Agent skill

Exam Audit

by ZeKaiNie in ZeKaiNie/universal-examprep-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。

MITAuto-check passedEducation

Install Exam Audit

skills CLI
$ npx skills add ZeKaiNie/universal-examprep-skill --skill exam-audit -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ZeKaiNie/universal-examprep-skill exam-audit --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ZeKaiNie/universal-examprep-skill.git skills-src && mkdir -p .claude/skills && cp -r skills-src/full/skills/exam-audit .claude/skills/exam-audit && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
exam-audit
GitHub stars
303
Token cost
~2.6k tokens
SKILL.md length
1,239 words
Files
1
Skills in repo
12
Repo updated
First seen
Licence
MIT

At a glance

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。

  • Works in 9 steps: Structure. For each phase listed in… → Quiz bank. For each item in… → Provenance honesty. Flag any… → …
  • Tasks that involve Study guides and flashcards
  • SKILL.md covers Purpose, Activation, Inputs and Workflow, plus 3 more sections
  • Calls python

What it does

Exam Audit is an agent skill from ZeKaiNie/universal-examprep-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。

Its SKILL.md is about 2.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Education, covering Study guides and flashcards. The repository describes itself as: Exam Cram Coach · 跨会话记忆与讲义溯源防幻觉的极速备考教练 | AI exam-prep tutor for Claude Code, Cursor, Codex, Antigravity: teaches from slides with page citations, crops figures, quizzes with real…. The licence is MIT.

When your agent uses it

  • Tasks that involve Study guides and flashcards

Example prompts

  • “/exam-audit”

Requirements

  • Python 3

Workflow steps

9 steps, taken from the first numbered list in SKILL.md.

  1. Structure. For each phase listed in study_plan.md, confirm a matching references/wiki/chN_*.md file exists. Flag orphan chapters (wiki…
  2. Quiz bank. For each item in references/quiz_bank.json: confirm type is one of the six allowed types (choice / subjective / diagram /…
  3. Provenance honesty. Flag any AI-generated answer presented as the teacher's standard answer (missing the ⚠️ marker). Flag any…
  4. Plan/progress consistency. When study_state.json exists, treat it as the source of truth: confirm study_progress.md is a faithful render…
  5. Teaching-example retention. Prefer references/teaching_baseline.json; validate its schema, exact per-chapter mapping, append-only policy…
  6. Three-sided visual completeness. Inspect each denominator separately: figure_page_index.json.wiki_visual_coverage for detected material…
  7. Structured ingestion integrity. When .ingest/ exists, strictly load every source, unit, mapping, issue, patch, unbound entry, and…
  8. Evidence-gated phase completion. In a structured workspace, a checked phase must carry valid phase_evidence plus a currently validated…
  9. Path safety. Flag traversal, symlink escapes, and absolute paths in fields that are defined as workspace-relative…

What it can do on your machine

Read from SKILL.md and the folder at commit b9e84f5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Exam Audit loads about 2.6k tokens when it runs. Until then it costs about 38 tokens; SKILL.md has 1,239 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~38
When it runs · the whole SKILL.md, loaded when a task matches
~2.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from ZeKaiNie/universal-examprep-skill at commit b9e84f5, republished under its MIT licence (© ZeKaiNie). 1,239 words, ~2,596 tokens.

Download SKILL.mdSave it as .claude/skills/exam-audit/SKILL.md (or your agent's skills folder).
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

exam-audit — workspace health check (read-only)

Purpose

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.

Activation

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.

