baoyu-xhs-images

name	baoyu-xhs-images
description	Generates 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", baoyu-xhs-images, or wants social media infographic series.
version	2.0.1
metadata	{"openclaw":{"homepage":"https://github.com/JimLiu/baoyu-skills#baoyu-xhs-images"}}

Image Card Series Generator

Break down complex content into eye-catching image card series with multiple style options.

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-image-gen) unless the user has explicitly pinned a different preferred_image_backend.
   • 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 reference_image_paths.
   • Other runtime-native tools — if the runtime exposes a different native image tool (e.g., Hermes image_generate), use it the same way.

⛔ 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.

⛔ 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 titles, body copy, tags, or any other text inside an already generated image card. If text is wrong or unclear, regenerate from a corrected prompt, switch to a layout with less on-card text, or ask the user which imperfect candidate to keep.

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 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 current generation group 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, session ID, 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:

• Honor the image-1 anchor chain: generate image 1 first, then batch images 2+ using image 1 as the reference.
• Never start a batch until every selected prompt file for that batch exists 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 3 until the user completes Step 2.
• Skip confirmation only when the current request explicitly says to do so, for example: --yes, "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 strategy / style / layout / palette / count / backend in the next user-facing update before generating.

Language

Respond in the user's language across questions, progress, errors, and completion summary. Keep technical tokens (style names, file paths, code) in English.

Options

Option	Description
--style <name>	Visual style (see Styles below)
--layout <name>	Information layout (see Layouts below)
--palette <name>	Color override: macaron / warm / neon
--preset <name>	Style + layout + optional palette shorthand (see Presets below; per-preset prompt fragments in references/style-presets.md)
--ref <files...>	Reference images applied to image 1 as the series anchor
--batch-size <n>	Temporary generation batch size for this run. Default: generation_batch_size from EXTEND.md, otherwise 4. Clamp to 1-8.
--yes	Non-interactive: skip all confirmations, use EXTEND.md or built-in defaults, auto-confirm recommended plan (Path A)

Dimensions

Three independent knobs combine freely:

Dimension	Controls	Options
Style	Visual aesthetics (lines, decorations, rendering)	12 styles (see Styles below)
Layout	Information structure (density, arrangement)	8 layouts (see Layouts below)
Palette (optional)	Color override, replaces the style's default colors	macaron / warm / neon (see Palettes below)

Example: --style notion --layout dense makes an intellectual knowledge card; add --palette macaron to soften the colors without changing notion's rendering rules. A --preset is a shorthand for style + layout (+ optional palette).

Palette behavior: no --palette → style's built-in colors; --palette <name> → overrides colors only, rendering rules unchanged. Some styles declare a default_palette (e.g., sketch-notes defaults to macaron).

Styles (12)

Style	Description
cute (Default)	Sweet, adorable, girly aesthetic
fresh	Clean, refreshing, natural
warm	Cozy, friendly, approachable
bold	High impact, attention-grabbing
minimal	Ultra-clean, sophisticated
retro	Vintage, nostalgic, trendy
pop	Vibrant, energetic, eye-catching
notion	Minimalist hand-drawn line art, intellectual
chalkboard	Colorful chalk on black board, educational
study-notes	Realistic handwritten photo style, blue pen + red annotations + yellow highlighter
screen-print	Bold poster art, halftone textures, limited colors, symbolic storytelling
sketch-notes	Hand-drawn educational infographic, macaron pastels on warm cream, wobble lines

Per-style specifications: references/presets/<style>.md.

Layouts (8)

Layout	Description
sparse (Default)	1-2 points, maximum impact
balanced	3-4 points, standard
dense	5-8 points, knowledge-card style
list	Enumeration / ranking (4-7 items)
comparison	Side-by-side contrast
flow	Process / timeline (3-6 steps)
mindmap	Center-radial (4-8 branches)
quadrant	Four-quadrant / circular sections

Layout specs: references/elements/canvas.md.

