/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。
$ npx -y skills add ZeKaiNie/universal-examprep-skill --skill exam-cheatsheet --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/exam-cheatsheet
Context preview
The summary Claude sees to decide when to auto-load this skill.
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。
SKILL.md
exam-cheatsheet.SKILL.mdname: exam-cheatsheet
description: >
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。
license: MIT
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`](../../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 & f
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name: exam-cheatsheet description: > 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。 license: MIT
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`](../../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 & f
Turn your slides, homework, and past papers into a source-aware tutor that remembers your progress.
Other skills on universal-examprep-skill.
- /confusion-tracker
教学过程中自动捕获和记录学习者的概念疑难点("为什么/是什么/怎么推/什么意思"类型的问题),保存到进度文件的"概念疑难点记录"区,形成考前回顾清单。
Open skill - /exam-audit
只读检查一个已生成的备考工作区是否健康并报告问题,默认不做任何修改。核对 .ingest 原材料版本、 内容单元、接管队列与派生产物完整性,以及 wiki、题库、视觉证据、计划和进度的一致性。当用户怀疑 工作区有问题、建库 readiness 被阻断、或想在开始复习前体检时使用。
Open skill - /exam-cram
临考前的极速备考总教练。把课件、大纲、重点与真题建成分章 wiki 和标准题库,再组织惰性授课、 题库判分、错题与疑难复盘及可选考前小抄,并持久化进度。用于期末、备考、突击、刷题、划重点、 错题与考前复习;不用于长期规划或与考试无关的写作/编程。
Open skill - /exam-help
备考教练的一屏速查卡:工作流、3×4 学习选择、产物偏好、工作区文件、6 大题型、来源规则与子技能路由。 用户问怎么用、有哪些模式、文件用途或支持题型时使用。
Open skill - /exam-ingest
从学生上传的课件/大纲/老师勾的重点/真题,一键初始化并验证备考工作区:解析 PDF、DOCX、PPTX、 XLSX、常见独立图片与 txt/md,建立分章节 LLM Wiki、标准题库、结构化接管队列与进度状态;仅在 Python 确实无法运行时 明确降级为手动写盘。当工作区尚未建立、资料发生变化、或建库 readiness 被阻断时使用。
Open skill - /exam-quiz
从 references/quiz_bank.json 抽取本章题目并按标准答案判分,支持选择、主观、画图、填空、判断、代码; 主观题按 keywords 要点覆盖判分,连续错两次提供提示/跳过/归档。禁止现场编题。用于阶段检查或模考。
Open skill

