baoyu-xhs-images

name	baoyu-xhs-images
description	Xiaohongshu (Little Red Book) infographic series generator with multiple style options. Breaks down content into 1-10 cartoon-style infographics. Use when user asks to create "小红书图片", "XHS images", or "RedNote infographics".

Xiaohongshu Infographic Series Generator

Break down complex content into eye-catching infographic series for Xiaohongshu with multiple style options.

Usage

# Auto-select style and layout based on content
/baoyu-xhs-images posts/ai-future/article.md

# Specify style
/baoyu-xhs-images posts/ai-future/article.md --style notion

# Specify layout
/baoyu-xhs-images posts/ai-future/article.md --layout dense

# Combine style and layout
/baoyu-xhs-images posts/ai-future/article.md --style tech --layout list

# Direct content input
/baoyu-xhs-images
[paste content]

# Direct input with options
/baoyu-xhs-images --style bold --layout comparison
[paste content]

Options

Option	Description
--style <name>	Visual style (see Style Gallery)
--layout <name>	Information layout (see Layout Gallery)

Two Dimensions

Dimension	Controls	Options
Style	Visual aesthetics: colors, lines, decorations	cute, fresh, tech, warm, bold, minimal, retro, pop, notion
Layout	Information structure: density, arrangement	sparse, balanced, dense, list, comparison, flow

Style × Layout can be freely combined. Example: --style notion --layout dense creates an intellectual-looking knowledge card with high information density.

Style Gallery

Style	Description
cute (Default)	Sweet, adorable, girly - classic Xiaohongshu aesthetic
fresh	Clean, refreshing, natural
tech	Modern, smart, digital
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

Detailed style definitions: references/styles/<style>.md

Layout Gallery

Layout	Description
sparse (Default)	Minimal information, maximum impact (1-2 points)
balanced	Standard content layout (3-4 points)
dense	High information density, knowledge card style (5-8 points)
list	Enumeration and ranking format (4-7 items)
comparison	Side-by-side contrast layout
flow	Process and timeline layout (3-6 steps)

Detailed layout definitions: references/layouts/<layout>.md

Auto Selection

Content Signals	Style	Layout
Beauty, fashion, cute, girl, pink	cute	sparse/balanced
Health, nature, clean, fresh, organic	fresh	balanced/flow
Tech, AI, code, digital, app, tool	tech	dense/list
Life, story, emotion, feeling, warm	warm	balanced
Warning, important, must, critical	bold	list/comparison
Professional, business, elegant, simple	minimal	sparse/balanced
Classic, vintage, old, traditional	retro	balanced
Fun, exciting, wow, amazing	pop	sparse/list
Knowledge, concept, productivity, SaaS	notion	dense/list

File Structure

Each session creates an independent directory named by content slug:

xhs-images/{topic-slug}/
├── source-{slug}.{ext}             # Source files (text, images, etc.)
├── analysis.md                     # Deep analysis results
├── outline-style-[slug].md         # Variant A (e.g., outline-style-tech.md)
├── outline-style-[slug].md         # Variant B (e.g., outline-style-notion.md)
├── outline-style-[slug].md         # Variant C (e.g., outline-style-minimal.md)
├── outline.md                      # Final selected
├── prompts/
│   ├── 01-cover-[slug].md
│   ├── 02-content-[slug].md
│   └── ...
├── 01-cover-[slug].png
├── 02-content-[slug].png
└── NN-ending-[slug].png

Slug Generation:

1. Extract main topic from content (2-4 words, kebab-case)
2. Example: "AI工具推荐" → ai-tools-recommend

Conflict Resolution: If xhs-images/{topic-slug}/ already exists:

• Append timestamp: {topic-slug}-YYYYMMDD-HHMMSS
• Example: ai-tools exists → ai-tools-20260118-143052

Source Files: Copy all sources with naming source-{slug}.{ext}:

• source-article.md, source-photo.jpg, etc.
• Multiple sources supported: text, images, files from conversation

Workflow

Step 1: Analyze Content → analysis.md

Read source content, save it if needed, and perform deep analysis.

Actions:

1. Save source content (if not already a file):
   • If user provides a file path: use as-is
   • If user pastes content: save to source.md in target directory
2. Read source content
3. Deep analysis following references/analysis-framework.md:
   • Content type classification (种草/干货/测评/教程/避坑...)
   • Hook analysis (爆款标题潜力)
   • Target audience identification
   • Engagement potential (收藏/分享/评论)
   • Visual opportunity mapping
   • Swipe flow design
4. Detect source language
5. Determine recommended image count (2-10)
6. Select 3 style+layout combinations
7. Save to analysis.md

Step 2: Generate 3 Outline Variants

Based on analysis, create three distinct style variants.