Palettes (optional override)

Replaces the style's colors while keeping rendering rules (line treatment, textures) intact.

Palette	Background	Zone Colors	Accent	Feel
macaron	Warm cream #F5F0E8	Blue #A8D8EA, Lavender #D5C6E0, Mint #B5E5CF, Peach #F8D5C4	Coral #E8655A	Soft, educational
warm	Soft peach #FFECD2	Orange #ED8936, Terracotta #C05621, Golden #F6AD55, Rose #D4A09A	Sienna #A0522D	Earth tones, cozy
neon	Dark purple #1A1025	Cyan #00F5FF, Magenta #FF00FF, Green #39FF14, Pink #FF6EC7	Yellow #FFFF00	High-energy, futuristic

Palette specs: references/palettes/<palette>.md.

Presets (style + layout shortcuts)

Quick-start combos, grouped by scenario. Use --preset <name> or recommend during Step 2.

Knowledge & Learning:

Preset	Style	Layout	Best For
knowledge-card	notion	dense	干货知识卡、概念科普
checklist	notion	list	清单、排行榜
concept-map	notion	mindmap	概念图、知识脉络
swot	notion	quadrant	SWOT 分析、四象限
tutorial	chalkboard	flow	教程步骤、操作流程
classroom	chalkboard	balanced	课堂笔记、知识讲解
study-guide	study-notes	dense	学习笔记、考试重点
hand-drawn-edu	sketch-notes	flow	手绘教程、流程图解
sketch-card	sketch-notes	dense	手绘知识卡
sketch-summary	sketch-notes	balanced	手绘总结、图文笔记

Lifestyle & Sharing:

Preset	Style	Layout	Best For
cute-share	cute	balanced	少女风分享、日常种草
girly	cute	sparse	甜美封面、氛围感
cozy-story	warm	balanced	生活故事、情感分享
product-review	fresh	comparison	产品对比、测评
nature-flow	fresh	flow	健康流程、自然主题

Impact & Opinion:

Preset	Style	Layout	Best For
warning	bold	list	避坑指南、重要提醒
versus	bold	comparison	正反对比
clean-quote	minimal	sparse	金句、极简封面
pro-summary	minimal	balanced	专业总结、商务内容

Trend & Entertainment:

Preset	Style	Layout	Best For
retro-ranking	retro	list	复古排行、经典盘点
throwback	retro	balanced	怀旧分享
pop-facts	pop	list	趣味冷知识
hype	pop	sparse	炸裂封面、惊叹分享

Poster & Editorial:

Preset	Style	Layout	Best For
poster	screen-print	sparse	海报风封面、影评书评
editorial	screen-print	balanced	观点文章、文化评论
cinematic	screen-print	comparison	电影对比、戏剧张力

Full prompt-fragment definitions: references/style-presets.md.

Auto-Selection

Match content signals to the best combo. First row whose keywords appear wins; fall back to cute-share if nothing matches.

Signals in source	Style	Layout	Recommended preset
beauty, fashion, cute, girl, pink	cute	sparse/balanced	cute-share, girly
health, nature, fresh, organic	fresh	balanced/flow	product-review, nature-flow
life, story, emotion, warm	warm	balanced	cozy-story
warning, important, must, critical	bold	list/comparison	warning, versus
professional, business, elegant	minimal	sparse/balanced	clean-quote, pro-summary
classic, vintage, traditional	retro	balanced	throwback, retro-ranking
fun, exciting, wow, amazing	pop	sparse/list	hype, pop-facts
knowledge, concept, productivity, SaaS	notion	dense/list	knowledge-card, checklist
education, tutorial, learning, classroom	chalkboard	balanced/dense	tutorial, classroom
notes, handwritten, study guide, realistic	study-notes	dense/list/mindmap	study-guide
movie, poster, opinion, editorial, cinematic	screen-print	sparse/comparison	poster, editorial, cinematic
hand-drawn, infographic, workflow, 手绘，图解	sketch-notes

