUN_X} {baseDir}/scripts/main.ts --prompt "A fashion editorial portrait" --image out.jpg --provider minimax
# MiniMax with subject reference (character/portrait consistency)
${BUN_X} {baseDir}/scripts/main.ts --prompt "A girl by the library window" --image out.jpg --provider minimax --model image-01 --ref portrait.png --ar 16:9
# Replicate (default: google/nano-banana-2)
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider replicate
# Replicate Seedream 4.5
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cinematic portrait" --image out.png --provider replicate --model bytedance/seedream-4.5 --ar 3:2
# Replicate Wan 2.7 Image Pro
${BUN_X} {baseDir}/scripts/main.ts --prompt "A concept frame" --image out.png --provider replicate --model wan-video/wan-2.7-image-pro --size 2048x1152
```
## Batch Mode
```bash
# Batch from saved prompt files
${BUN_X} {baseDir}/scripts/main.ts --batchfile batch.json
# Batch with explicit worker count
${BUN_X} {baseDir}/scripts/main.ts --batchfile batch.json --jobs 4 --json
```
### Batch File Format
```json
{
"jobs": 4,
"tasks": [
{
"id": "hero",
"promptFiles": ["prompts/hero.md"],
"image": "out/hero.png",
"provider": "replicate",
"model": "google/nano-banana-2",
"ar": "16:9",
"quality": "2k"
},
{
"id": "diagram",
"promptFiles": ["prompts/diagram.md"],
"image": "out/diagram.png",
"ref": ["references/original.png"]
}
]
}
```
Paths in `promptFiles`, `image`, and `ref` are resolved relative to the batch file's directory. `jobs` is optional (overridden by CLI `--jobs`). A top-level array without the `jobs` wrapper is also accepted.
skills/baoyu-imagine/scripts/build-batch.test.ts
+190
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import assert from "node:assert/strict";
import fs from "node:fs/promises";
import os from "node:os";
import path from "node:path";
import { execFile } from "node:child_process";
import { promisify } from "node:util";
import test from "node:test";
const execFileAsync = promisify(execFile);
const repoRoot = path.resolve(import.meta.dirname, "..", "..", "..");
const scriptPath = path.join(repoRoot, "skills", "baoyu-imagine", "scripts", "build-batch.ts");
async function makeFixture(): Promise<{
root: string;
outlinePath: string;
promptsDir: string;
outputPath: string;
}> {
const root = await fs.mkdtemp(path.join(os.tmpdir(), "baoyu-imagine-build-batch-"));
const outlinePath = path.join(root, "outline.md");
const promptsDir = path.join(root, "prompts");
const outputPath = path.join(root, "batch.json");
await fs.mkdir(promptsDir, { recursive: true });
await fs.writeFile(
outlinePath,
`## Illustration 1
**Position**: demo
**Purpose**: demo
**Visual Content**: demo
**Filename**: 01-demo.png
`,
);
await fs.writeFile(path.join(promptsDir, "01-demo.md"), "A demo prompt\n");
return { root, outlinePath, promptsDir, outputPath };
}
async function runBuildBatch(args: string[]): Promise<void> {
await execFileAsync(process.execPath, ["--import", "tsx", scriptPath, ...args], {
cwd: repoRoot,
});
}
test("build-batch omits default model so baoyu-imagine can resolve env or EXTEND defaults", async () => {
const fixture = await makeFixture();
await runBuildBatch([
"--outline",
fixture.outlinePath,
"--prompts",
fixture.promptsDir,
"--output",
fixture.outputPath,
]);
const batch = JSON.parse(await fs.readFile(fixture.outputPath, "utf8")) as {
tasks: Array<Record<string, unknown>>;
};
assert.equal(batch.tasks.length, 1);
assert.equal(batch.tasks[0]?.provider, "replicate");
assert.equal(Object.hasOwn(batch.tasks[0]!, "model"), false);
});
test("build-batch preserves explicit model overrides", async () => {
const fixture = await makeFixture();
await runBuildBatch([
"--outline",
fixture.outlinePath,
"--prompts",
fixture.promptsDir,
"--output",
fixture.outputPath,
"--model",
"acme/custom-model",
]);
const batch = JSON.parse(await fs.readFile(fixture.outputPath, "utf8")) as {
tasks: Array<Record<string, unknown>>;
};
assert.equal(batch.tasks[0]?.model, "acme/custom-model");
});
test("build-batch propagates direct-usage references from prompt frontmatter", async () => {
const fixture = await makeFixture();
await fs.writeFile(
path.join(fixture.promptsDir, "01-demo.md"),
`---
illustration_id: 01
type: infographic
