Mmcp.market

baoyu-article-illustrator skill

by guanyang·guanyang/open-agent-hub·973 stars·MIT

Analyzes article structure, identifies positions requiring visual aids, generates illustrations with Type × Style × Palette three-dimension approach. Use when user asks to "illustrate article", "add images", "generate images for article", or "为文章配图".

A100/100content scan

Is the baoyu-article-illustrator skill safe?

Clean: nothing in its files matched our rules. We read 37 files in the folder on 2026-09-28.

No findings.

Install the baoyu-article-illustrator skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description.

git clone --depth 1 https://github.com/guanyang/open-agent-hub.git /tmp/open-agent-hub
mkdir -p ~/.claude/skills
cp -r /tmp/open-agent-hub/skills/baoyu-article-illustrator ~/.claude/skills/baoyu-article-illustrator
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

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, requestuserinput, 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 preferredimagebackend 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-image-gen) unless the user has explicitly pinned a different preferredimagebackend.
  • Codex via codex exec (codex-imagegen) — if the current runtime exposes no native imagegen skill but the codex CLI is on PATH with an active codex login, route through baoyu-image-gen --provider codex-cli (preferred), or — if baoyu-image-gen is unavailable — invoke the bundled wrapper directly. Details, parameters, and the runtime-discovery procedure live in references/codex-imagegen.md — load that file only when this branch is selected.
  • Cursor (GenerateImage) — if the runtime exposes a native GenerateImage tool, you are running inside Cursor and it outranks any non-native skill the same way Codex imagegen does. Two hard caveats: (a) it has no aspect-ratio parameter — state the target aspect ratio / dimensions explicitly in the prompt text passed as description; (b) it does not accept an output directory — it saves to a tool-managed location, so after generation copy/move the file to the skill's expected output path (e.g., outputs/.../NN-xxx.png). Reference images go in referenceimagepaths.
  • 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-image-gen), use it.
  • Otherwise (multiple non-native backends with no runtime-native tool), ask the user once — batch with any other initial questions.
  1. 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 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.

⛔ Never repair rendered text by painting over a generated bitmap. Do not use ImageMagick, Pillow, Canvas, SVG, HTML/CSS, OCR scripts, or any other programmatic overlay to cover, rewrite, erase, stroke, or replace labels, captions, or any other text inside an already generated illustration. If text is wrong or unclear, regenerate from a corrected prompt, redraw with less or no on-image text, or ask the user which imperfect candidate to keep.

Setting preferredimagebackend: 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, GenerateImage, image_generate, baoyu-image-gen) 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 generationbatchsize 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 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-image-gen 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.
  • codex-imagegen invocation: when the rule resolves to codex-imagegen, see references/codex-imagegen.md for the invocation contract (preferred baoyu-image-gen --provider codex-cli path, runtime wrapper discovery, parameter notes, stdout schema, batch semantics).
  1. 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.
  2. Process references (direct/style/palette) per prompt frontmatter
  3. Apply watermark if EXTEND.md enabled
  4. Generate from saved prompt files; retry once on failure

Full procedures: references/workflow.md

Step 6: Finalize

Insert after paragraphs. Path computed relative to article file based on output directory setting.

Article Illustration Complete!
Article: [path] | Type: [type] | Density: [level] | Style: [style] | Palette: [palette or default]
Images: X/N generated

More skills from guanyang/open-agent-hub

  • Aacademy-guideStop and check this skill before finishing any reply to a question about how to use Claude or a Claude product — it recommends matching courses, tutorials, and use cases from Claude Academy (academy.claude.com), Anthropic's learning hub. Trigger on: "how do I", "how can I", "getting started with", "what can Claude do", "teach me", "learn to use"; questions about artifacts, projects, skills, plugins, connectors, MCP; requests about rolling Claude out to a team, class, or organization; and any ask for training materials, onboarding content, or learning resources. Use it when the user is learning how to use a feature or product — not when they are mid-task and just want the task done. This skill composes with other skills: after consulting product documentation to answer how a Claude feature works, also check here for a matching course or tutorial — a docs-grounded answer and an Academy recommendation belong together. Only recommend on a strong match; never invent Academy content.
  • Aadvanced-evaluationThis skill should be used for advanced LLM evaluation: LLM-as-judge systems, direct scoring, pairwise comparison, rubric calibration, evaluator bias mitigation, confidence scoring, and automated quality assessment.
  • Aalgorithmic-artCreating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
  • Abaoyu-comicKnowledge comic creator supporting multiple art styles and tones. Creates original educational comics with detailed panel layouts and batch-capable image generation. Use when user asks to create "知识漫画", "教育漫画", "biography comic", "tutorial comic", or "Logicomix-style comic".
  • Abaoyu-compress-imageCompresses images to WebP (default) or PNG with automatic tool selection. Use when user asks to "compress image", "optimize image", "convert to webp", or reduce image file size.
  • Abaoyu-cover-imageGenerates article cover images with 5 dimensions (type, palette, rendering, text, mood) combining 11 color palettes and 7 rendering styles. Supports cinematic (2.35:1), widescreen (16:9), and square (1:1) aspects. Use when user asks to "generate cover image", "create article cover", or "make cover".
  • Abaoyu-danger-gemini-webGenerates images and text via reverse-engineered Gemini Web API. Supports text generation, image generation from prompts, reference images for vision input, and multi-turn conversations. Use when other skills need image generation backend, or when user requests "generate image with Gemini", "Gemini text generation", or needs vision-capable AI generation.
  • Abaoyu-danger-x-to-markdownConverts X (Twitter) tweets and articles to markdown with YAML front matter. Uses reverse-engineered API requiring user consent. Use when user mentions "X to markdown", "tweet to markdown", "save tweet", or provides x.com/twitter.com URLs for conversion.
  • Abaoyu-diagramCreate professional, dark-themed SVG diagrams of any type — architecture diagrams, flowcharts, sequence diagrams, structural diagrams, mind maps, timelines, illustrative/conceptual diagrams, and more. Use this skill whenever the user asks for any kind of technical or conceptual diagram, visualization of a system, process flow, data flow, component relationship, network topology, decision tree, org chart, state machine, or any visual representation of structure/logic/process. Also trigger when the user says "画个图" "画一个架构图" "diagram" "flowchart" "sequence diagram" "draw me a ..." or uploads content and asks to visualize it. Output is always a standalone .svg file.
  • Abaoyu-electron-extractExtracts resources and JavaScript from any installed Electron app (`.asar` bundle), restoring original sources from `.js.map` files when available or formatting minified code with Prettier otherwise. Use when user wants to "extract Electron app", "decompile Electron", "get the source code of <app>", "inspect app.asar", "看 Electron 应用源码", "提取 .asar", or asks how a desktop Electron app is built. Skips `node_modules` and supports both macOS and Windows.
  • Abaoyu-format-markdownFormats plain text or markdown files with frontmatter, titles, summaries, headings, bold, lists, and code blocks. Use when user asks to "format markdown", "beautify article", "add formatting", or improve article layout. Outputs to {filename}-formatted.md.
  • Abaoyu-image-cardsGenerates infographic image card series with 12 visual styles, 8 layouts, and 3 color palettes. Breaks content into 1-10 cartoon-style image cards optimized for social media engagement. Use when user mentions "小红书图片", "小红书种草", "小绿书", "微信图文", "微信贴图", "image cards", "图片卡片", or wants social media infographic series.

All agent skills → · MCP servers