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Knowledge Cards Generator

从粘贴的文章、本地 Markdown/TXT 文件或 PDF 中提取核心知识点，生成 5-8 张 Markdown 知识卡片；每张含标题、核心知识、解释和示例或自测题。不支持网页抓取、扫描版 PDF、加密 PDF、Anki 导出和图形界面，PDF 文本提取依赖 pypdf。

slyum123
slyum123
热度 13.96
最近更新 2026-08-13 14:16:17
分类：知识依赖：代码执行 / 读写文件 / 内部调用大模型应用场景：摘要总结 / 笔记整理 / 测验

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add make-knowledge-cards
Metadata
name
make-knowledge-cards
version
1.0.0
author
WorkBuddy User
description
Convert pasted articles, local Markdown/TXT files, or PDF documents into 5-8 concise knowledge cards. Each card captures one key concept with a title, core knowledge summary, brief explanation, and an example or self-test question. Trigger when the user asks to make knowledge cards, create study cards, summarize into cards, or provides an article or PDF for card-based learning. Do NOT use for web scraping, Anki export, or graphical interfaces.
agent_created
true

Make Knowledge Cards

Overview

Transform articles, pasted text, local Markdown/TXT files, or PDF documents into structured knowledge cards for efficient review and self-testing. Each card isolates a single knowledge point with enough context to understand and recall it independently.

When to Use

  • User pastes an article and asks to generate knowledge cards
  • User provides a local .md, .txt, or .pdf file path for card generation
  • User explicitly requests "knowledge cards", "study cards", or "flashcards" from text content
  • User wants to review key points from a long-form article or PDF document in a card format

Limitations

The following are NOT supported and should be declined with a clear explanation:

  • Web scraping: Do not fetch URLs or scrape web pages. If a URL is provided, ask the user to paste the article text instead.
  • Scanned PDFs: If a PDF contains only scanned images (no embedded text), extraction will fail. Ask the user to run OCR first and provide the text.
  • Encrypted PDFs: If a PDF is password-protected, ask the user to decrypt it first.
  • Anki export: Do not generate .apkg files or Anki-compatible formats.
  • Graphical UI: Do not render cards as images or interactive interfaces. Output is plain Markdown text only.

Dependencies

PDF text extraction requires the pypdf Python library. Install it before using the PDF feature:

pip install -r skills/make-knowledge-cards/scripts/requirements.txt

If pypdf is not installed and the user provides a PDF, inform them of the dependency and offer to install it, or ask them to paste the text manually.

Workflow

Step 1 — Acquire Source Text

  1. If the user pastes text directly, use it as the source.
  2. If the user provides a local file path:
    • .md or .txt: Read the file content using the Read tool.
    • .pdf: Extract text using the bundled script. Run: 
      python scripts/extract_pdf.py "<pdf_file_path>"
      The script outputs extracted text to stdout. If the exit code is non-zero, check stderr for the error message:
      • Exit code 2: File not found — ask the user to verify the path.
      • Exit code 3: PDF is encrypted — ask the user to decrypt it first.
      • Exit code 4: No text extracted — the PDF may be scanned images only. Ask the user to run OCR and provide the text.
    • Other formats (.docx, .html, etc.): Not supported. Ask the user to paste text or provide a .md / .txt / .pdf file.
  3. If the user provides a URL, explain the limitation and ask for pasted text or a local file.

Step 2 — Analyze Content

  1. Read the full source text carefully before extracting any knowledge points.
  2. Identify the article's main topic and logical structure.
  3. Distinguish between:
    • Core knowledge: Concepts, principles, definitions, causal relationships, and methods that are central to the article's purpose.
    • Supporting detail: Examples, anecdotes, statistics, and background context that illustrate but do not constitute standalone knowledge.
    • Filler: Transitional phrases, repetition, and tangential remarks.
  4. Deduplicate: When the same concept appears multiple times, merge into a single card.

