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Exam Review

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ZeKaiNie
exam-review

考前复盘已记录错题与概念疑难:重做原题、复述疑难、更新已订正/已回顾/待回顾状态,并形成最后扫雷清单。 进入最终复习阶段或用户要求复盘、查漏补缺时使用。

Overview

PublisherZeKaiNie
Repositoryuniversal-examprep-skill
Skill nameexam-review
Stars
314
Forks
17
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by ZeKaiNie on GitHub. Read the source before you install it.

Installation

Install the Exam Review AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/ZeKaiNie/universal-examprep-skill/tree/main/full/skills/exam-review
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ZeKaiNie/universal-examprep-skill.git /tmp/universal-examprep-skill
mkdir -p .claude/skills
cp -r /tmp/universal-examprep-skill/full/skills/exam-review .claude/skills/exam-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Exam Review in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Exam Review on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Exam Review is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

exam-review — mistake and confusion review

Purpose

Clear recorded mistakes/confusions before the exam. Replay only existing records; teach no new chapter and invent no question.

Activation

Use at final review or when the student explicitly asks to replay mistakes/find gaps.

Inputs

  • study_state.json's mistake_archive and confusion_log when state exists; otherwise generated 「❌ 错题档案」 and confusion compatibility rows.
  • references/quiz_bank.json, used to fetch each recorded mistake by exact ID.

Workflow

  1. Replay recorded mistakes. Reload state (or update_progress.py show), fetch each exact bank item, and ask it again. Never add an unrecorded/bank-external question.

    For requires_assets=true or maybe_requires_assets=true, before asking, explaining, hinting, or solving, render every question-side question_context / figure / diagram / table asset, labelled 题面图 or Question-side asset. Only later may solution/review show 答案图 / Answer-side asset. A path is not an image. Preserve but never display student_attempt; its physical path is tainted across the complete quiz, teaching, and content-unit layers, including duplicate official-looking declarations. Missing/unreadable or UI-unrenderable prompt assets cause a fail-closed skip; stub / page_reference also require the original prompt page first. Use scripts/show_question_assets.py for every replay and treat a nonzero result as a skip; do not render a raw bank path directly. See exam-quiz and docs/file-format.md §4.

  2. Update mistakes. Correct replay → 已订正; still wrong → explain from the stored explanation and retain it.

  3. Replay confusions. Reload confusion_log; ask the student to restate what/why/how. Correct restatement → 已回顾; vague → explain once and retain 待回顾.

  4. Persist the open list first. Compile unresolved mistakes plus 待回顾 confusions for the final sprint/exam-cheatsheet. Pipe each conclusion/list to python "${CLAUDE_SKILL_DIR}/scripts/notebook.py" --workspace <ws> add-entry --chapter <ch> --type review --id <slug> --title <gist>; same IDs replace and rebuild the index. Then send a digest and language-pack notebook link. If writing fails, say so and give the full list in chat; file-less clients use chat/text breakpoints.

  5. Persist row status. If study_state.json is absent and Python works, run update_progress.py --workspace <ws> init first; only when Python truly cannot run may Markdown rows be changed in place. Write results via update_progress.py set-mistake-status / set-confusion-status, and genuinely new rows via add-mistake / add-confusion. A nonzero Python command is a fail-loud error, not permission to hand-edit. Never leave a mastered row stale or overwrite another skill's writes.

Output Contract

  • Produce one 「还没拿下的清单」 with current 已订正 / 已回顾 / 待回顾 statuses, notebook link, and refreshed panel.
  • Persist conclusions before the digest and persist every status via update_progress.py set-mistake-status / set-confusion-status.
  • Student prose is English by default, Simplified Chinese for a Chinese opening, or explicit bilingual blocks.

Language packs

Display aliases are normalized to zh, en, or bilingual.

Boundaries

  • study_state.json is the source of truth; mutate rows only via update_progress.py. Mark replay results via update_progress.py set-mistake-status / set-confusion-status; set-check alone is not a substitute.
  • Default question scope is mixed. A restricted scope excludes/counts missing source_type; announce any one-turn override first: 「⚠️ 临时覆盖你的 范围偏好」 / ⚠️ Temporarily overriding your <scope> scope preference. Use scripts/select_questions.py.
  • Share confusion rows with confusion-tracker; replay only recorded bank items and never erase concurrent writes.

Frequently asked questions

What does the Exam Review AI skill do?

考前复盘已记录错题与概念疑难:重做原题、复述疑难、更新已订正/已回顾/待回顾状态,并形成最后扫雷清单。 进入最终复习阶段或用户要求复盘、查漏补缺时使用。

Why use Exam Review on TypingMind?

Because you install it once and use it with any model. Exam Review is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Exam Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ZeKaiNie/universal-examprep-skill/tree/main/full/skills/exam-review. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Exam Review?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Exam Review?

As many as you like. As long as a model supports skills, you can use Exam Review with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Exam Review AI skill free?

Yes. It is published on GitHub by ZeKaiNie under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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