For each variant:

1. Generate outline (outline-style-[slug].md):
   • YAML front matter with style, layout, image_count
   • Cover design with hook
   • Each image: layout, core message, text content, visual concept
   • Written in user's preferred language
   • Reference: references/outline-template.md

Variant	Selection Logic	Example Filename
A	Primary recommendation	outline-style-tech.md
B	Alternative style	outline-style-notion.md
C	Different audience/mood	outline-style-minimal.md

All variants are preserved after selection for reference.

Step 3: User Confirms All Options

IMPORTANT: Present ALL options in a single confirmation step using AskUserQuestion. Do NOT interrupt workflow with multiple separate confirmations.

Determine which questions to ask:

Question	When to Ask
Style variant	Always (required)
Default layout	Only if user might want to override
Language	Only if source_language ≠ user_language

Language handling:

• If source language = user language: Just inform user (e.g., "Images will be in Chinese")
• If different: Ask which language to use

AskUserQuestion format:

Question 1 (Style): Which style variant?
- A: tech + dense (Recommended) - 专业科技感，适合干货
- B: notion + list - 清爽知识卡片
- C: minimal + balanced - 简约高端风格
- Custom: 自定义风格描述

Question 2 (Layout) - only if relevant:
- Keep variant default (Recommended)
- sparse / balanced / dense / list / comparison / flow

Question 3 (Language) - only if mismatch:
- 中文 (匹配原文)
- English (your preference)

After confirmation:

1. Copy selected outline-style-[slug].md → outline.md
2. Update YAML front matter with confirmed options
3. If custom style: regenerate outline with that style
4. User may edit outline.md directly for fine-tuning

Step 4: Generate Images

With confirmed outline + style + layout:

For each image (cover + content + ending):

1. Save prompt to prompts/NN-{type}-[slug].md (in user's preferred language)
2. Generate image using confirmed style and layout
3. Report progress after each generation

Image Generation Skill Selection:

• Check available image generation skills
• If multiple skills available, ask user preference

Session Management: If image generation skill supports --sessionId:

1. Generate unique session ID: xhs-{topic-slug}-{timestamp}
2. Use same session ID for all images
3. Ensures visual consistency across generated images

Step 5: Completion Report

Xiaohongshu Infographic Series Complete!

Topic: [topic]
Style: [style name]
Layout: [layout name or "varies"]
Location: [directory path]
Images: N total

✓ analysis.md
✓ outline-style-tech.md
✓ outline-style-notion.md
✓ outline-style-minimal.md
✓ outline.md (selected: tech + dense)

Files:
- 01-cover-[slug].png ✓ Cover (sparse)
- 02-content-[slug].png ✓ Content (balanced)
- 03-content-[slug].png ✓ Content (dense)
- 04-ending-[slug].png ✓ Ending (sparse)

Image Modification

Edit Single Image

1. Identify image to edit (e.g., 03-content-chatgpt.png)
2. Update prompt in prompts/03-content-chatgpt.md if needed
3. Regenerate image using same session ID

Add New Image

1. Specify insertion position (e.g., after image 3)
2. Create new prompt with appropriate slug
3. Generate new image
4. Renumber files: All subsequent images increment NN by 1
5. Update outline.md with new image entry

Delete Image

1. Remove image file and prompt file
2. Renumber files: All subsequent images decrement NN by 1
3. Update outline.md to remove image entry

Content Breakdown Principles

1. Cover (Image 1): Hook + visual impact → sparse layout
2. Content (Middle): Core value per image → balanced/dense/list/comparison/flow
3. Ending (Last): CTA / summary → sparse or balanced

Style × Layout Matrix (✓✓ = highly recommended, ✓ = works well):

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

References

Detailed templates and guidelines in references/ directory:

• analysis-framework.md - XHS-specific content analysis
• outline-template.md - Outline format and examples
• styles/<style>.md - Detailed style definitions
• layouts/<layout>.md - Detailed layout definitions
• base-prompt.md - Base prompt template

Notes

• Image generation typically takes 10-30 seconds per image
• Auto-retry once on generation failure
• Use cartoon alternatives for sensitive public figures
• All prompts and text use confirmed language preference
• Maintain style consistency across all images in series

Extension Support

Custom styles and configurations via EXTEND.md.

Check paths (priority order):

1. .baoyu-skills/baoyu-xhs-images/EXTEND.md (project)
2. ~/.baoyu-skills/baoyu-xhs-images/EXTEND.md (user)

If found, load before Step 1. Extension content overrides defaults.