Inputs

  • references/wiki/ — chapter knowledge files (chN_*.md).
  • references/quiz_bank.json — quiz items.
  • references/teaching_examples.json — optional parallel teaching inventory; examples may remain here even when they are deliberately excluded from the gradable bank.
  • references/teaching_baseline.json — append-only retention baseline for new workspaces; it must never shrink on re-ingest and must not be hand-edited.
  • references/figure_page_index.json / references/image_question_index.json — regenerable three-sided visual coverage evidence (wiki / prompt / answer).
  • .ingest/source_manifest.json — source-root-relative paths, source hashes, media types, and parse status.
  • .ingest/base_content_units.jsonl / content_units.jsonl — deterministic extraction baseline and the compiled view after replaying validated patches.
  • .ingest/base_chapter_phase_mappings.jsonl / chapter_phase_mappings.jsonl — explicit chapter-to-study-phase identity.
  • .ingest/review_queue.jsonl / review_patches.jsonl — typed issue lifecycle and append-only evidence-bound patch ledger.
  • .ingest/build_manifest.json / unbound_review.json — source root, page-quality accounting, input/derived hashes, and issues not yet bound to one source record.
  • ingest_report.json — import counts, current-snapshot statistics, missing-answer alerts, and the legacy retention fallback.
  • study_plan.md — phase plan with chapter anchors.
  • study_state.json — the structured progress state (the SINGLE SOURCE OF TRUTH when present: phase_checklist, mistake_archive, confusion_log).
  • study_progress.md — a GENERATED VIEW of the state (rendered phase checkpoints and recorded wrong-question IDs); stale/hand-edited when it drifts from study_state.json.

Workflow

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 proof that every meaningful figure was found.
  7. Structured ingestion integrity. When .ingest/ exists, strictly load every source, unit, mapping, issue, patch, unbound entry, and build-manifest section. Re-hash current source bytes; source drift invalidates patches and derived indexes. Require one page_anchor for every accounted page, including blank/scanned pages. Require blocking issues in pending / claimed / validated / blocked to keep readiness blocked; applied/resolved/superseded issues must agree with the ledger and compiled outputs, while unrecoverable issues remain visible warnings. Verify build-manifest hashes for the wiki, bank, retrieval index, visual/teaching manifests, and structured facts.
  8. Evidence-gated phase completion. In a structured workspace, a checked phase must carry valid phase_evidence plus a currently validated notebook/chNN.guide.json with profile=full; the typed validator owns the de-duplicated teaching-example + gradable-bank denominator and source-reference checks. Under standing visual, also require capabilities.artifact_ready.status=ready, matching receipt hashes, and accepted all-page QA before completion. Under chat, the typed manifest is still required but PDF is not. verified needs at least two distinct handled bank items and at least one passed; preferences.no_questions=true caps the phase at covered_unverified. A language change makes the old manifest/artifact stale until relocalized and, for visual output, rerendered and re-QA'd. Legacy workspaces without .ingest/ remain compatible but must be reported as lacking the structured completeness gate.
  9. Path safety. Flag traversal, symlink escapes, and absolute paths in fields that are defined as workspace-relative. .ingest/build_manifest.json.source_root is intentionally an absolute materials-root binding and is not itself an error; report it only when missing, unreadable, moved, or inconsistent with source records.
Show full SKILL.md (283 more words)Show less

Run python <package-root>/scripts/validate_workspace.py <workspace> --json as the canonical static check and include its readiness in the report. ok=true means structurally runnable (no validation errors); it does not erase warnings or mean content-complete.

Output Contract

Emit a single issue list. Each entry contains: 【级别(阻断/警告/提示)】 (severity: blocker / warning / notice) + 【位置文件】 (file path) + 【现象】 (concrete symptom) + 【建议修法】 (suggested fix). End with exactly one readiness verdict: ready (no errors or warnings), usable_with_gaps (no errors, but warnings/incomplete evidence remain), or blocked (one or more errors). Never translate ok=true into ready without checking warnings.

Do NOT auto-fix. After reporting, fix item-by-item only if the user grants permission, or hand the workspace back to exam-ingest for rebuild.

Preserve these provenance labels VERBATIM when quoting them in findings: 🟢 来自资料 / 🟡 AI补充,可能与你老师讲的不完全一致 / ⚠️ AI生成答案,非老师/教材提供.

Student-facing output defaults to English (Simplified Chinese if the student opened in Chinese); the persisted study_state.json.language code (zh/en/bilingual) switches it per exam-cram's dispatch rule with single-language purity.

