测验阅读笔记 · Reading Notes Quizzer

    1

    把阅读笔记变成可自测的测验:按要点出题、配答案与解析、标注掌握程度,让读过的内容真正记住。 Turn reading notes into self-grading quizzes with key-point questions, answers and explanations, and mastery tracking so what you read actually sticks.

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    Works with the AI tools you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIGitHub CopilotGitHub CopilotGemini CLIGemini CLI+20 more

    测验阅读笔记 · Reading Notes Quizzer

    Example session with this skill installed

    我刚读完《影响力》前三章的读书笔记,想把这部分内容变成自测题,考理解而不是死记硬背。

    • Read your context and instructions
    • Compiled the reading notes quizzer

    测验概览
    基于《影响力》前三章笔记生成 8 道自测题,覆盖互惠、承诺一致、社会认同三个概念;建议先闭卷做,再对照解析订正,最后看掌握度自评表。

    一、选择题(每题 4 选 1)

    1. 「先给试用装再推销」利用的是哪个原理?
      A. 稀缺 B. 互惠 C. 权威 D. 喜好
      答案:B。解析:免费试用让人产生「欠人情」感,更倾向回报,对应互惠原理。
    2. 人们写下公开承诺后更难反悔,这属于哪类机制?
      A. 承诺与一致 B. 社会认同 C. 锚定 D. 损失厌恶
      答案:A。解析:公开承诺会带来保持一致的心理压力。

    二、判断题

    1. 社会认同原理只在紧急场景起作用。(×)
      解析:在不确定、他人相似、人数多等场景都会被激活。
    2. 互惠原理必须对方先给出真实利益。(√)
      解析:哪怕是小的、非自愿接受的好处,也可能触发回报倾向。

    三、简答题

    1. 用一句话解释社会认同,并举一个电商场景的例子。
      参考要点:人在不确定时看别人怎么做;例如商品页展示「10 万人已购」。

    掌握度自评表

    知识点答对建议
    互惠2/2已掌握
    承诺一致1/1已掌握
    社会认同1/2回读第三章「相似性」小节

    下一步

    1. 针对答错的第 3 题回读笔记对应段落;
    2. 三天后用同一套题再测一次,检查留存;
    3. 把简答题答案录成语音,复述给同伴听。

    Connects securely to your tools. The creator never sees your data.

    What you get

    生成符合企业审计要求的阅读笔记自检清单统一团队内部技术术语及跨平台命令输出规范快速校验技术文档是否覆盖权限与合规红线

    About this skill

    它解决什么问题

    读完书和文章,笔记记了一堆,合上就忘。没有自测,大脑会把「看懂了」误当成「记住了」,过一周只剩模糊印象。直接让 AI 出题,常出成浅层的填空题,考不到理解。

    这个技能做什么

    把阅读笔记按「核心概念 → 逻辑关系 → 应用场景」拆成知识点,生成选择题、判断题与简答题三档测验,每题附答案、解析与出处;做完再输出掌握度自评表,标记薄弱点。

    为什么比裸提示词强

    • 考理解不考记忆:题目围绕概念关系与应用。
    • 答案解析齐全:每题有出处与错因说明。
    • 薄弱点可见:自评表标出需要回读的段落。

    典型用例

    • 把一本书的读书笔记变成复习测验。
    • 把课程讲义整理成课后自测题。
    • 团队共读后做互测与讨论题。

    局限

    题目质量取决于笔记完整度,笔记缺失处会标注「待补充」;开放性问题无唯一标准答案。


    English · 英文介绍

    The problem

    After reading books and articles, I took a bunch of notes and forgot about them after closing them. Without self-assessment, the brain will mistake "understood" for "remembered", and a week later there will be only a vague impression. Directly asking AI to give questions often results in shallow fill-in-the-blank questions that fail to understand the test.

    What it does

    Divide the reading notes into knowledge points according to "Core Concepts → Logical Relationships → Application Scenarios" and generate three tests of multiple-choice questions, true-false questions and short-answer questions, with answers, analysis and sources attached to each question; after completion, output the mastery self-evaluation form and mark weak points.

    Why is it better than naked prompt words?

    • Comprehension is not tested but memory is tested: the questions focus on conceptual relationships and applications.
    • The answers are fully analyzed: each question has its source and error explanation.
    • Weak points are visible: the self-assessment form marks passages that need to be read back.

    Typical use cases

    • Turn reading notes from a book into a review quiz.
    • Organize the course notes into post-class self-test questions.
    • After reading together as a team, do mutual tests and discussion questions.

    Limitations

    The quality of the questions depends on the completeness of the notes. Missing notes will be marked "to be supplemented"; there is no single standard answer to open questions.

    How to install

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

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