Style × Layout Matrix

Compatibility scores (✓✓ highly recommended, ✓ works well, ✗ avoid). Use when the user picks a non-default combo and you want to flag a poor match.

	sparse	balanced	dense	list	comparison	flow	mindmap	quadrant
cute	✓✓	✓✓	✓	✓✓	✓	✓	✓	✓
fresh	✓✓	✓✓	✓	✓	✓	✓✓	✓	✓
warm	✓✓	✓✓	✓	✓	✓✓	✓	✓	✓
bold	✓✓	✓	✓	✓✓	✓✓	✓	✓	✓✓
minimal	✓✓	✓✓	✓✓	✓	✓	✓	✓	✓
retro	✓✓	✓✓	✓	✓✓	✓	✓	✓	✓
pop	✓✓	✓✓	✓	✓✓	✓✓	✓	✓	✓
notion	✓✓	✓✓	✓✓	✓✓

Outline Strategies

Three differentiated approaches — each produces a structurally different outline. The workflow recommends one; Path C generates all three and lets the user choose.

Strategy	Concept	Best for	Structure
A — Story-Driven	Personal experience as the thread, emotional resonance first	Reviews, personal shares, transformation	Hook → Problem → Discovery → Experience → Conclusion
B — Information-Dense	Value-first, efficient information delivery	Tutorials, comparisons, checklists	Core conclusion → Info card → Pros/Cons → Recommendation
C — Visual-First	Visual impact as core, minimal text	High-aesthetic products, lifestyle, mood content	Hero image → Detail shots → Lifestyle scene → CTA

Reference Images

User-supplied refs are separate from the internal "image-1 as anchor" chain (Step 3) — they layer on top of it.

Intake: via --ref <files...> or paths pasted in conversation.

• File path → copy to refs/NN-ref-{slug}.{ext}
• Pasted with no path → ask for the path, or extract style traits as a text fallback

Usage modes (per reference):

Usage	Effect
direct	Pass the file to the backend (typically on image 1 only, so the anchor propagates through the chain)
style	Extract style traits and append to every card's prompt body
palette	Extract hex colors and append to every card's prompt body

Record refs in each affected card's prompt frontmatter:

references:
  - ref_id: 01
    filename: 01-ref-brand.png
    usage: direct

At generation time: verify files exist. Image 1 with usage: direct + backend that accepts refs → pass via the backend's ref parameter (becomes the chain anchor). Images 2+ keep using image-1 as --ref per Step 3 — do NOT re-stack user refs on top (avoids conflicting signals). For style/palette, embed extracted traits in every prompt.

File Layout

image-cards/{topic-slug}/
├── source-{slug}.{ext}
├── analysis.md
├── outline-strategy-{a,b,c}.md    # Path C only
├── outline.md
├── prompts/NN-{type}-{slug}.md
├── NN-{type}-{slug}.png
└── refs/                          # only if --ref used

Slug: 2-4 words, kebab-case. "AI 工具推荐" → ai-tools-recommend. On collision, append -YYYYMMDD-HHMMSS.

Backup rule (applies throughout): before overwriting any file — source, outline, prompt, image — rename the existing one to <name>-backup-YYYYMMDD-HHMMSS.<ext>. This protects user edits.

Workflow

- [ ] Step 0: Load EXTEND.md ⛔ BLOCKING (interactive only)
- [ ] Step 1: Analyze content → analysis.md
- [ ] Step 2: Smart Confirm ⚠️ REQUIRED (Path A / B / C)
- [ ] Step 3: Generate images
- [ ] Step 4: Completion report

Step 0: Load EXTEND.md ⛔ BLOCKING

Check these paths in order; first hit wins:

Path	Scope
.baoyu-skills/baoyu-xhs-images/EXTEND.md	Project
${XDG_CONFIG_HOME:-$HOME/.config}/baoyu-skills/baoyu-xhs-images/EXTEND.md	XDG
$HOME/.baoyu-skills/baoyu-xhs-images/EXTEND.md	User home

• Found → read, parse, print a summary (style / layout / watermark / language), continue.
• Not found + interactive → run first-time setup (see references/config/first-time-setup.md) and save before anything else. Do NOT analyze content or ask style questions until preferences exist — this keeps first-run behavior predictable.
• Not found + --yes → skip setup, use built-in defaults (no watermark, style/layout auto-selected, language from content). Do not prompt, do not create EXTEND.md.

