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。
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。
$ npx -y skills add ZeKaiNie/universal-examprep-skill --skill exam-audit --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/exam-auditContext preview
The summary Claude sees to decide when to auto-load this skill.
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。
name: exam-audit description: > 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。 license: MIT
Inspect a prep workspace built by `exam-ingest` and report health issues. This is a read-only inspector. Do NOT fix anything by default; only fix after the user explicitly grants permission. Emit a concrete issue report; never silently modify or delete files.
Activate when the user suspects the workspace is broken (missing chapters, ungradable quiz items, inconsistent progress), or when the user wants a pre-review health check before studying. Do not activate to build, teach, or grade.
Inspect read-only. Open and parse files; never write, rename, or delete. Check each item below and record every failure as a concrete issue (file path + what is wrong).
1. **Structure.** For each phase listed in `study_plan.md`, confirm a matching `references/wiki/chN_*.md` file exists. Flag orphan chapters (wiki files no phase references) and broken links (phases pointing to absent chapters). 2. **Quiz bank.** For each item in `references/quiz_bank.json`: confirm `type` is one of the six allowed types (choice / subjective / diagram / fill_blank / true_false / code); confirm `choice` items carry `options`; treat missing `keywords` on subjective items as a grading-quality warning. An item without an answer must declare `answer_status: unknown`; in a structured workspace it also remains a blocking review issue until an evidence-backed official answer, an explicitly labeled AI answer, or an unrecoverable terminal decision is recorded. 3. **Provenance honesty.** Flag any AI-generated answer presented as the teacher's standard answer (missing the ⚠️ marker). Flag any AI-supplement wiki passage that should carry 🟡 but does not. 4. **Plan/progress consistency.** When `study_state.json` exists, treat it as the source of truth: confirm `study_progress.md` is a faithful render of it (flag drift / stale hand-edits where the md disagrees with the state), and check the state's `phase_checklist` phases map to `study_plan.md`. When no `study_state.json` exists, audit `study_progress.md` directly. Either way, confirm each rendered phase-checkpoint line maps to a phase in `study_plan.md` and every wrong-question ID exists in `references/quiz_bank.json`. Note: the template anchor `<!-- PHASE_CHECKLIST -->` is replaced by `scripts/ingest.py` at generation time and is absent from a correct finished workspace — do NOT report its absence as a problem. 5. **Teaching-example retention.** Prefer `references/teaching_baseline.json`; validate its schema, exact per-chapter mapping, append-only policy, and require every baseline ID to have a same-chapter current snapshot in `references/teaching_examples.json`. Presence of the same ID in `references/quiz_bank.json` is diagnostic overlap only and never substitutes for that teaching snapshot. Only old workspaces without the baseline file may fall back to `ingest_report.json.teaching_example_ids`. It is valid for an ungradable worked example to be absent from the bank if the teaching layer retains it; disappearance from the current teaching layer is a blocking retention gap even when a quiz item survives. Validate the current teaching manifest's IDs, chapter/phase tags, source pages, answer source, and asset paths. Read it per chapter in tutoring; do not treat the whole manifest as a new answer source. 6. **Three-sided visual completeness.** Inspect each denominator separately: `figure_page_index.json.wiki_visual_coverage` for detected material pages embedded in wiki, `image_question_index.json.prompt_suspects` for missing prompt context, and `answer_suspects` for missing answer context. A zero on one side is never evidence that the other two are complete. Require matching `integrity` snapshots and re-hash their declared quiz, teaching, wiki, and asset inputs; stale or missing freshness evidence blocks a new-manifest phase from being complete. Flag NUL/control-byte warnings and missing/capped pages. State that this is deterministic recall coverage, not semantic
Drop your course folder on a coding agent. Get a tutor that teaches from your own slides, shows the figures, quizzes you with your own homework, and remembers where you stopped.
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。
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)→…
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。
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。
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…