Baoyu Xhs Images

Two Dimensions

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

Style Gallery

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

Layout Gallery

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

Auto Style Selection

When no --style is specified, analyze content to select:

Auto Layout Selection

When no --layout is specified, analyze content structure to select:

Layout by Position:

File Management

With Article Path

Save to xhs-images/ subdirectory in the same folder as the article:

posts/ai-future/
├── article.md
└── xhs-images/
    ├── outline.md
    ├── prompts/
    │   ├── 01-cover.md
    │   ├── 02-content-1.md
    │   └── ...
    ├── 01-cover.png
    ├── 02-content-1.png
    └── 03-ending.png

Without Article Path

Save to xhs-outputs/YYYY-MM-DD/[topic-slug]/:

xhs-outputs/
└── 2026-01-13/
    └── ai-agent-guide/
        ├── outline.md
        ├── prompts/
        │   ├── 01-cover.md
        │   └── ...
        ├── 01-cover.png
        └── 02-ending.png

Workflow

Step 1: Analyze Content & Select Style/Layout

1. Read content
2. If --style specified, use that style; otherwise auto-select
3. If --layout specified, use that layout; otherwise auto-select per image
4. Determine image count based on content complexity:

Note: Layout can vary per image in a series. Cover typically uses sparse, content pages use balanced/dense/list as appropriate.

Step 2: Generate Outline

Plan for each image with style and layout specifications:

# Xiaohongshu Infographic Series Outline

**Topic**: [topic description]
**Style**: [selected style]
**Default Layout**: [selected layout or "varies"]
**Image Count**: N
**Generated**: YYYY-MM-DD HH:mm

---

## Image 1 of N

**Position**: Cover
**Layout**: sparse
**Core Message**: [one-liner]
**Filename**: 01-cover.png

**Text Content**:
- Title: xxx
- Subtitle: xxx

**Visual Concept**: [style + layout appropriate description]

---

## Image 2 of N

**Position**: Content
**Layout**: [balanced/dense/list/comparison/flow]
**Core Message**: [one-liner]
**Filename**: 02-xxx.png

**Text Content**:
- Title: xxx
- Points: [list based on layout density]

**Visual Concept**: [description matching style + layout]

---
...

Step 3: Save Outline

Save outline as outline.md.

Step 4: Generate Images One by One

For each image, create a prompt file with style and layout specifications.

Prompt Format:

Infographic theme: [topic]
Style: [style name]
Layout: [layout name]
Position: [cover/content/ending]

Visual composition:
- Main visual: [style-appropriate description]
- Arrangement: [layout-specific structure]
- Decorative elements: [style-specific decorations]

Color scheme:
- Primary: [style primary color]
- Background: [style background color]
- Accent: [style accent color]

Text content:
- Title: 「xxx」(large, prominent)
- Key points: [based on layout density]

Layout instructions: [layout-specific guidance]
Style notes: [style-specific characteristics]

Layout-Specific Instructions:

Image Generation Skill Selection:

1. Check available image generation skills
2. If multiple skills available, ask user to choose

Generation Flow:

1. Call selected image generation skill with prompt file and output path
2. Confirm generation success
3. Report progress: "Generated X/N"
4. Continue to next

Step 5: Completion Report

Xiaohongshu Infographic Series Complete!

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

- 01-cover.png ✓ Cover (sparse)
- 02-content-1.png ✓ Content (balanced)
- 03-content-2.png ✓ Content (dense)
- 04-ending.png ✓ Ending (sparse)

Outline: outline.md

Content Breakdown Principles

1. Cover (Image 1): Strong visual impact, core title, attention hook → sparse layout
2. Content (Middle): Core points per image, density varies by content → balanced/dense/list/comparison/flow
3. Ending (Last): Summary / call-to-action / memorable quote → sparse or balanced

Style × Layout Matrix (recommended combinations):

✓✓ = highly recommended, ✓ = works well

Notes

• Image generation typically takes 10-30 seconds per image
• Auto-retry once on generation failure
• Use cartoon alternatives for sensitive public figures
• Output language matches input content language
• Maintain selected style consistency across all images in series