references:
- ref_id: 01
filename: 01-ref-brand.png
usage: direct
- ref_id: 02
filename: 02-ref-style.png
usage: style
---
A demo prompt
`,
);
await runBuildBatch([
"--outline",
fixture.outlinePath,
"--prompts",
fixture.promptsDir,
"--output",
fixture.outputPath,
]);
const batch = JSON.parse(await fs.readFile(fixture.outputPath, "utf8")) as {
tasks: Array<Record<string, unknown>>;
};
assert.deepEqual(batch.tasks[0]?.ref, ["references/01-ref-brand.png"]);
});
test("build-batch omits ref field when no direct references exist", async () => {
const fixture = await makeFixture();
await fs.writeFile(
path.join(fixture.promptsDir, "01-demo.md"),
`---
illustration_id: 01
references:
- ref_id: 01
filename: 01-ref-palette.png
usage: palette
---
A demo prompt
`,
);
await runBuildBatch([
"--outline",
fixture.outlinePath,
"--prompts",
fixture.promptsDir,
"--output",
fixture.outputPath,
]);
const batch = JSON.parse(await fs.readFile(fixture.outputPath, "utf8")) as {
tasks: Array<Record<string, unknown>>;
};
assert.equal(Object.hasOwn(batch.tasks[0]!, "ref"), false);
});
test("build-batch honors --refs-dir override", async () => {
const fixture = await makeFixture();
await fs.writeFile(
path.join(fixture.promptsDir, "01-demo.md"),
`---
illustration_id: 01
references:
- ref_id: 01
filename: brand.png
usage: direct
---
A demo prompt
`,
);
await runBuildBatch([
"--outline",
fixture.outlinePath,
"--prompts",
fixture.promptsDir,
"--output",
fixture.outputPath,
"--refs-dir",
"refs",
]);
const batch = JSON.parse(await fs.readFile(fixture.outputPath, "utf8")) as {
tasks: Array<Record<string, unknown>>;
};
assert.deepEqual(batch.tasks[0]?.ref, ["refs/brand.png"]);
});
skills/baoyu-imagine/scripts/build-batch.ts
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import path from "node:path";
import process from "node:process";
import { readdir, readFile, writeFile } from "node:fs/promises";
type CliArgs = {
outlinePath: string | null;
promptsDir: string | null;
outputPath: string | null;
imagesDir: string | null;
refsDir: string;
provider: string;
model: string | null;
aspectRatio: string;
quality: string;
jobs: number | null;
help: boolean;
};
type OutlineEntry = {
index: number;
filename: string;
};
type PromptReference = {
filename: string;
usage: "direct" | "style" | "palette";
};
function printUsage(): void {
console.log(`Usage:
npx -y tsx scripts/build-batch.ts --outline outline.md --prompts prompts --output batch.json --images-dir attachments
Options:
--outline <path> Path to outline.md
--prompts <path> Path to prompts directory
--output <path> Path to output batch.json
--images-dir <path> Directory for generated images
--refs-dir <path> Directory holding reference images, relative to batch file (default: references)
--provider <name> Provider for baoyu-imagine batch tasks (default: replicate)
--model <id> Explicit model for baoyu-imagine batch tasks (default: resolved by baoyu-imagine config/env)
--ar <ratio> Aspect ratio for all tasks (default: 16:9)
--quality <level> Quality for all tasks (default: 2k)
--jobs <count> Recommended worker count metadata (optional)
-h, --help Show help`);
}
function parseArgs(argv: string[]): CliArgs {
const args: CliArgs = {
outlinePath: null,
promptsDir: null,
outputPath: null,
imagesDir: null,
refsDir: "references",
provider: "replicate",
model: null,
aspectRatio: "16:9",
quality: "2k",
jobs: null,
help: false,
};
for (let i = 0; i < argv.length; i++) {
const current = argv[i]!;
if (current === "--outline") args.outlinePath = argv[++i] ?? null;
else if (current === "--prompts") args.promptsDir = argv[++i] ?? null;
else if (current === "--output") args.outputPath = argv[++i] ?? null;
else if (current === "--images-dir") args.imagesDir = argv[++i] ?? null;
else if (current === "--refs-dir") args.refsDir = argv[++i] ?? args.refsDir;
else if (current === "--provider") args.provider = argv[++i] ?? args.provider;
else if (current === "--model") args.model = argv[++i] ?? args.model;
else if (current === "--ar") args.aspectRatio = argv[++i] ?? args.aspectRatio;
else if (current === "--quality") args.quality = argv[++i] ?? args.quality;
else if (current === "--jobs") {
const value = argv[++i];
args.jobs = value ? parseInt(value, 10) : null;