Step 3 — Select Knowledge Points

  1. Extract 5-8 knowledge points from the core knowledge identified in Step 2.
  2. Apply these selection criteria in order of priority:
    • Importance: Does the point matter to the article's central argument or learning objective?
    • Independence: Can the point be understood without reading other cards?
    • Non-redundancy: Is this point already covered by another card?
  3. If the source contains fewer than 5 distinct important knowledge points, produce fewer cards. Do not pad with low-value or repetitive content.
  4. If the source contains more than 8 important points, select the 5-8 most central ones. Briefly note that some points were omitted at the end of the output.
  5. For very long articles (over 5000 words), still produce at most 8 cards. Focus on the article's overarching themes and key conclusions rather than individual section details.
  6. If the user explicitly requests a specific number of cards outside the 5-8 range, honor the request but warn when the requested count exceeds the available distinct knowledge points.

Step 4 — Build Each Card

For every knowledge point, construct a card with exactly these four fields:

Card Fields

Field Description Guidelines
Title Short phrase naming the knowledge point Concise noun phrase or concept name (2-8 words); no full sentences
Core Knowledge One or two sentences stating the key fact or principle Direct and precise; must be derivable from the source text; no fabricated details
Explanation 2-4 sentences unpacking the knowledge point Clarify cause, mechanism, context, or nuance; stay faithful to the source; do not introduce external information
Example / Self-Test Either a concrete example or a self-test question Priority: (1) if the source provides a relevant example, adapt it as "Example:"; (2) otherwise, write a self-test question whose answer requires recalling the core knowledge, labeled as "Self-Test:". Never invent examples not grounded in the source text.

Card Formatting

Render each card as a Markdown block:

---

### Card N: [Title]

**Core Knowledge:** [1-2 sentence summary]

**Explanation:** [2-4 sentence elaboration]

**[Example / Self-Test]:** [content]

Step 5 — Quality Check

Before outputting, verify each card against this checklist:

  • Faithfulness: Every statement in the card is supported by the source text. No fabricated facts, numbers, names, or examples.
  • Singularity: The card covers exactly one knowledge point. If two concepts are bundled, split into two cards or choose the more important one.
  • Independence: The card is understandable without reading other cards. No references like "as mentioned in Card 3".
  • No duplication: No two cards repeat the same point with different wording.
  • Appropriate count: Total cards are 5-8 (or fewer if the source genuinely lacks enough distinct important points).

Step 6 — Output

  1. Output all cards in sequence using the format from Step 4.

  2. After the last card, add a brief summary line:

    ---
*Generated N knowledge cards from [source description].*

  3. If knowledge points were omitted due to the 8-card limit, append:

    *Note: Some secondary points were omitted to stay within the card limit.*

Output Language

  • If the source text is in Chinese, produce cards in Chinese.
  • If the source text is in English, produce cards in English.
  • If the source is mixed, match the dominant language.

Example Output

---

### Card 1: 间隔重复原理

**Core Knowledge:** 间隔重复是一种学习策略，通过逐渐增加复习间隔来强化长期记忆。

**Explanation:** 该方法基于遗忘曲线理论——记忆会随时间自然衰退，但在即将遗忘时复习能有效巩固记忆。每次成功回忆后，下一次复习的间隔适当延长，从而以最少的时间投入维持记忆。

**Self-Test:** 为什么在"即将遗忘时"复习比"频繁复习"更高效？

---

### Card 2: 主动回忆

**Core Knowledge:** 主动回忆是指学习者主动从记忆中提取信息，而非被动地重新阅读材料。

**Explanation:** 研究表明，主动提取信息的过程本身就能强化记忆通路。与反复阅读相比，自我测试或尝试回忆能显著提升长期记忆效果，即使回忆时出现错误。

**Example:** 读完一段文章后合上书，尝试用自己的话复述核心观点，而不是再读一遍。

---

*Generated 2 knowledge cards from "高效学习方法" article excerpt.*