Baoyu Article Illustrator

Article Illustrator

Analyze articles, identify illustration positions, generate images with Type × Style × Palette consistency.

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.

Image Generation Tools

When this skill needs to render an image, resolve the backend in this order:

1. Current-request override — if the user names a specific backend in the current message, use it.
2. Saved preference — if EXTEND.md sets preferred_image_backend to a backend available right now, use it.
3. Auto-select (when the preference is auto, unset, or the pinned backend isn't available):
   • Codex (imagegen) — first, inspect your available-skills / tool inventory. If a skill named imagegen is listed, you are running inside Codex and MUST use it: invoke via the Skill tool with skill: "imagegen", passing the saved prompt file's content (plus output path and aspect ratio per Codex imagegen's own args). Codex imagegen is the official raster backend in that runtime and outranks any non-native skill (e.g., baoyu-imagine) unless the user has explicitly pinned a different preferred_image_backend.
   • GPT-Image-2 (gpt-image-2) — if the Python script {output-dir}/gpt_image2_gen.py exists OR D:\data\images\Article-illustrations\gpt_image2_gen.py exists, use it via the Bash tool (see GPT-Image-2 Backend for full invocation details). This backend generates high-quality 16:9 images via apimart.ai API at ~$0.005/image with no watermark.
   • Other runtime-native tools — if the runtime exposes a different native image tool (e.g., Hermes image_generate), use it the same way.
   • Otherwise, if exactly one non-native backend is installed (e.g., baoyu-imagine), use it.
   • Otherwise (multiple non-native backends with no runtime-native tool), ask the user once — batch with any other initial questions.
4. If none are available, tell the user and ask how to proceed.

⛔ Never substitute SVG, HTML, canvas, or other code-based rendering for raster image generation. Codex imagegen's own description says it should be used "when the output should be a bitmap asset rather than repo-native code or vector." If you cannot resolve a raster backend via step 3, fall through to step 4 and ask the user — do not silently emit SVG, write inline <svg> markup, or produce HTML/CSS art as a substitute. This applies even if the article/section seems "diagram-like": the consumer skill calling this rule has already decided that a raster image is what it needs.

Setting preferred_image_backend: ask forces the step-3 prompt every run regardless of available backends. Users change the pinned backend via the ## Changing Preferences section below.

Prompt file requirement (hard): write each image's full, final prompt to a standalone file under prompts/ (naming: NN-{type}-[slug].md) BEFORE invoking any backend. The backend receives the prompt file (or its content); the file is the reproducibility record and lets you switch backends without regenerating prompts.

Concrete tool names (imagegen, image_generate, baoyu-imagine) above are examples — substitute the local equivalents under the same rule.

Batch Generation Policy

After every prompt file for the run has been saved and verified, generate images in batches by default.

Priority order:

1. Use the chosen backend's native batch / multi-task interface if it exists. Each task must keep its own prompt file, output path, aspect ratio, and direct reference images.
2. If no native batch interface exists but the runtime can issue parallel tool calls, dispatch up to generation_batch_size images at a time. Default: 4. An explicit user request in the current message, such as --batch-size 4 or "并行4张一起生成", overrides EXTEND.md.
3. If neither native batch nor parallel tool calls are available, generate sequentially.

Rules:

• Never start the first batch until all prompt files for that batch exist on disk.
• Retry failed items once without regenerating successful items.
• Do not use subagents merely to parallelize image rendering. Use subagents only for separate prompt iteration or creative exploration.

Confirmation Policy

Default behavior: confirm before generation.

• Treat explicit skill invocation, a file path, matched signals/presets, and EXTEND.md defaults as recommendation inputs only. None of them authorizes skipping confirmation.
• Do not start Step 4 or later until the user completes Step 3.
• Skip confirmation only when the current request explicitly says to do so, for example: "直接生成", "不用确认", "跳过确认", "按默认出图", or equivalent wording.
• If confirmation is skipped explicitly, state the assumed type / density / style / palette / language / backend in the next user-facing update before generating.

Reference Images

Users may supply reference images via --ref <files...> or by providing file paths / pasting images in conversation. Refs guide style, palette, composition, or subject for specific illustrations.

Full detection, storage, and processing rules are in references/workflow.md (Step 1.0 saves to references/NN-ref-{slug}.{ext}; Step 5.3 processes per-illustration usage direct | style | palette). When the chosen backend supports batch input, direct-usage entries in each prompt file's references: frontmatter should be propagated into its batch payload so backends can pass them through (e.g. baoyu-imagine accepts ref per task).

Three Dimensions

Combine freely: --type infographic --style vector-illustration --palette macaron

Or use presets: --preset edu-visual → type + style + palette in one flag. See Style Presets.

Types

Styles

See references/styles.md for Core Styles, full gallery, and Type × Style compatibility.

Workflow

- [ ] Step 1: Pre-check (EXTEND.md, references, config)
- [ ] Step 2: Analyze content
- [ ] Step 3: Confirm settings (AskUserQuestion)
- [ ] Step 4: Generate outline
- [ ] Step 5: Generate images
- [ ] Step 6: Finalize

Step 1: Pre-check

1.5 Load Preferences (EXTEND.md) ⛔ BLOCKING

Check EXTEND.md in priority order — the first one found wins:

Full procedures: references/workflow.md

Step 2: Analyze

CRITICAL: Metaphors → visualize underlying concept, NOT literal image.

Full procedures: references/workflow.md

Step 3: Confirm Settings ⚠️

Hard gate: this step is mandatory per the Confirmation Policy — Steps 4+ cannot start until the user confirms here (or explicitly opts out with "直接生成" / equivalent wording in the current request).

ONE AskUserQuestion, max 4 Qs. Q1-Q2 REQUIRED. Q3 required unless preset chosen.