EXTEND.md keys: watermark, preferred style/layout, custom style definitions, language preference, preferred image backend, generation batch size. Schema: references/config/preferences-schema.md.

Step 1: Analyze Content → analysis.md

1. Save the source (backup rule applies if source.md exists).
2. Run the deep analysis in references/workflows/analysis-framework.md: content type, hook potential, audience, engagement signals, visual opportunity map, swipe flow.
3. Detect source language, pick recommended image count (2-10).
4. Auto-recommend strategy + style + layout + palette using the Auto-Selection table above.
5. Write everything to analysis.md.

Step 2: Smart Confirm ⚠️ REQUIRED

Hard gate: this step is mandatory per the Confirmation Policy — Step 3 cannot start until the user confirms here (or explicitly opts out with --yes / equivalent wording in the current request).

Goal: present the auto-recommended plan and let the user confirm or adjust. Skip this step entirely under --yes — proceed with Path A using the analysis and any CLI overrides.

Display summary before asking:

📋 内容分析
  主题：[topic] | 类型：[content_type]
  要点：[key points]
  受众：[audience]

🎨 推荐方案（自动匹配）
  策略：[A/B/C] [name]（[reason]）
  风格：[style] · 布局：[layout] · 配色：[palette or 默认] · 预设：[preset]
  图片：[N]张（封面+[N-2]内容+结尾）
  元素：[background] / [decorations] / [emphasis]

Then ask one question — three paths. Verbatim option copy: references/confirmation.md.

Path A — Quick confirm (trust auto-recommendation): generate a single outline using the recommended strategy + style → save to outline.md → Step 3.

Path B — Customize: ask five questions (strategy/style, layout, palette, count, optional notes) with the recommendation pre-filled — blanks keep the recommendation. Generate one outline with the user's choices → outline.md → Step 3. See references/confirmation.md.

Path C — Detailed mode: two sub-confirmations.

• Step 2a — Content understanding: ask selling points (multi-select), audience, style preference (authentic / professional / aesthetic / auto), optional context. Update analysis.md.
• Step 2b — Three outline variants: generate outline-strategy-a.md, outline-strategy-b.md, outline-strategy-c.md. Each MUST have a different structure AND a different recommended style — include style_reason in the frontmatter. Page-count heuristic: A ~4-6, B ~3-5, C ~3-4. Template: references/workflows/outline-template.md; frontmatter example in references/confirmation.md.
• Step 2c — Selection: ask three questions (outline A/B/C/Combined, style, visual elements). Save selected/merged outline to outline.md → Step 3.

Step 3: Generate Images

With confirmed outline + style + layout + palette:

Visual consistency — image-1 anchor chain: character / mascot / color rendering drifts between calls unless you anchor them. Generate image 1 (cover) first WITHOUT --ref, then pass image 1 as --ref to every subsequent image. This is the single most important consistency trick for this skill — don't skip it even if the backend also supports a session ID.

Generation flow:

1. Write the full prompt for every image to prompts/NN-{type}-{slug}.md in the user's preferred language (backup rule applies), then verify all selected prompt files exist.
2. Generate image 1 first without --ref; backup rule applies to the PNG file. This establishes the anchor.
3. Build a task list for images 2+ using image 1 as --ref <path-to-image-01.png>.
4. Dispatch images 2+ in batches per the ## Batch Generation Policy: backend native batch first, runtime parallel tool calls second, sequential only as fallback.
5. Report progress after each completed image. On failure, retry only the failed item once from the same saved prompt file.

