---

name: anki-card-maker description: "Extract key knowledge from study materials (text, Markdown, notes) and generate front-question + back-answer flashcards, producing an Anki-compatible CSV file ready for import. Trigger when users mention flashcards, Anki, spaced repetition, need to convert notes into Q&A pairs, or request memory cards or review cards from their study content." license: MIT

anki-card-maker

Automatically extracts knowledge points from study materials and generates flashcards in "front question + back answer" format, outputting a CSV file ready for direct import into Anki.

Two working modes are supported:

• auto mode: Rule-based extraction of definitions, Q&A pairs, lists, and other structured knowledge from Markdown/plain text
• json mode: Accepts pre-constructed JSON flashcard data and formats it as Anki CSV

Quick Start

# Auto-extract flashcards from Markdown notes
python scripts/generate_flashcards.py --input notes.md --output flashcards.csv

# Generate Anki CSV from JSON data (ideal for agent calls)
python scripts/generate_flashcards.py --mode json --input cards.json --output flashcards.csv

# Use via stdin/stdout
cat notes.md | python scripts/generate_flashcards.py > flashcards.csv

Agent Workflow

When a user provides study materials and requests flashcard generation, the recommended workflow is:

1. Read the material: Read the study material file provided by the user
2. Intelligent extraction: Analyze the material content, extract core knowledge points, and generate high-quality Q&A pairs. Follow these principles:
   • Each card focuses on a single knowledge point (minimum information principle)
   • Use precise question format on the front; avoid vague questions
   • Provide concise but complete answers on the back
   • Cover core concepts, definitions, formulas, cause-and-effect relationships, comparisons, etc.
3. Generate CSV: Write the extracted Q&A pairs as JSON, then call the script to convert to Anki CSV
4. Deliver the file: Inform the user of the output path and import instructions

Agent Call Example

Construct extracted knowledge points as a JSON array and convert to CSV via --mode json:

cat <<'EOF' > /tmp/cards.json
[
  {"front": "What is photosynthesis?", "back": "The process by which plants use light energy to convert CO₂ and H₂O into organic matter while releasing O₂", "tags": "biology"},
  {"front": "What is the chemical equation for photosynthesis?", "back": "6CO₂ + 6H₂O → C₆H₁₂O₆ + 6O₂", "tags": "biology"}
]
EOF
python scripts/generate_flashcards.py --mode json --input /tmp/cards.json --output flashcards.csv

Parameters

Parameter	Description	Default
--input, -i	Input file path	stdin
--output, -o	Output CSV file path	stdout
--mode, -m	Extraction mode: auto (rule-based) or json (structured input)	auto
--no-tags	Omit the tags column	tags included
--separator, -s	CSV separator: \t, ;, ,	Tab

Output Format

The generated CSV follows the Anki import specification:

#separator:Tab
#html:true
#columns:Front	Back	Tags
What is photosynthesis?	The process by which plants use light energy to convert CO₂ and H₂O into organic matter while releasing O₂	biology

How to Import into Anki

1. Open Anki → File → Import
2. Select the generated CSV file
3. Anki will automatically detect the separator and column mapping
4. Confirm and click "Import"

Knowledge Structures Supported in Auto Mode

Structure Type	Example	Generated Flashcard
Definition (Term: Definition)	Photosynthesis: Plants use light energy...	Q: What is photosynthesis? A: Plants use light energy...
Q&A pair	Q: What is DNA? A: Deoxyribonucleic acid	Extracted directly as a flashcard
Heading + list	## Organelles - Mitochondria - Ribosome	Q: What are the key points of Organelles? A: List
Heading + paragraph	## Newton's First Law An object at rest...	Q: Explain: Newton's First Law A: Paragraph content

Prerequisites

• Python 3.6+
• No additional dependencies required (uses standard library only)