Full procedures: references/workflow.md

Step 4: Generate Outline

Save outline.md with frontmatter (type, density, style, palette, image_count) and entries:

## Illustration 1
**Position**: [section/paragraph]
**Purpose**: [why]
**Visual Content**: [what]
**Filename**: 01-infographic-concept-name.png

Full template: references/workflow.md

Step 5: Generate Images

⛔ BLOCKING: Prompt files MUST be saved before ANY image generation. This is a hard requirement regardless of which backend is chosen — the prompt file is the reproducibility record.

1. For each illustration, create a prompt file per references/prompt-construction.md
2. Save to prompts/NN-{type}-{slug}.md with YAML frontmatter
3. Prompts MUST use type-specific templates with structured sections (ZONES / LABELS / COLORS / STYLE / ASPECT)
4. LABELS MUST include article-specific data: actual numbers, terms, metrics, quotes
5. DO NOT pass ad-hoc inline prompts to --prompt without saving prompt files first
6. Select the backend via the ## Image Generation Tools rule at the top: use whatever is available; if multiple, ask the user once. Do this once per session before any generation.
7. Execution strategy: Generate in batches per the ## Batch Generation Policy: backend native batch first, runtime parallel tool calls second, sequential only as fallback. Default batch size is 4 unless EXTEND.md or the current request overrides it.
8. Process references (direct/style/palette) per prompt frontmatter
9. Apply watermark if EXTEND.md enabled
10. Generate from saved prompt files; retry once on failure

Full procedures: references/workflow.md

Step 6: Finalize

Insert ![description]({relative-path}/NN-{type}-{slug}.png) after paragraphs. Path computed relative to article file based on output directory setting.

Step 7: Upload to PicGo & Replace URLs (Conditional)

⚡ Only when EXTEND.md has picgo_upload.enabled: true and PicGo Server is reachable.

This step converts local image paths into online URLs so the article works everywhere — not just on your machine.

Prerequisites:

• PicGo GUI must be running with Server enabled (default http://127.0.0.1:36677)
• picgo_upload.server_url in EXTEND.md points to the correct address

Workflow:

For each generated image:
  1️⃣  POST image file to {server_url}/upload (multipart/form-data, field name: "file")
  2️⃣  Parse response JSON → extract URL from result[0] or success[0].url
  3️⃣  Verify URL starts with expected_url_prefix (if set)
  4️⃣  In the article markdown, replace the local path with the online URL
  5️⃣  Record mapping in output-dir/upload-log.json

Upload API Details (PicGo Server v1):

# Single upload
curl -X POST http://127.0.0.1:36677/upload \
  -F "file=@/path/to/image.png"

# Response format
{
  "success": true,
  "result": [
    {
      "picName": "image.png",
      "url": "https://gitee.com/da-qiang-classmate/typora/raw/master/image/xxxxx.png"
    }
  ]
}

Error Handling:

Upload Log Format (output-dir/upload-log.json):

{
  "timestamp": "2026-05-16T15:30:00+08:00",
  "article": "path/to/article.md",
  "mappings": [
    {
      "local_path": "01-infographic-speed-trap.png",
      "local_full": "D:\\data\\images\\Article-illustrations\\01-infographic-speed-trap.png",
      "online_url": "https://gitee.com/da-qiang-classmate/typora/raw/master/image/xxx.png",
      "uploaded_at": "2026-05-16T15:30:01+08:00"
    }
  ]
}

Article Markdown Replacement Rule:

After successful upload, find and replace in the article file:

Pattern:  !\[([^\]]*)\]({local-relative-path}/{filename})
Replace: ![$1]({online-url-from-picgo})

The replacement uses exact string match (not regex) on the path inserted in Step 6.

Step 7 Complete!
Uploaded: X/Y images → Gitee ({expected_url_prefix})
Article links updated: {article-path}
Log saved: {output-dir}/upload-log.json

If picgo_upload.enabled is false or missing, skip this step entirely and output a reminder:

💡 PicGo upload not enabled. Images are local only. Enable it in EXTEND.md (picgo_upload.enabled: true) to auto-upload after generation.

Output Directory

Output directory is determined by default_output_dir in EXTEND.md (set during first-time setup):

All auxiliary files (outline, prompts) are saved inside the output directory:

{output-dir}/
├── outline.md
├── prompts/
│   └── NN-{type}-{slug}.md
└── NN-{type}-{slug}.png

When input is pasted content (no file path), always uses illustrations/{topic-slug}/ with source-{slug}.{ext} saved alongside.

Slug: 2-4 words, kebab-case. Conflict: append -YYYYMMDD-HHMMSS.

Modification

References

Changing Preferences

EXTEND.md lives at the first matching path listed in Step 1.5. Three ways to change it:

• Edit directly — open EXTEND.md and change fields. Full schema: references/config/preferences-schema.md.
• Reconfigure interactively — delete EXTEND.md (or ask "reconfigure baoyu-article-illustrator preferences" / "重新配置"). The next run re-triggers first-time setup.
• Common one-line edits:
  • preferred_image_backend: auto — default; runtime-native tool wins, falls back to the only installed backend, asks only if multiple non-native are present.
  • preferred_image_backend: gpt-image-2 — pin to GPT-Image-2 via apimart.ai (no watermark, ~$0.005/image).
  • preferred_image_backend: codex-imagegen — pin to Codex's built-in.
  • preferred_image_backend: baoyu-imagine — pin to the baoyu-imagine skill.
  • preferred_image_backend: ask — confirm backend every run.
  • generation_batch_size: 4 — default number of images to render concurrently when the runtime supports parallel generation calls.
  • preferred_type: infographic, preferred_style: notion, preferred_palette: macaron, language: zh.
  • default_output_dir: imgs-subdir — where to write generated images relative to the article.