Agent skill

Baoyu Image Cards

by guanyang in guanyang/open-agent-hub

Generates infographic image card series with 12 visual styles, 8 layouts, and 3 color palettes.

MITAuto-check passedMedia & Creative

Install Baoyu Image Cards

skills CLI
$ npx skills add guanyang/open-agent-hub --skill baoyu-image-cards -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install guanyang/open-agent-hub baoyu-image-cards --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/guanyang/open-agent-hub.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/baoyu-image-cards .claude/skills/baoyu-image-cards && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
baoyu-image-cards
GitHub stars
977
Token cost
~6.7k tokens
SKILL.md length
2,850 words
Files
29 (incl. references)
Skills in repo
26
Repo updated
First seen
Licence
MIT

At a glance

Generates infographic image card series with 12 visual styles, 8 layouts, and 3 color palettes.

  • Works in 5 steps: Load EXTEND.md ⛔ BLOCKING → Analyze Content → analysis.md → Smart Confirm ⚠️ REQUIRED → …
  • User mentions 小红书图片
  • SKILL.md covers User Input Tools, Image Generation Tools, Batch Generation Policy and Confirmation Policy, plus 18 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Baoyu Image Cards is an agent skill from guanyang/open-agent-hub. 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", or wants social media infographic series.

Its SKILL.md is about 6.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 33 other files, including reference files (for example `references/config/first-time-setup.md`, `references/config/preferences-schema.md` and `references/config/watermark-guide.md`).

It sits in Media & Creative, covering Infographics, Theming and dark mode and Image generation. The repository describes itself as: A lightweight, zero-dependency CLI tool to manage and activate capabilities for AI coding assistants (such as Claude Code, Cursor, Trae, etc.). The licence is MIT.

When your agent uses it

  • User mentions 小红书图片
  • Wants social media infographic series

Example prompts

  • “image cards”
  • “Use the baoyu-image-cards skill to generate infographic image card series with 12 visual styles, 8 layouts, and 3 color palettes”
  • “/baoyu-image-cards”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Load EXTEND.md ⛔ BLOCKING
  2. Analyze Content → analysis.md
  3. Smart Confirm ⚠️ REQUIRED
  4. Generate Images
  5. Completion Report

What it can do on your machine

Read from SKILL.md and the folder at commit acd7c6c. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are yaml).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Baoyu Image Cards loads about 6.7k tokens when it runs, and up to ~27k if it reads all its reference files. Until then it costs about 83 tokens; SKILL.md has 2,850 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~83
When it runs · the whole SKILL.md, loaded when a task matches
~6.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~27k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from guanyang/open-agent-hub at commit acd7c6c, republished under its MIT licence (© guanyang). 2,850 words, ~6,696 tokens.

Download SKILL.mdSave it as .claude/skills/baoyu-image-cards/SKILL.md (or your agent's skills folder). This skill also uses 28 other files; get the full folder from GitHub.
name
baoyu-image-cards
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", or wants social media infographic series.
version
1.57.0

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-imagine) unless the user has explicitly pinned a different preferred_image_backend.
    • 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.

⛔ 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, image_generate, baoyu-imagine) 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

OptionDescription
--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.
--yesNon-interactive: skip all confirmations, use EXTEND.md or built-in defaults, auto-confirm recommended plan (Path A)

Dimensions

Three independent knobs combine freely:

DimensionControlsOptions
StyleVisual aesthetics (lines, decorations, rendering)12 styles (see Styles below)
LayoutInformation structure (density, arrangement)8 layouts (see Layouts below)
Palette (optional)Color override, replaces the style's default colorsmacaron / 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)

StyleDescription
cute (Default)Sweet, adorable, girly aesthetic
freshClean, refreshing, natural
warmCozy, friendly, approachable
boldHigh impact, attention-grabbing
minimalUltra-clean, sophisticated
retroVintage, nostalgic, trendy
popVibrant, energetic, eye-catching
notionMinimalist hand-drawn line art, intellectual
chalkboardColorful chalk on black board, educational
study-notesRealistic handwritten photo style, blue pen + red annotations + yellow highlighter
screen-printBold poster art, halftone textures, limited colors, symbolic storytelling
sketch-notesHand-drawn educational infographic, macaron pastels on warm cream, wobble lines

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

Layouts (8)

LayoutDescription
sparse (Default)1-2 points, maximum impact
balanced3-4 points, standard
dense5-8 points, knowledge-card style
listEnumeration / ranking (4-7 items)
comparisonSide-by-side contrast
flowProcess / timeline (3-6 steps)
mindmapCenter-radial (4-8 branches)
quadrantFour-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.

