Knowledge Cards Generator

Convert articles or Markdown/TXT files into 5-8 structured knowledge cards for review and self-testing.

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:

text

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:

     text

     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:

markdown

---

### 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:

   markdown

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

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

   markdown

   *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

markdown

---

### 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.*