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Knowledge card creator, your memory notes

by @goog

Knowledge Card generator. Extracts key knowledge from user-provided material (text, files, URLs), determines optimal card type (concept/memo/process/comparis...

Versionv1.6.5
Downloads639
Installs1
TERMINAL
clawhub install kcard

πŸ“– About This Skill


name: kcard description: 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 "ηŸ₯识卑片", "kcard", "make a card", "knowledge card", or wants to turn notes/articles into memorable visual cards.

Knowledge Card Generator

Workflow

1. Parse Input Material

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

  • If URL β†’ fetch and extract main content by web_fetch tool
  • If file β†’ 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 β†’ Definition β†’ Analogy β†’ Key Points | | Memo | Steps, commands, configs, references | Title β†’ Ordered Steps β†’ Tips / Gotchas | | Process | Sequential workflow or lifecycle | Title β†’ Phases β†’ Steps per Phase β†’ Output | | Comparison | Compares 2+ items | Dimension β†’ Item A vs Item B β†’ 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__.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 /scripts/render_card.py  [--output ] [--theme ] [--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
  • πŸ“‹ Tips & Best Practices

  • 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