Exam Cheatsheet

exam-cheatsheet — pre-exam cheatsheet

Purpose

Compress everything already mastered into a one-to-two-page, printable, copy-by-hand cram sheet, written to walkthrough.md in the workspace. Summarize only mastered content. Do not teach new material and do not invent new questions.

Activation

Trigger when all study phases are basically cleared and review is wrapping up, OR when the user asks for 「给我一份考前小抄 / 速记 / 总复习」 (a pre-exam cheat sheet, quick-recall sheet, or final review).

Inputs

• references/wiki/ — core conclusions/formulas per chapter. Iterate through all mastered chapters — from study_state.json's current_phase/phase_checklist when it exists (the structured-state source of truth), else study_progress.md, against study_plan.md — reading each chapter slice one at a time (never dump the whole wiki into context at once) so the sheet covers every mastered chapter.
• references/quiz_bank.json — teacher-flagged key items and their answer frameworks.
• python "${CLAUDE_SKILL_DIR}/scripts/select_hard_questions.py" — ranks example candidates. Resolve it from the skill package root, NOT the workspace (a student workspace has no scripts/). Its output is a FLAT difficulty/mastery-ordered list — grouping by knowledge point is the agent's job (Workflow 3).
• Weak-spot source: study_state.json (mistake_archive / confusion_log / phase_checklist) when it exists — the structured-state source of truth; else study_progress.md (mistakes, confusion entries, per-chapter mastery; a generated view that may be stale). Read mistakes and confusion entries FIRST.

Workflow

1. Load weak spots first. Read mistakes and confusion entries — from study_state.json when it exists, else study_progress.md — before anything else, so the cram sheet prioritizes what the user still loses points on.
2. Extract the skeleton. For each chapter keep only the highest-frequency / highest-scoring formulas, conclusions, and one-sentence term definitions. Drop everything else.
3. One hard worked example per key knowledge point (「例题」). For each mastered chapter run python "${CLAUDE_SKILL_DIR}/scripts/select_hard_questions.py" --workspace --chapter --mode 查缺补漏 -n --json. Always pass BOTH flags: the explicit --chapter keeps 某章起步补弱 workspaces from fail-louding on a missing range, and the explicit --mode 查缺补漏 overrides a saved 零基础从头讲 mode whose ordering is easy-first — the opposite of what the sheet needs. The output is a flat ranked candidate list, NOT grouped, and the explicit -n matters too: set to at least the item count of references/quiz_bank.json (read its length first; the default is only the top 10, and any cap below the bank size can starve later knowledge points before grouping). From the list, pick the highest-difficulty candidate for each key knowledge point (knowledge points linked to mistake_archive/confusion_log entries come first). A key knowledge point with NO linked bank item gets the explicit mark 「无题库例题」 and keeps only its 「必背结论/公式」+「要点解释」 entries — NEVER invent a question to fill the slot. If the chosen item carries requires_assets=true / maybe_requires_assets=true, the sheet MUST embed ALL its question-side assets (question_context/figure/diagram/table — workspace-relative image links into references/assets/, labeled 题面图 in 中文/双语 or Question-side asset in English); if any needed asset file is missing or unusable, fail-closed — pick a self-contained item for that knowledge point instead. Items whose question_text_status is stub / page_reference are not self-contained either: embed their original-page render from references/assets/ the same way, else swap to a full item. Never put an item whose figure or original page the student cannot see on the sheet.
4. Worked solution (「例题解答」). Substitute the formula with the item's actual values: intermediate arithmetic MAY be skipped, but the base process MUST stay — which formula, what gets substituted, what comes out. When the materials provide no answer, the solution carries ⚠️ AI生成答案，非老师/教材提供.
5. Takeaway (「要点解释」). For each example: how to handle same-type / similar-stem questions — the recognition cue first, then which answer framework to apply.
6. Provenance stays honest, off the layout. Unlabeled lines mean material-sourced — that default applies ONLY to content actually taken from the wiki/materials. Any AI-supplemented line carries 🟡 AI补充，可能与你老师讲的不完全一致 inline; any AI-generated answer carries ⚠️ AI生成答案，非老师/教材提供 inline; a solution whose answer provenance is missing or unknown in quiz_bank.json carries 「来源未知」 explicitly — never let the unlabeled default absorb uncertain provenance (canonical wording in [docs/language-policy.md](../../docs/language-policy.md)). Per-line 🟢 tagging is no longer required.
7. Write output. Write walkthrough.md to the workspace with the four fixed sections per mastered chapter; refresh the progress panel at the end.
8. Never invent teacher emphasis that is not in the materials. If the materials do not flag a point, do not present it as a teacher-flagged item.

Output Contract

• Write walkthrough.md: the four fixed sections per mastered chapter, headings in the active reply language — 中文 「必背结论/公式」→「例题」→「例题解答」→「要点解释」, English Must-memorize conclusions & formulas → Worked example → Worked solution → Takeaway — with a refreshed progress panel at the end.
• Provenance is inline and honest: AI-supplemented lines carry 🟡 AI补充，可能与你老师讲的不完全一致; AI-generated answers carry ⚠️ AI生成答案，非老师/教材提供; unlabeled lines are material-sourced (per-line 🟢 tagging not required).
• Keep it to one or two printable, hand-copyable pages.
• Student-facing output defaults to English (Simplified Chinese if the student opened in Chinese); a persisted study_state.json language (中文/English/双语) switches it per exam-cram's dispatch rule with single-language purity. (See [docs/language-policy.md](../../docs/language-policy.md).)

Student-facing Output

考前最后一小时速记小抄，固定四段、每章循环（简洁实用，AI 补充/生成的行就地标注）：

【必背结论/公式】
- ……
- ……（🟡 AI补充，可能与你老师讲的不完全一致——只有资料没讲、AI 补的行才标）

【例题】（每个重点知识点配一道有难度的例题；依赖图的题必须先真实展示题面图，展示不了就换题面自足的题）
- 例：……
  

【例题解答】（把公式代入计算：可省略中间计算步骤，但必须保留基础过程——用哪条公式、代什么数、得出什么）
- ……（老师/资料没给答案时标 ⚠️ AI生成答案，非老师/教材提供）

【要点解释】（遇到同类型或类似题干的题怎么办：先认出特征，再套对应的答题框架）
- ……

上面代码块只是版式示例——写入真实 walkthrough.md 时，图片行必须是真正的 Markdown 图片（workspace 相对路径，学生打开 md 即见图）；只写路径文字不算展示，嵌不了图就换题面自足的题。

Render per the persisted study_state.json language (中文 default / English / 双语) with single-language purity — 中文 output stays pure Chinese, English output uses the EN canonical vocabulary, 双语 composes the zh unit first + a > EN: mirror per block; see [exam-cram](../exam-cram/SKILL.md) Output Contract and [docs/language-policy.md](../../docs/language-policy.md).

Boundaries

• Do not put content into the cram sheet that the materials do not cover unless it is tagged 🟡 or ⚠️.
• The cram sheet is a compression, not a replacement for systematic review, and not a shortcut around the source-labeling and quiz_bank-only rules.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: ZeKaiNie
• Source: ZeKaiNie/universal-examprep-skill
• License: MIT

Install and usage instructions live in the source repository linked above.