PaletteBackgroundZone ColorsAccentFeel
macaronWarm cream #F5F0E8Blue #A8D8EA, Lavender #D5C6E0, Mint #B5E5CF, Peach #F8D5C4Coral #E8655ASoft, educational
warmSoft peach #FFECD2Orange #ED8936, Terracotta #C05621, Golden #F6AD55, Rose #D4A09ASienna #A0522DEarth tones, cozy
neonDark purple #1A1025Cyan #00F5FF, Magenta #FF00FF, Green #39FF14, Pink #FF6EC7Yellow #FFFF00High-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:

PresetStyleLayoutBest For
knowledge-cardnotiondense干货知识卡、概念科普
checklistnotionlist清单、排行榜
concept-mapnotionmindmap概念图、知识脉络
swotnotionquadrantSWOT 分析、四象限
tutorialchalkboardflow教程步骤、操作流程
classroomchalkboardbalanced课堂笔记、知识讲解
study-guidestudy-notesdense学习笔记、考试重点
hand-drawn-edusketch-notesflow手绘教程、流程图解
sketch-cardsketch-notesdense手绘知识卡
sketch-summarysketch-notesbalanced手绘总结、图文笔记

Lifestyle & Sharing:

PresetStyleLayoutBest For
cute-sharecutebalanced少女风分享、日常种草
girlycutesparse甜美封面、氛围感
cozy-storywarmbalanced生活故事、情感分享
product-reviewfreshcomparison产品对比、测评
nature-flowfreshflow健康流程、自然主题

Impact & Opinion:

PresetStyleLayoutBest For
warningboldlist避坑指南、重要提醒
versusboldcomparison正反对比
clean-quoteminimalsparse金句、极简封面
pro-summaryminimalbalanced专业总结、商务内容

Trend & Entertainment:

PresetStyleLayoutBest For
retro-rankingretrolist复古排行、经典盘点
throwbackretrobalanced怀旧分享
pop-factspoplist趣味冷知识
hypepopsparse炸裂封面、惊叹分享

Poster & Editorial:

PresetStyleLayoutBest For
posterscreen-printsparse海报风封面、影评书评
editorialscreen-printbalanced观点文章、文化评论
cinematicscreen-printcomparison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 sourceStyleLayoutRecommended preset
beauty, fashion, cute, girl, pinkcutesparse/balancedcute-share, girly
health, nature, fresh, organicfreshbalanced/flowproduct-review, nature-flow
life, story, emotion, warmwarmbalancedcozy-story
warning, important, must, criticalboldlist/comparisonwarning, versus
professional, business, elegantminimalsparse/balancedclean-quote, pro-summary
classic, vintage, traditionalretrobalancedthrowback, retro-ranking
fun, exciting, wow, amazingpopsparse/listhype, pop-facts
knowledge, concept, productivity, SaaSnotiondense/listknowledge-card, checklist
education, tutorial, learning, classroomchalkboardbalanced/densetutorial, classroom
notes, handwritten, study guide, realisticstudy-notesdense/list/mindmapstudy-guide
movie, poster, opinion, editorial, cinematicscreen-printsparse/comparisonposter, editorial, cinematic
hand-drawn, infographic, workflow, 手绘, 图解sketch-notesflow/balanced/densehand-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.

sparsebalanceddenselistcomparisonflowmindmapquadrant
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.

StrategyConceptBest forStructure
A — Story-DrivenPersonal experience as the thread, emotional resonance firstReviews, personal shares, transformationHook → Problem → Discovery → Experience → Conclusion
B — Information-DenseValue-first, efficient information deliveryTutorials, comparisons, checklistsCore conclusion → Info card → Pros/Cons → Recommendation
C — Visual-FirstVisual impact as core, minimal textHigh-aesthetic products, lifestyle, mood contentHero image → Detail shots → Lifestyle scene → CTA
Show full SKILL.md (1,207 more words)Show less

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):

UsageEffect
directPass the file to the backend (typically on image 1 only, so the anchor propagates through the chain)
styleExtract style traits and append to every card's prompt body
paletteExtract hex colors and append to every card's prompt body

Record refs in each affected card's prompt frontmatter:

yaml
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:

PathScope
.baoyu-skills/baoyu-image-cards/EXTEND.mdProject
${XDG_CONFIG_HOME:-$HOME/.config}/baoyu-skills/baoyu-image-cards/EXTEND.mdXDG
$HOME/.baoyu-skills/baoyu-image-cards/EXTEND.mdUser 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.

