quiz

name	quiz
description	Spaced repetition quiz using GraphRAG entities. Generates questions, grades answers, and tracks learning progress with optimal review intervals.

Spaced Repetition Quiz

Generate and run quizzes from GraphRAG entities to strengthen long-term memory through retrieval practice, spaced repetition, and interleaving.

Usage

/quiz
/quiz --topic "認知科学"
/quiz --count 3
/quiz --project project_a

Workflow

1. Select Entities for Quiz

Run the quiz selection to get entities due for review:

python ${CLAUDE_SKILL_DIR}/../../../scripts/quiz.py select -k ${COUNT:-5} ${TOPIC_FLAG} ${PROJECT_FLAG}

Where:

• COUNT defaults to 5, override with --count N
• TOPIC_FLAG is --topic "topic" if a topic argument was provided
• PROJECT_FLAG is -p project_name if a project was specified

Parse the arguments from the user's input:

• --topic "X" or bare text → use as --topic
• --count N or -k N → use as -k
• --project X or -p X → use as -p

This returns JSON with entities including: name, type, description, correct/incorrect counts, community, and related entities.

2. Generate Quiz Questions

For each selected entity, generate ONE question. Use these question types, varying across entities:

• Definition: "〜とは何ですか？" (test core understanding)
• Relationship: "〜と〜の関係は？" (use the entity's relations from the selection data)
• Application: "〜を実際にどう活用しますか？" (test practical understanding)
• Comparison: "〜と〜の違いは？" (use related entities from the same community)

IMPORTANT: Present questions ONE AT A TIME. Show the question, wait for the user's answer, then grade before showing the next question.

Show question like this:

**Q1/5** [エンティティ名 (タイプ)]
〜〜〜の質問〜〜〜

3. Grade Each Answer

After the user answers, grade on a 3-level scale using the entity's description and relationships as ground truth:

• Correct (score: 1.0): Core concept accurately captured
• Partial (score: 0.5): Some understanding but key elements missing
• Incorrect (score: 0.0): Fundamental misunderstanding or no answer

Provide brief feedback: what was right, what was missing, and the key point from the graph.

4. Record Each Result

After grading, record the result immediately:

python ${CLAUDE_SKILL_DIR}/../../../scripts/quiz.py record ${PROJECT_FLAG} --json '{"entity_name": "ENTITY_NAME", "is_correct": true/false, "question": "THE_QUESTION", "user_answer": "USER_ANSWER", "score": 0.0-1.0, "feedback": "FEEDBACK"}'

• is_correct: true if score >= 0.5, false otherwise
• Escape JSON strings properly (especially quotes in Japanese text)

5. Show Summary

After all questions, show a summary:

## クイズ結果

| # | エンティティ | 結果 | 次回復習 |
|---|---|---|---|
| 1 | Entity A | ⭕ 正解 | 4日後 |
| 2 | Entity B | ❌ 不正解 | 1日後 |
| 3 | Entity C | △ 部分正解 | 2日後 |

正答率: 2/3 (67%)

How Spaced Repetition Works

• Correct answers → review interval doubles (1→2→4→8→16 days, max 90 days)
• Incorrect answers → interval resets to 1 day
• Entities with more incorrect answers get prioritized
• Never-quizzed entities get medium priority
• Different entity types are mixed (interleaving) to strengthen pattern recognition

Stats

To check quiz statistics, run:

python ${CLAUDE_SKILL_DIR}/../../../scripts/quiz.py stats ${PROJECT_FLAG}