quiz

---

name: quiz description: Spaced repetition quiz using GraphRAG entities. Generates questions, grades answers, and tracks learning progress with optimal review intervals. disable-model-invocation: false argument-hint: [--topic "topic"] [--count 5] [--project project_name] allowed-tools: Bash, Read

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}