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

PositionPurposeTypical layout
Cover (image 1)Hook + visual impactsparse
Content (middle)Core value per imagebalanced / dense / list / comparison / flow
Ending (last)CTA / summarysparse or balanced

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

Image Modification

ActionHow
EditUpdate prompts/NN-{type}-{slug}.md first, then regenerate with the same session ID
AddSpecify position, create prompt, generate, renumber subsequent files NN+1, update outline
DeleteRemove 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

FileContent
references/confirmation.mdVerbatim AskUserQuestion copy for every confirmation path
references/style-presets.mdFull preset shortcut definitions
references/presets/<style>.mdPer-style element definitions
references/palettes/<name>.mdPer-palette color definitions
references/elements/canvas.mdAspect ratios, safe zones, grid layouts
references/elements/image-effects.mdCutout, stroke, filters
references/elements/typography.mdDecorated text, tags, text direction
references/elements/decorations.mdEmphasis marks, backgrounds, doodles, frames
references/workflows/analysis-framework.mdContent analysis framework
references/workflows/outline-template.mdOutline template with layout guide
references/workflows/prompt-assembly.mdPrompt assembly guide
references/config/preferences-schema.mdEXTEND.md schema
references/config/first-time-setup.mdFirst-time setup flow
references/config/watermark-guide.mdWatermark 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-image-cards 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-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 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.

© guanyang, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 28 other files (references) in skills/baoyu-image-cards of guanyang/open-agent-hub.

  • SKILL.md
  • references/config/first-time-setup.md
  • references/config/preferences-schema.md
  • references/config/watermark-guide.md
  • references/confirmation.md
  • references/elements/canvas.md
  • references/elements/decorations.md
  • references/elements/image-effects.md
  • references/elements/typography.md
  • references/palettes/macaron.md
  • references/palettes/neon.md
  • references/palettes/warm.md
  • references/presets/bold.md
  • references/presets/chalkboard.md
  • references/presets/cute.md
  • references/presets/fresh.md
  • … and 13 more

Open the folder on GitHubat commit acd7c6c

Compare with similar skills

Baoyu Image Cards next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Baoyu Image Cards compared with similar skills
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Baoyu Image Cards this skillguanyang/open-agent-hub977—~6.7kAutomated safety check: PassMIT
Data Viz Rendererzebbern/claude-code-guide4.7k—~1.3kAutomated safety check: PassMIT
Image Generationonyx-dot-app/onyx32k1 repos~1.7kAutomated safety check: PassCustom licence
SEO Image GeneratorAgriciDaniel/claude-seo19k2 repos~2.1kAutomated safety check: PassMIT
Ecom Image2buluslan/gpt-image2-ecommerce410—~2.9kAutomated safety check: PassMIT
Design Image Studiokangarooking/design-image-studio102—~1.5kAutomated safety check: PassMIT

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Questions about Baoyu Image Cards

What does Baoyu Image Cards do?

Generates infographic image card series with 12 visual styles, 8 layouts, and 3 color palettes. Baoyu Image Cards is an agent skill from guanyang/open-agent-hub. Generates infographic image card series with 12 visual styles, 8 layouts, and 3 color palettes.

When should I use Baoyu Image Cards?

Baoyu Image Cards fits situations like: user mentions 小红书图片; wants social media infographic series.

How do I install Baoyu Image Cards in Claude Code?

Run `npx skills add guanyang/open-agent-hub --skill baoyu-image-cards -a claude-code`. Or copy the skill folder (skills/baoyu-image-cards in guanyang/open-agent-hub) into .claude/skills/baoyu-image-cards in your project. Claude Code loads it when a task matches its description.

How do I install Baoyu Image Cards in Codex?

Run `npx skills add guanyang/open-agent-hub --skill baoyu-image-cards -a codex`. Or copy the skill folder (skills/baoyu-image-cards in guanyang/open-agent-hub) into .agents/skills/baoyu-image-cards in your project. Codex loads it when a task matches its description.

Can I use Baoyu Image Cards in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add guanyang/open-agent-hub --skill baoyu-image-cards -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/baoyu-image-cards, .gemini/skills/baoyu-image-cards, .github/skills/baoyu-image-cards and .opencode/skills/baoyu-image-cards in your project.

What does Baoyu Image Cards need to run?

SKILL.md names no scripts, command-line tools or credentials: Baoyu Image Cards is instructions for the agent only.

Does Baoyu Image Cards access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Baoyu Image Cards safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Baoyu Image Cards use?

Baoyu Image Cards is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Baoyu Image Cards use?

About 6.7k tokens (SKILL.md is roughly 27k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 20k tokens, read only when the agent opens those files.

What are the alternatives to Baoyu Image Cards?

Skills that share tags, products or a category with Baoyu Image Cards: Data Viz Renderer (zebbern/claude-code-guide, 4.7k stars), Image Generation (onyx-dot-app/onyx, 32k stars), SEO Image Generator (AgriciDaniel/claude-seo, 19k stars) and Ecom Image2 (buluslan/gpt-image2-ecommerce, 410 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Baoyu Image Cards?

guanyang (a GitHub user) maintains it in guanyang/open-agent-hub, which has 977 GitHub stars. The repository holds 26 skills in this directory. The repository was last updated on October 10, 2026.

Source: guanyang/open-agent-hub on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.