Knowledge Card Generator

Workflow

1. Parse Input Material

Accept any of: pasted text, file path, URL, or image.

• If URL → fetch and extract main content by web_fetch tool
• If file → read it

Extract 3–7 core knowledge points. Prioritize: definitions > mechanisms > examples > details.

2. Determine Card Type

Pick the best-fit type based on content nature:

If unsure, default to Concept card.

3. Apply Cognitive Science Principles

Follow these when structuring the card:

• Chunking: Group related info into 3–5 chunks max per section
• Dual Coding: Pair text with a visual metaphor or emoji anchors
• Elaboration: Add a "Why It Matters" or analogy section
• Spaced Repetition Cue: End with a self-test question (❓)
• Progressive Disclosure: Layer from simple to detailed

4. Generate Markdown

Use the template from references/card-templates.md. Output a single Markdown file.

Naming convention: kcard_<topic>_<type>.md (e.g., kcard_react-hooks_concept.md)

Save to user's specified path or default: ~/.openclaw/workspace/kcards/

5. Render to Image

Run the rendering script to convert the Markdown into a PNG:

python <skill-dir>/scripts/render_card.py <path-to-markdown> [--output <output.png>] [--theme <warm|cool|girly|tech>] [--width 800]

Default theme: warm. Default output: same path with .png extension.

The script:

1. Parses Markdown to styled HTML
2. Renders HTML to image via headless browser or html2image
3. Returns the output path

Present the final image to the user.

Output Format

Always output:

1. The Markdown source file (for editing/reuse)
2. The rendered PNG image
3. A brief one-line summary of what the card covers

Notes

• Keep cards concise: one concept per card, maximum 195 words
• Use Chinese or English based on input language
• Emoji anchors are encouraged but keep them minimal (1–3 per section)
• For batch requests, process cards sequentially and summarize all outputs