A professional Python-driven rendering engine that converts Markdown text into high-quality, styled 3:4 aspect ratio images for social media platforms.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install card-renderer
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install card-renderer using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Card Renderer?
Card Renderer is a specialized design automation tool designed to bridge the gap between technical writing and visual content creation. By leveraging the power of Python and the Pillow library, it allows developers and creators to programmatically transform standard Markdown files into aesthetically pleasing image cards optimized for platforms like Xiaohongshu. This skill is a core part of the Openclaw Skills library, enabling users to maintain a high-quality visual brand without manual graphic design work.
The tool features a wide array of visual themes ranging from tech-centric VS Code and Mac Pro styles to artistic Bauhaus and Cyberpunk aesthetics. It handles the complexities of typography, layout, and pagination automatically, ensuring that even long-form content is presented clearly across multiple detail cards while maintaining a consistent 3:4 (1080x1440) aspect ratio.
Card Renderer Use Cases
- Creating viral technical content for social media by converting Markdown tutorials into visual carousels.
- Generating professional code snippet cards using the VS Code or Mac Pro geek-style themes.
- Sharing structured educational insights using the Minimal Grid or Bauhaus design systems.
- Producing elegant daily journals or quotes using Vintage parchment and Polaroid styles.
- Automating the creation of marketing assets for AI technology updates and developer showcases.
How Card Renderer Works
- The skill accepts a primary title, a decorative subtitle, and the file path to the source Markdown or text content.
- A specific Python rendering script is selected based on the desired visual theme (e.g., Cyberpunk, Dreamy, or Magazine).
- The engine strips unsupported characters and emojis to ensure rendering stability using the PIL library.
- It generates a high-resolution cover card (1080x1440) featuring the main branding and titles.
- Long content is automatically passed through a pagination logic to create sequential detail cards.
- The final PNG files are saved to the designated output directory and referenced via a media preview tag for immediate viewing.
Card Renderer Setup
The skill requires Python 3 and the Pillow image processing library. It is optimized for macOS to utilize built-in system fonts like STHeiti and Monaco.
pip install Pillow
To render a card, execute the script for your chosen style from the CLI:
python3 scripts/render_mac_pro_card.py "Your Main Title" "Your Subtitle" "path/to/content.md" "/path/to/output_folder"
Card Renderer Data Schema & Taxonomy
The skill produces a structured set of image assets based on the input text. It follows a specific naming convention to distinguish between the cover and the content body.
| File Name | Role | Aspect Ratio |
|---|---|---|
*_cover.png |
Primary branding/title card | 3:4 (1080x1440) |
*_detail_N.png |
Paginated content cards (1, 2, 3...) | 3:4 (1080x1440) |
Input Format |
Markdown (.md) or Text (.txt) | N/A |
Card Renderer Advanced Features
- Smart Pagination: Automatically splits long text into multiple readable cards without breaking sentences mid-flow.
- Diverse Theme Library: Includes 11+ unique visual styles including Glassmorphism, Swiss Magazine, and Retro Typewriter.
- Openclaw Skills Integration: Built-in support for MEDIA tag printing to enable instant image previews in AI chat interfaces.
- High-Fidelity Rendering: Fixed 1080x1440 resolution ensures crisp visuals on mobile retina displays.
- Extensible Architecture: Developers can create custom styles by following the internal render_cover and render_detail Python patterns.
SKILL.md
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