Skill detail
paper-audit
Useful for reviewing submission readiness, but not for drafting or revising prose.
Inspect before use
Automated review checks relevance, not safety or endorsement. Read the source instructions before using this skill.
SKILL.md
The saved excerpt is a snapshot from review. The external source remains the complete and most current version.
--- name: paper-audit description: Reviewer-style audit and submission gate for academic papers in .tex, .typ, or .pdf. Use for peer-review critique, readiness/gate decisions, blocker triage, revision roadmaps, journal-style reports, and re-audits. Do not use for source editing, sentence polishing, bibliography search, or compile repair. when_to_use: >- Trigger on "review my paper", "act as a reviewer", "simulate peer review", "audit this paper", "审稿", "投稿门控", "投稿前体检", "把把关", "看看能不能投", "出审稿意见", "is this ready to submit", or requests for major/minor issues and revision priorities. metadata: category: academic-writing tags: [audit, deep-review, paper, pdf, latex, typst, chinese, english, reviewer, gate, re-audit] version: "6.0.0" last_updated: "2026-08-09" argument-hint: "[paper.tex|paper.typ|paper.pdf] [--mode quick-audit|deep-review|gate|re-audit|polish] [--report-style deep-review|peer-review] [--focus full|editor|theory|literature|methodology|logic] [--venue VENUE] [--lang en|zh] [--previous-report PATH] [--literature-search] [--tavily-key KEY] [--s2-key KEY] [--scholar-eval] [--regression] [--overwrite-workspace] [--format md|json]" allowed-tools: Read, Glob, Grep, Bash(uv *), Task --- # Paper Audit Skill v6.0 `paper-audit` is **deep-review-first**: behave like a serious reviewer — find technical, methodological, claim-level, and cross-section issues; keep script-backed findings separate from reviewer judgment; return a structured issue bundle plus a revision roadmap. Use it for audit and review, not as the first tool for source editing, sentence rewriting, or build fixing. A script-backed `PRESUBMISSION` layer handles final-week mechanical checks (em dashes, AI-tone term frequency, abstract completeness, LaTeX citation/label/equation hygiene, paragraph-shape weak signals, concrete captions). It plugs into existing modes and is not a separate public mode; see `references/PRESUBMISSION_GUIDE.md`. **Requirements**: `.tex`/`.typ` audit needs only the Python standard library. **PDF mode needs `pip install pymupdf`** (the `enhanced` extraction path also needs `pymupdf4llm`); both are optional and lazily imported — a `.pdf` input without them fails with a clear install hint. ## What This Skill Produces - `quick-audit`: fast submission-readiness screen with script-backed findings, incl. `PRESUBMISSION` - `deep-review`: reviewer-style structured issue bundle with major/moderate/minor findings - `gate`: PASS/FAIL calibrated for submission blockers; `PRESUBMISSION` Major/Minor stay advisory - `re-audit`: compare current issue bundle against a previous audit, incl. mechanical regressions - `polish`: precheck-only handoff into a polishing workflow The primary product is no longer just a score: the `deep-review` workspace root contains exactly four reader-facing files — `review_report.md`, `revision_suggestions.md`, and their HTML twins — with everything else under `artifacts/`. Full artifact map and the `--lang en|zh` report-language rules: `references/output-layout.md`. ## Do Not Use - direct source surgery on `.tex` / `.typ` - compilation debugging as the main task - free-form literature survey writing - paragraph-level related-work rewriting - cosmetic grammar cleanup without an audit goal - cover letter generation / optimization / claim alignment — route to `cover-letter` ## Critical Rules - Don't rewrite the paper source — `paper-audit` is a reviewer, not an editor; switch skills explicitly if the user wants prose changes, so review evidence stays separable from edits. - Don't fabricate references, baselines, or reviewer evidence — invented citations and made-up reviewer voices undermine every other finding in the bundle. - Distinguish `[Script]` from `[LLM]` findings — script-backed items have a deterministic anchor the user can rerun, while LLM findings need a quote or section to be falsifiable. - Anchor every reviewer finding to a quote, section, or exact textual location — unanchored complaints becomeRead the full source on GitHub (opens external page)