Language packs

This skill produces no student-facing template; its report is agent-composed in the student's language. There are no locales/zh/skills/exam-audit.md or locales/en/skills/exam-audit.md pack files — compose the issue report directly in the language given by study_state.json.language:

  • 中文 → Simplified Chinese, using the zh canonical wording in ../../docs/language-policy.md
  • English → English, using the EN canonical vocabulary in ../../docs/language-policy.md
  • 双语 → compose zh-first with a > EN: mirror line per block (rules in ../../docs/language-policy.md) Display aliases such as 中文, English, and 双语 are normalized by update_progress.py; route persisted state on zh, en, or bilingual. Unset language → the merged first-ask decides it; default English unless the student opened in Chinese.

Boundaries

  • Zero modifications and zero deletions by default — this is an inspection, not construction.
  • Do not infer the teacher's intent; report only objective inconsistencies and leave the judgment to the student.

© ZeKaiNie, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in full/skills/exam-audit of ZeKaiNie/universal-examprep-skill.

Open the folder on GitHubat commit b9e84f5

Compare with similar skills

Exam Audit next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Exam Audit compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Exam Audit this skillZeKaiNie/universal-examprep-skill303—~2.6kAutomated safety check: PassMIT
Deep Reading Analystginobefun/deep-reading-analyst-skill3544 repos~3.6kAutomated safety check: PassMIT
NihaishaJuneYaooo/nihaisha-nishi-tcm2.2k—~4kAutomated safety check: PassNone
Claude Certification Tutorrohitg00/ai-engineering-from-scratch67k—~3kAutomated safety check: PassMIT
StudyVault Quiz Tutorbevibing/tutor-skills1.3k—~1.4kAutomated safety check: PassMIT
Project Mastery Coachtudoumashu/ai-memory-skillpack456—~1.8kAutomated safety check: PassMIT

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Categories

Questions about Exam Audit

What does Exam Audit do?

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。. Exam Audit is an agent skill from ZeKaiNie/universal-examprep-skill.

When should I use Exam Audit?

Exam Audit fits situations like: tasks that involve Study guides and flashcards.

How do I install Exam Audit in Claude Code?

Run `npx skills add ZeKaiNie/universal-examprep-skill --skill exam-audit -a claude-code`. Or copy the skill folder (full/skills/exam-audit in ZeKaiNie/universal-examprep-skill) into .claude/skills/exam-audit in your project. Claude Code loads it when a task matches its description.

How do I install Exam Audit in Codex?

Run `npx skills add ZeKaiNie/universal-examprep-skill --skill exam-audit -a codex`. Or copy the skill folder (full/skills/exam-audit in ZeKaiNie/universal-examprep-skill) into .agents/skills/exam-audit in your project. Codex loads it when a task matches its description.

Can I use Exam Audit in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ZeKaiNie/universal-examprep-skill --skill exam-audit -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/exam-audit, .gemini/skills/exam-audit, .github/skills/exam-audit and .opencode/skills/exam-audit in your project.

What does Exam Audit need to run?

Going by SKILL.md and its folder, Exam Audit needs the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Exam Audit access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Exam Audit safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Exam Audit use?

Exam Audit is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Exam Audit use?

About 2.6k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Exam Audit?

Skills that share tags, products or a category with Exam Audit: Deep Reading Analyst (ginobefun/deep-reading-analyst-skill, 354 stars), Nihaisha (JuneYaooo/nihaisha-nishi-tcm, 2.2k stars), Claude Certification Tutor (rohitg00/ai-engineering-from-scratch, 67k stars) and StudyVault Quiz Tutor (bevibing/tutor-skills, 1.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Exam Audit?

ZeKaiNie (a GitHub user) maintains it in ZeKaiNie/universal-examprep-skill, which has 303 GitHub stars. The repository holds 12 skills in this directory. The repository was last updated on September 28, 2026.

Source: ZeKaiNie/universal-examprep-skill on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.