} else if (current === "--help" || current === "-h") {
args.help = true;
}
}
return args;
}
function parsePromptReferences(content: string): PromptReference[] {
const fmMatch = content.match(/^---\s*\n([\s\S]*?)\n---\s*(?:\n|$)/);
if (!fmMatch) return [];
const lines = fmMatch[1]!.split(/\r?\n/);
const refs: PromptReference[] = [];
let current: Partial<PromptReference> | null = null;
let inReferences = false;
let listIndent = 0;
const flush = () => {
if (current?.filename) {
refs.push({
filename: current.filename,
usage: (current.usage ?? "direct") as PromptReference["usage"],
});
}
current = null;
};
const unquote = (raw: string): string => raw.trim().replace(/^["']|["']$/g, "");
for (const line of lines) {
if (!line.trim() || line.trim().startsWith("#")) continue;
const keyMatch = line.match(/^(\S[^:]*):\s*(.*)$/);
if (keyMatch) {
flush();
if (keyMatch[1] === "references") {
inReferences = true;
listIndent = 0;
continue;
}
inReferences = false;
continue;
}
if (!inReferences) continue;
const itemMatch = line.match(/^(\s*)-\s*(.*)$/);
if (itemMatch) {
flush();
listIndent = itemMatch[1]!.length;
current = {};
const rest = itemMatch[2]!.trim();
if (rest) {
const kv = rest.match(/^(\w+)\s*:\s*(.*)$/);
if (kv && (kv[1] === "filename" || kv[1] === "usage")) {
(current as Record<string, string>)[kv[1]] = unquote(kv[2]!);
}
}
continue;
}
const kvMatch = line.match(/^(\s+)(\w+)\s*:\s*(.*)$/);
if (kvMatch && kvMatch[1]!.length > listIndent && current) {
if (kvMatch[2] === "filename" || kvMatch[2] === "usage") {
(current as Record<string, string>)[kvMatch[2]!] = unquote(kvMatch[3]!);
}
}
}
flush();
return refs;
}
function parseOutline(content: string): OutlineEntry[] {
const entries: OutlineEntry[] = [];
const blocks = content.split(/^## Illustration\s+/m).slice(1);
for (const block of blocks) {
const indexMatch = block.match(/^(\d+)/);
const filenameMatch = block.match(/\*\*Filename\*\*:\s*(.+)/);
if (indexMatch && filenameMatch) {
entries.push({
index: parseInt(indexMatch[1]!, 10),
filename: filenameMatch[1]!.trim(),
});
}
}
return entries;
}
async function findPromptFile(promptsDir: string, entry: OutlineEntry): Promise<string | null> {
const files = await readdir(promptsDir);
const prefix = String(entry.index).padStart(2, "0");
const match = files.find((f) => f.startsWith(prefix) && f.endsWith(".md"));
return match ? path.join(promptsDir, match) : null;
}
async function main(): Promise<void> {
const args = parseArgs(process.argv.slice(2));
if (args.help) {
printUsage();
return;
}
if (!args.outlinePath) {
console.error("Error: --outline is required");
process.exit(1);
}
if (!args.promptsDir) {
console.error("Error: --prompts is required");
process.exit(1);
}
if (!args.outputPath) {
console.error("Error: --output is required");
process.exit(1);
}
const outlineContent = await readFile(args.outlinePath, "utf8");
const entries = parseOutline(outlineContent);
if (entries.length === 0) {
console.error("No illustration entries found in outline.");
process.exit(1);
}
const tasks = [];
for (const entry of entries) {
const promptFile = await findPromptFile(args.promptsDir, entry);
if (!promptFile) {
console.error(`Warning: No prompt file found for illustration ${entry.index}, skipping.`);
continue;
}
const imageDir = args.imagesDir ?? path.dirname(args.outputPath);
const promptContent = await readFile(promptFile, "utf8");
const refs = parsePromptReferences(promptContent)
.filter((r) => r.usage === "direct")
.map((r) => path.posix.join(args.refsDir, r.filename));
const task: Record<string, unknown> = {
id: `illustration-${String(entry.index).padStart(2, "0")}`,
promptFiles: [promptFile],
image: path.join(imageDir, entry.filename),
provider: args.provider,
ar: args.aspectRatio,
quality: args.quality,
};
if (args.model) task.model = args.model;
if (refs.length > 0) task.ref = refs;
tasks.push(task);
}
const output: Record<string, unknown> = { tasks };
if (args.jobs) output.jobs = args.jobs;
await writeFile(args.outputPath, JSON.stringify(output, null, 2) + "\n");
console.log(`Batch file written: ${args.outputPath} (${tasks.length} tasks)`);
}
main().catch((error) => {
console.error(error instanceof Error ? error.message : String(error));
process.exit(1);
});