paper-audit

name	paper-audit
description	Deep-review-first audit for Chinese and English academic papers across LaTeX, Typst, and PDF formats. Use whenever the user wants reviewer-style paper critique, pre-submission readiness checks, pass/fail gate decisions, structured revision roadmaps, or re-audits of revised manuscripts. Trigger even if the user only says "review my paper", "check if this is ready to submit", "audit this PDF", "simulate peer review", "find the biggest problems in this manuscript", or "re-check whether I fixed the review issues". Do not use for direct source editing or compilation-heavy repair; route those to the format-specific writing skills instead.
metadata	{"category":"academic-writing","tags":["audit","deep-review","paper","pdf","latex","typst","chinese","english","reviewer","gate","re-audit"],"version":"4.2","last_updated":"2026-04-02"}
argument-hint	[paper.tex|paper.typ|paper.pdf] [--mode quick-audit|deep-review|gate|re-audit|polish] [--focus full|editor|theory|literature|methodology|logic] [--venue VENUE] [--previous-report PATH] [--literature-search] [--scholar-eval] [--format markdown|json]
allowed-tools	Read, Glob, Grep, Bash(uv *), Task

Paper Audit Skill v4.2

paper-audit is now deep-review-first. Its core job is to behave like a serious reviewer: find technical, methodological, claim-level, and cross-section issues; keep script-backed findings separate from reviewer judgment; and return a structured issue bundle plus a revision roadmap.

Use it for audit and review. Do not use it as the first tool for source editing, sentence rewriting, or build fixing.

What This Skill Produces

• quick-audit: fast submission-readiness screen with script-backed findings
• deep-review: reviewer-style structured issue bundle with major/moderate/minor findings
• gate: PASS/FAIL decision calibrated for submission blockers
• re-audit: compare current issue bundle against a previous audit
• polish: precheck-only handoff into a polishing workflow

The primary product is no longer just a score. For deep-review, the main outputs are:

• final_issues.json
• overall_assessment.txt
• review_report.md
• revision_roadmap.md

Do Not Use

• direct source surgery on .tex / .typ
• compilation debugging as the main task
• free-form literature survey writing
• cosmetic grammar cleanup without an audit goal

Critical Rules

• Never rewrite the paper source unless the user explicitly switches to an editing skill.
• Never fabricate references, baselines, or reviewer evidence.
• Always distinguish [Script] from [LLM] findings.
• Always anchor reviewer findings to a quote, section, or exact textual location.
• Be conservative with OCR noise, formatting quirks, and obvious copy-editing trivia.
• Review like a careful reader: understand the author's intended meaning before flagging an issue.

Mode Selection

Requested intent	Mode
"check my paper", "quick audit", "submission readiness"	quick-audit
"review my paper", "simulate peer review", "harsh review", "deep review"	deep-review
"is this ready to submit", "gate this submission", "blockers only"	gate
"did I fix these issues", "re-audit", "compare against old review"	re-audit
"polish the writing, but only if safe"	polish

Legacy aliases still work for one compatibility cycle:

• self-check -> quick-audit
• review -> deep-review

Committee Focus Routing (deep-review)

For deep-review, use the Academic Pre-Review Committee by default. This is a 5-role review pass:

1. Editor (desk-reject screen)
2. Reviewer 1 (theory contribution)
3. Reviewer 3 (literature dialogue / gap)
4. Reviewer 2 (methodology transparency)
5. Reviewer 4 (logic chain)

If the user requests a single dimension, run only the matching committee role(s).

If --focus ... is provided, it overrides keyword inference:

• --focus full (default)
• --focus editor|theory|literature|methodology|logic

Keyword map (English + Chinese):

• editor: "desk reject", "pre-screen", "editor", "EIC", "主编", "预筛", "初筛"
• theory: "theory", "contribution", "novelty", "theoretical dialogue", "理论", "贡献", "创新性"
• literature: "related work", "literature", "research gap", "citation", "文献", "综述", "Research Gap", "引用"
• methodology: "methods", "sample", "coding", "data", "design", "SRQR", "方法", "样本", "编码", "数据", "研究设计", "透明度"
• logic: "logic", "argument", "causal", "structure", "论证", "因果", "逻辑", "结构"

Output language: match the user's request language. If ambiguous, match the paper language.

Review Standard

Read these references before running reviewer-style work:

1. references/REVIEW_CRITERIA.md
2. references/DEEP_REVIEW_CRITERIA.md
3. references/CHECKLIST.md
4. references/CONSOLIDATION_RULES.md
5. references/ISSUE_SCHEMA.md

The deep-review workflow uses a 16-part issue taxonomy:

1. formula / derivation errors
2. notation inconsistency
3. prose vs formal object mismatch
4. numerical inconsistency
5. missing justification
6. overclaim or claim inaccuracy
7. ambiguity that can mislead a careful reader
8. underspecified methods / missing information
9. internal contradiction
10. self-consistency of standards
11. table structure violations
12. abstract structural incompleteness
13. theory contribution deficiency
14. qualitative methodology opacity
15. pseudo-innovation / straw man
16. paragraph-level argument incoherence

Workflow

Common Step 0

Parse $ARGUMENTS and infer the mode if the user did not provide one. State the inferred mode before running commands if you had to infer it.

quick-audit

1. Run:

   uv run python -B "$SKILL_DIR/scripts/audit.py" <paper> --mode quick-audit ...
   

