domains / media / xhs-infographic-cards / baoyu-xhs-images

baoyu-xhs-images

Skill card

combined

The two copies describe the same task but split the procedure in a way that hides real alternatives. JimLiu/baoyu-skills is the canonical, versioned skill and contains the operational constraints the other copy lacks: a strict image-backend resolution order (Codex imagegen, codex CLI, Cursor GenerateImage, other native, then non-native), a hard rule to write each prompt to a standalone file before invoking any backend, a batch-generation policy, and explicit prohibitions against substituting SVG/HTML/canvas for raster output and against programmatically repairing rendered text. LeoYeAI/openclaw-master-skills is a bulk mirror that instead documents the user-facing interface: CLI usage, the 10-style and 8-layout galleries, auto-selection heuristics, three outline strategies, and the session file structure. Neither copy is a complete reproduction of the other; crowning one would either drop the backend/prompt-file/batch constraints or drop the style/layout/outline workflow. The copies also disagree on the number of visual styles (12 in JimLiu vs 10 in LeoYeAI) and on the trigger keywords, so a combined instruction is needed.

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Other copies

Combined skill

---
name: baoyu-xhs-images
description: Generates Xiaohongshu (Little Red Book) infographic image card series with multiple visual styles, 8 layouts, and 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", "XHS images", "RedNote infographics", "image cards", "图片卡片", baoyu-xhs-images, or wants social media infographic series.
version: 2.0.1
---

# 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](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.
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.** 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.

**⛔ 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.

**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.

## 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.

## Usage

```bash
# 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 notion --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, warm, bold, minimal, retro, pop, notion, chalkboard, study-notes |
| **Layout** | Information structure: density, arrangement | sparse, balanced, dense, list, comparison, flow, mindmap, quadrant |

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 |
| `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 |

Detailed style definitions: `references/presets/<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) |
| `mindmap` | Center radial mind map layout (4-8 branches) |
| `quadrant` | Four-quadrant / circular section layout |

Detailed layout definitions: `references/elements/canvas.md`

## Auto Selection

| Content Signals | Style | Layout |
|-----------------|-------|--------|
| Beauty, fashion, cute, girl, pink | `cute` | sparse/balanced |
| Health, nature, clean, fresh, organic | `fresh` | balanced/flow |
| 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 |
| Education, tutorial, learning, teaching, classroom | `chalkboard` | balanced/dense |
| Notes, handwritten, study guide, knowledge, realistic, photo | `study-notes` | dense/list/mindmap |

## Outline Strategies

Three differentiated outline strategies for different content goals:

### Strategy A: Story-Driven (故事驱动型)

| Aspect | Description |
|--------|-------------|
| **Concept** | Personal experience as main thread, emotional resonance first |
| **Features** | Start from pain point, show before/after change, strong authenticity |
| **Best for** | Reviews, personal shares, transformation stories |
| **Structure** | Hook → Problem → Discovery → Experience → Conclusion |

### Strategy B: Information-Dense (信息密集型)

| Aspect | Description |
|--------|-------------|
| **Concept** | Value-first, efficient information delivery |
| **Features** | Clear structure, explicit points, professional credibility |
| **Best for** | Tutorials, comparisons, product reviews, checklists |
| **Structure** | Core conclusion → Info card → Pros/Cons → Recommendation |

### Strategy C: Visual-First (视觉优先型)

| Aspect | Description |
|--------|-------------|
| **Concept** | Visual impact as core, minimal text |
| **Features** | Large images, atmospheric, instant appeal |
| **Best for** | High-aesthetic products, lifestyle, mood-based content |
| **Structure** | Hero image → Detail shots → Lifestyle scene → CTA |

## 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 + questions asked
├── outline-strategy-a.md           # Strategy A: Story-driven
├── outline-strategy-b.md           # Strategy B: Information-dense
├── outline-strategy-c.md           # Strategy C: Visual-first
├── outline.md                      # Final selected/merged outline
├── 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-recommend-20250101-120000`