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.
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
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.
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):
Prefer built-in user-input tools exposed by the current agent runtime — e.g., AskUserQuestion, request_user_input, clarify, ask_user, or any equivalent.
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.
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:
Current-request override — if the user names a specific backend in the current message, use it.
Saved preference — if EXTEND.md sets preferred_image_backend to a backend available right now, use it.
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.
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.
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.
⛔ 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:
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.
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.
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
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
flow/balanced/dense
hand-drawn-edu, sketch-card, sketch-summary
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
✓✓
✓✓
✓✓
✓✓
✓✓
✓✓
✓✓
✓✓
chalkboard
✓✓
✓✓
✓✓
✓✓
✓
✓✓
✓✓
✓
study-notes
✗
✓
✓✓
✓✓
✓
✓
✓✓
✓
screen-print
✓✓
✓✓
✗
✓
✓✓
✓
✗
✓✓
sketch-notes
✓
✓✓
✓✓
✓✓
✓
✓✓
✓✓
✓
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:
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
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.
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.
Save the source (backup rule applies if source.md exists).
Run the deep analysis in references/workflows/analysis-framework.md: content type, hook potential, audience, engagement signals, visual opportunity map, swipe flow.
Auto-recommend strategy + style + layout + palette using the Auto-Selection table above.
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.
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.
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:
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.
Generate image 1 first without --ref; backup rule applies to the PNG file. This establishes the anchor.
Build a task list for images 2+ using image 1 as --ref <path-to-image-01.png>.
Dispatch images 2+ in batches per the ## Batch Generation Policy: backend native batch first, runtime parallel tool calls second, sequential only as fallback.
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.
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.
