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Exam Cheatsheet

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ZeKaiNie
exam-cheatsheet

全员通关后把 错题本+笔记本+知识点窗口+wiki 编译成考前速记小抄 cheatsheet.md(每条要点带可溯源 锚点),并在视觉产物模式或用户明确要求 PDF/打印版时按指定页数渲染成打印级 PDF:按「必背结论/公式 → 有难度例题(必要时含题面图)→ 例题解答(代入公式、保留基础过程)→ 要点解释(同类题怎么办)」 四段组织。当复习收尾、用户要「考前小抄/速记/总结/打印版」时使用。

Overview

PublisherZeKaiNie
Repositoryuniversal-examprep-skill
Skill nameexam-cheatsheet
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 Cheatsheet 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-cheatsheet
  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-cheatsheet .claude/skills/exam-cheatsheet
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Exam Cheatsheet 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 Cheatsheet 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 Cheatsheet 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-cheatsheet — pre-exam cheatsheet compiler

Purpose

Compile, rather than free-generate, mastered content into workspace-root cheatsheet.md. Every top-level bullet must link into notebook/, mistakes/, or references/wiki/. Do not teach new material or invent questions. Render the requested-page-count PDF only for standing visual mode or an explicit PDF/print request. Never write the retired walkthrough.md; leave an existing copy untouched.

Activation

Trigger on an explicit request for 「考前小抄 / 速记 / 总复习」, or when review is wrapping up after all phases and persisted artifact_mode=visual. Automatic final review under chat stays a conversational exam-review summary.

Inputs

  • Weak-spot source: study_state.json (mistake_archive, confusion_log, and phase_checklist) when it exists; otherwise the possibly stale generated study_progress.md. Read these first, then mistakes/index.md and notebook/index.md when present; their full entries provide preferred ready-made anchors.
  • Rank knowledge_window status out_window above in_window and verified (codes are defined by scripts/i18n.py).
  • Read core conclusions and formulas from every mastered chapter in references/wiki/, derived from study_state.json's current_phase/phase_checklist when it exists, otherwise study_progress.md, checked against study_plan.md. Lazy-load one chapter at a time.
  • Use references/quiz_bank.json for teacher-flagged items and answer frameworks. Resolve scripts/select_hard_questions.py from ${CLAUDE_SKILL_DIR}, never the student workspace; it returns a flat ranked list which the agent groups by knowledge point.

Workflow

  1. Gate artifacts. Read study_state.json.artifact_mode; missing, legacy, or unknown means chat. Never infer a subscription tier or add a fourth required first-contact question. Automatic chat review creates no sheet; an explicit sheet request may create Markdown. Only standing visual or an explicit one-shot PDF/print request authorizes rendering. A one-shot request does not modify the persisted value. Never install dependencies or skills silently.
  2. Build the skeleton. Weak spots come first. Per chapter retain only high-frequency or high-scoring formulas, conclusions, and one-sentence definitions.
  3. Select one hard example per key point. For each mastered chapter run python "${CLAUDE_SKILL_DIR}/scripts/select_hard_questions.py" --workspace <ws> --chapter <N> --mode 查缺补漏 -n <M> --json. Both --chapter and --mode are required: they avoid a missing-range failure in 某章起步补弱 and override easy-first 零基础从头讲. Set <M> at least to the bank length so the default top ten cannot starve later points. Group the flat result, prioritize points linked to mistakes/confusions, and choose the hardest candidate per point. With no linked bank item, emit 「无题库例题」 and only the 「必背结论/公式」 and 「要点解释」 sections; never invent a replacement.
  4. Fail closed on prompt assets. For requires_assets=true or maybe_requires_assets=true, embed every question_context, figure, diagram, and table as workspace-relative references/assets/ links, labeled 题面图 for zh/bilingual or Question-side asset for en. Preserve but never embed student_attempt; one declaration taints the same physical path across the complete quiz, teaching, and content-unit layers, including a duplicate official-looking declaration. Missing or unusable assets require a self-contained alternative. A stub or page_reference item likewise needs its original-page render or replacement by a full item. Never include an example whose prompt figure/page is invisible. cheatsheet_render.py performs the shared three-layer policy and canonical-path gate; do not bypass it with a custom Markdown/image renderer.
  5. Write the four sections. The worked solution states the formula, substituted values, and result; only intermediate arithmetic may be omitted. The takeaway starts with the recognition cue and then the answer framework. Material-backed lines may remain unlabeled; AI supplements require 🟡 AI补充,可能与你老师讲的不完全一致, AI answers require ⚠️ AI生成答案,非老师/教材提供, and missing/unknown bank answer provenance requires 「来源未知」. Do not let uncertain content inherit the material default; see docs/language-policy.md.
  6. Attach traceability. End every top-level - bullet with [→](notebook/chNN.md#<anchor>), [→](mistakes/chNN.md#<anchor>), or [→](references/wiki/<file>.md), preferring notebook/mistake evidence. Run python "${CLAUDE_SKILL_DIR}/scripts/validate_workspace.py" <ws> and fix every untraced or dead link before delivery.
  7. Write only when authorized. Create workspace-root cheatsheet.md with the four sections for every mastered chapter and a refreshed progress panel. Under chat, this requires an explicit sheet request.
  8. Render only when authorized. For standing visual or explicit one-shot PDF/print, ask for the page count if omitted (default 2), then run python "${CLAUDE_SKILL_DIR}/scripts/cheatsheet_render.py" --workspace <ws> --pages <N>. Exit 0 must produce exactly N print-safe pages with margins ≥12 mm. Exit 3 returns cheatsheet.html plus the emitted print instruction. Visually inspect the result; adjust --font-size, not margins, until it fits N pages and the last page has at most about 15% blank. Under ordinary chat, stop after validated Markdown and do not ask for page count.
  9. Never invent teacher emphasis; only material-flagged points may be described that way.

Output Contract

  • cheatsheet.md uses active-language headings per mastered chapter: zh uses 「必背结论/公式」→「例题」→「例题解答」→「要点解释」; en uses Must-memorize conclusions & formulas → Worked example → Worked solution → Takeaway. Every bullet is traced and validation passes.
  • An explicit chat request delivers Markdown only unless it also requests print/PDF. Authorized rendering delivers exact-page-count cheatsheet.pdf, or cheatsheet.html plus print instructions on the no-browser path.
  • Student-facing output defaults to English (Simplified Chinese if the student opened in Chinese). Persisted language values zh, en, and bilingual select single-language or mirrored output per docs/language-policy.md.

Language packs

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

Boundaries

  • Unsupported content needs the applicable 🟡 or ⚠️ label. The sheet compresses completed review; it never bypasses source labels or the quiz_bank-only rule.

Frequently asked questions

What does the Exam Cheatsheet AI skill do?

全员通关后把 错题本+笔记本+知识点窗口+wiki 编译成考前速记小抄 cheatsheet.md(每条要点带可溯源 锚点),并在视觉产物模式或用户明确要求 PDF/打印版时按指定页数渲染成打印级 PDF:按「必背结论/公式 → 有难度例题(必要时含题面图)→ 例题解答(代入公式、保留基础过程)→ 要点解释(同类题怎么办)」 四段组织。当复习收尾、用户要「考前小抄/速记/总结/打印版」时使用。

Why use Exam Cheatsheet on TypingMind?

Because you install it once and use it with any model. Exam Cheatsheet 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 Cheatsheet in TypingMind?

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

Which AI models can use Exam Cheatsheet?

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 Cheatsheet?

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

Is the Exam Cheatsheet 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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