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quiz-generator测验生成器

Agent Skill

quiz-generator 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

1,420

周安装

58

GitHub Stars

158

下载量

459
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:quiz-generator(测验生成器)
来源仓库:https://github.com/panaversity/agentfactory
仓库路径:skills/quiz-generator
安装命令:
npx skills add https://github.com/panaversity/agentfactory --skill quiz-generator
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/panaversity/agentfactory --skill quiz-generator

简介

quiz-generator 用于处理 GitHub 仓库、Issue 和 Pull Request 协作信息。

  • 适合围绕仓库状态、代码变更或协作事项进行整理和分析。
  • 可结合原始 README 和安装命令进一步验证具体用法。
  • 使用前应确认权限范围、维护状态及是否涉及联网或文件操作。
  • 建议核对来源仓库状态,避免推荐未经安全评估的第三方能力包。

SKILL.md

Quiz Generator

Quick Start

# 1. Generate 50 questions for chapter
# Focus on conceptual (75%+ Apply level), not recall

# 2. Redistribute answers evenly
python scripts/redistribute_answers_v2.py quiz.md A

# 3. Validate option lengths (±3 words per question)
# Manually count words for ALL 50 questions

Persona

You generate college-level conceptual quizzes that test understanding, not memorization. Your goal is 50 comprehensive questions covering all chapter concepts with immediate feedback per answer.

Fixed Constraints

question_count: 50  # Comprehensive bank
questions_per_batch: 15-20  # Displayed per session
options_per_question: 4  # Always exactly 4
correct_answer_distribution: ~12-13 per index (0-3)
feedback_timing: immediate  # After each answer
passing_score: NONE  # No threshold
file_naming: ##_chapter_##_quiz.md

Analysis Questions

1. Is this conceptual (not recall)?

TypeExampleValid?
Recall"What is a Python list?"
Conceptual"Which operation reveals a mutability issue?"

Target: 75%+ at Apply level or higher

2. Are options equal length (±3 words)?

OptionsWordsValid?
A: "Yes" / B: "It processes async"2 vs 4
A: "Yes" / B: "The framework processes requests asynchronously"2 vs 6

Rule: ALL options within ±3 words to prevent pattern-guessing

3. Are answers evenly distributed?

IndexCountValid?
012-13
112-13
212-13
312-13

Rule: No 3+ consecutive same index, no obvious patterns

Principles

Principle 1: 50 Questions Required

  • Comprehensive coverage (all chapter concepts)
  • Spaced repetition (different questions each retake)
  • Component shuffles and displays 15-20 per session

Principle 2: Immediate Feedback

Show after EACH answer (not at end):

  • ✅ Correct option highlighted (green)
  • ❌ Why wrong (if incorrect)
  • Explanation (100-150 words)

Principle 3: Address All Options

Every explanation must cover:

  1. Why correct is correct (2-3 sentences)
  2. Why each distractor is wrong (1-2 sentences × 3)
  3. Real-world connection (1-2 sentences)

Principle 4: Source Attribution

source: "Lesson 1: Understanding Mutability"

Links each question to specific lesson for review.

Quiz Component Format

---
sidebar_position: 5
title: "Chapter X: [Topic] Quiz"
---

# Chapter X Quiz

Brief intro (1-2 sentences).

<Quiz
  title="Chapter X Assessment"
  questions={[
    {
      question: "Conceptual question here?",
      options: [
        "Option A (4-6 words)",
        "Option B (4-6 words)",
        "Option C (4-6 words) ← CORRECT",
        "Option D (4-6 words)"
      ],
      correctOption: 2,  // Index 0-3, NOT 1-4!
      explanation: "Why C is correct (2-3 sentences). Why A is wrong (1-2 sentences). Why B is wrong. Why D is wrong. Real-world connection.",
      source: "Lesson 1: Topic Title"
    },
    // ... 49 more questions (total: 50)
  ]}
  questionsPerBatch={18}
/>

Answer Redistribution

LLMs struggle with even distribution. Use the script after generation:

python scripts/redistribute_answers_v2.py quiz.md A

Sequences A-H provide different distributions (~12-13 per index).

What it does:

  1. Parses quiz questions
  2. Swaps option positions to match sequence
  3. Updates explanations to reference new positions
  4. Validates all explanations match correct answers

Option Length Validation (CRITICAL)

Problem: Unequal lengths let students guess by picking longest/shortest.

Solution: Manually count words for EVERY option in EVERY question.

✅ PASS: 4, 5, 4, 5 words (all within ±3)
❌ FAIL: 2, 4, 11, 3 words (2 to 11 = 9-word spread)

Also verify:

  • Longest option correct in ~25% (not biased)
  • Shortest option correct in ~25% (not biased)

Common Pitfalls

PitfallWrongRight
Question count<50 questionsExactly 50
Index valuescorrectOption: 4correctOption: 3 (0-3)
Missing sourceNo source fieldsource: "Lesson N: Title"
Passing scorepassingScore={70}No prop (removed)
Recall questions"What is X?""Which reveals X issue?"
Weak explanationsOnly explains correctAddresses all 4 options
Answer patterns0,1,2,3,0,1,2,3...Random, ~12-13 per index
Option lengths2 vs 11 wordsAll within ±3 words

File Naming

Pattern: ##_chapter_##_quiz.md

ChapterLessonsFilename
2405_chapter_02_quiz.md
5607_chapter_05_quiz.md
14506_chapter_14_quiz.md

Handoff Checklist

Content:

  • 50 questions (not fewer)
  • 75%+ Apply level or higher
  • All major topics covered
  • No recall questions

Distribution:

  • correctOption uses 0-3 (not 1-4)
  • ~12-13 per index
  • No 3+ consecutive same index

Option Lengths:

  • ALL options counted (all 50 questions)
  • ALL within ±3 words
  • Longest not biased toward correct
  • Shortest not biased toward correct

Explanations:

  • 100-150 words each
  • Explains why correct
  • Addresses each distractor
  • Real-world connection

Format:

  • Valid JSX syntax
  • Exactly 4 options per question
  • source field on all 50
  • NO passingScore prop
  • File named correctly

If Verification Fails

  1. Run redistribution script: python scripts/redistribute_answers_v2.py quiz.md A
  2. Re-count option lengths manually
  3. Check explanation references match correctOption
  4. Stop and report if issues persist after 2 attempts

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

36.12%
按下载量换算166

Claude

32.21%
按下载量换算148

Cursor

18.63%
按下载量换算86

Gemini CLI

9.41%
按下载量换算43

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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