Watermark (if enabled in EXTEND.md): append to the generation prompt:

Include a subtle watermark "[content]" positioned at [position].
The watermark should be legible but not distracting.

See references/config/watermark-guide.md.

Backend selection: per the Image Generation Tools rule at the top — use whatever is available, ask once if multiple, before any generation. Under --yes, use the EXTEND.md preference and fall back to the first available backend. Prompt files MUST exist before invoking any backend.

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 — n=1 per call so card batches must dispatch one wrapper call per card; the wrapper does NOT accept --sessionId, so chain consistency must come from --ref per Step 3 above).

Session ID (if the backend supports --sessionId): use cards-{topic-slug}-{timestamp} for every image; combined with the ref chain this gives maximum consistency.

Step 4: Completion Report

Image Card Series Complete!

Topic: [topic]
Mode: [Quick / Custom / Detailed]
Strategy: [A/B/C/Combined]
Style: [name]
Palette: [name or "default"]
Layout: [name or "varies"]
Location: [directory]
Images: N total

✓ analysis.md
✓ outline.md
✓ outline-strategy-a/b/c.md (detailed mode only)

- 01-cover-[slug].png ✓ Cover (sparse)
- 02-content-[slug].png ✓ Content (balanced)
- ...
- NN-ending-[slug].png ✓ Ending (sparse)

Content Breakdown Principles

Position	Purpose	Typical layout
Cover (image 1)	Hook + visual impact	sparse
Content (middle)	Core value per image	balanced / dense / list / comparison / flow
Ending (last)	CTA / summary	sparse or balanced

For the style × layout compatibility matrix, see the Style × Layout Matrix above.

Image Modification

Action	How
Edit	Update prompts/NN-{type}-{slug}.md first, then regenerate with the same session ID
Add	Specify position, create prompt, generate, renumber subsequent files NN+1, update outline
Delete	Remove files, renumber subsequent NN-1, update outline

Always update the prompt file before regenerating — it's the source of truth and makes changes reproducible.

Text correction policy:

• If a card's title, body copy, tags, or any other rendered text is misspelled, garbled, hard to read, or visually weak, do not patch the bitmap with code.
• For text-correction regenerations, write a new prompt file and a new output path so the flawed candidate is preserved for comparison.
• Post-processing is limited to crop, resize, compression, or format conversion that does not alter text or the main composition.

References

File	Content
references/confirmation.md	Verbatim AskUserQuestion copy for every confirmation path
references/style-presets.md	Full preset shortcut definitions
references/presets/<style>.md	Per-style element definitions
references/palettes/<name>.md	Per-palette color definitions
references/elements/canvas.md	Aspect ratios, safe zones, grid layouts
references/elements/image-effects.md	Cutout, stroke, filters
references/elements/typography.md	Decorated text, tags, text direction
references/elements/decorations.md	Emphasis marks, backgrounds, doodles, frames
references/workflows/analysis-framework.md	Content analysis framework
references/workflows/outline-template.md	Outline template with layout guide
references/workflows/prompt-assembly.md	Prompt assembly guide
references/config/preferences-schema.md	EXTEND.md schema
references/config/first-time-setup.md	First-time setup flow
references/config/watermark-guide.md	Watermark configuration

Notes

• Auto-retry once on generation failure before reporting an error.
• For sensitive public figures, use stylized cartoon alternatives.
• Smart Confirm (Step 2) is required; Detailed mode adds a second confirmation (2a + 2c).

Changing Preferences

EXTEND.md lives at the first matching path listed in Step 0. 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-xhs-images 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: codex-imagegen — pin to Codex's built-in.
  • preferred_image_backend: baoyu-image-gen — pin to the baoyu-image-gen skill.
  • preferred_image_backend: ask — confirm backend every run.
  • generation_batch_size: 4 — default number of images to render concurrently when the backend/runtime supports batch or parallel generation.
  • preferred_style: notion, preferred_layout: dense, preferred_palette: macaron, language: zh.
  • watermark.enabled: true + watermark.content: "@handle" — add a watermark.