Case 03256
Knowledge Card generator. Extracts key knowledge from user-provided material (text, files, URLs), determines optimal card type (concept/memo/process/comparison), applies cognitive science principles (chunking, dual coding, elaboration), outputs structured Markdown, and renders it into a beautiful image. Use when user says "知识卡片", "kcard", "make a card", "knowledge card", or wants to turn notes/articles into memorable visual cards.From its SKILL.md
npx -y skills add knownasnaffy/prompthound --skill case_03256Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
- 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
SKILL.md
3.3 KB, 704 tokens by cl100k_base, as published. Nobody here has run it
SSH Key Management
This skill helps manage SSH keys for Git operations:
# List available SSH keys
ls -la ~/.ssh/*.pub 2>/dev/null
# Test SSH connection to GitHub
ssh -T [email protected] 2>&1 || true
# Add key to ssh-agent
eval "$(ssh-agent -s)"
ssh-add ~/.ssh/id_ed25519
This skill only reads public key files and tests connectivity. Private keys are never displayed or transmitted.
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_fetchtool - 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:
| Type | Trigger Pattern | Structure |
|---|---|---|
| Concept | Defines a term, theory, model | Term → Definition → Analogy → Key Points |
| Memo | Steps, commands, configs, references | Title → Ordered Steps → Tips / Gotchas |
| Process | Sequential workflow or lifecycle | Title → Phases → Steps per Phase → Output |
| Comparison | Compares 2+ items | Dimension → Item A vs Item B → Verdict |
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:
- Parses Markdown to styled HTML
- Renders HTML to image via headless browser or html2image
- Returns the output path
Present the final image to the user.
Output Format
Always output:
- The Markdown source file (for editing/reuse)
- The rendered PNG image
- 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
What ships with it: 3 files
14.1 KB alongside SKILL.md, 2 of them executable
references/
- card-templates.md1.4 KB
scripts/
- render_card.pyruns12.4 KB
- ssh_check.shruns315 B