2. Present a concise report:
   • Submission Blockers first
   • then Quality Improvements
   • then checklist items
   • mark quick-audit findings with [Script] provenance
3. If the user clearly wants reviewer-depth critique after the quick screen, escalate to deep-review.

deep-review

Use this as the default reviewer-style path.

Phase 1: Prepare workspace

Run:

uv run python -B "$SKILL_DIR/scripts/prepare_review_workspace.py" <paper> --output-dir ./review_results

This creates:

• full_text.md
• metadata.json
• section_index.json
• claim_map.json
• paper_summary.md
• sections/*.md
• comments/
• references/ (minimal copies for reviewer agents)
• committee/ (committee reviewer artifacts)

Phase 2: Phase 0 automated audit

Run:

uv run python -B "$SKILL_DIR/scripts/audit.py" <paper> --mode deep-review ...

Treat this as Phase 0 only. It supplies script-backed context and scores, not the final review.

Phase 3: Committee + Review Lanes

Phase 3A: Academic Pre-Review Committee (default)

Decide committee focus:

• If --focus ... is provided, use it.
• Otherwise infer from the user request using the keyword map in "Committee Focus Routing".
• If nothing matches, default to full (all five roles).

Dispatch the committee reviewers (in this exact order) and have them write artifacts into the workspace:

1. agents/committee_editor_agent.md
   • write: committee/editor.md
   • write: comments/committee_editor.json
2. agents/committee_theory_agent.md
   • write: committee/theory.md
   • write: comments/committee_theory.json
3. agents/committee_literature_agent.md
   • write: committee/literature.md
   • write: comments/committee_literature.json
4. agents/committee_methodology_agent.md
   • write: committee/methodology.md
   • write: comments/committee_methodology.json
5. agents/committee_logic_agent.md
   • write: committee/logic.md
   • write: comments/committee_logic.json

If subagents are unavailable, run the committee reviewers inline, but keep the same file outputs.

Then write: committee/consensus.md

• include: overall score (1-10), ordered priorities, and the top 3 issues to fix first
• scoring formula:
  • start at 9.0
  • subtract: 1.5 * (# major) + 0.7 * (# moderate) + 0.2 * (# minor)
  • floor at 1.0
  • if Editor verdict is Desk Reject, cap at 4.0

Note: render_deep_review_report.py automatically embeds committee/*.md into review_report.md when present.

Phase 3B: Section and cross-cutting review lanes (coverage)

Read:

• references/SUBAGENT_TEMPLATES.md
• references/REVIEW_LANE_GUIDE.md

Then dispatch reviewer tasks for:

• section lanes
  • introduction / related work
  • methods
  • results
  • discussion / conclusion
  • appendix, if present
• cross-cutting lanes
  • claims vs evidence
  • notation and numeric consistency
  • evaluation fairness and reproducibility
  • self-standard consistency
  • prior-art and novelty grounding

Each lane writes a JSON array into comments/.

If subagents are unavailable, use the built-in deterministic fallback lane pass in scripts/audit.py so the workflow still writes lane-compatible JSON into comments/ before consolidation.

Phase 4: Consolidation

Run:

uv run python -B "$SKILL_DIR/scripts/consolidate_review_findings.py" <review_dir>
uv run python -B "$SKILL_DIR/scripts/verify_quotes.py" <review_dir> --write-back
uv run python -B "$SKILL_DIR/scripts/render_deep_review_report.py" <review_dir>

Consolidation rules:

• merge exact duplicates
• keep distinct paper-level consequences separate even if they share a root cause
• preserve singleton findings unless clearly false positive
• assign comment_type, severity, confidence, and root_cause_key

Phase 5: Present result

Summarize:

• 1 short paragraph overall assessment
• counts of major / moderate / minor issues
• 3 highest-priority revision items
• path to review_report.md and final_issues.json

gate

1. Run:

   uv run python -B "$SKILL_DIR/scripts/audit.py" <paper> --mode gate ...
   

2. EIC Screening (Phase 0.5): Read agents/editor_in_chief_agent.md and perform the editor-in-chief desk-reject screening on the paper's title, abstract, and introduction. This evaluates pitch quality, venue fit, fatal flaws, and presentation baseline. A desk-reject verdict is a gate blocker.
3. Report PASS/FAIL.
4. Present EIC screening results first (verdict + score + justification).
5. List blockers next.
6. Keep advisory items separate from blockers.
7. For IEEE pseudocode checks, make it explicit which issues are mandatory and which are only IEEE-safe recommendations.

re-audit

1. Requires --previous-report PATH.
2. Run:

   uv run python -B "$SKILL_DIR/scripts/audit.py" <paper> --mode re-audit --previous-report <path> ...
   

3. If both old and new final_issues.json bundles are available, also run:

   uv run python -B "$SKILL_DIR/scripts/diff_review_issues.py" <old_final_issues.json> <new_final_issues.json>
   

4. Present:
   • root-cause-aware status labels: FULLY_ADDRESSED, PARTIALLY_ADDRESSED, NOT_ADDRESSED, NEW
   • use structured prior issue bundles when available, but still accept Markdown previous reports

polish

1. Run the audit precheck:

   uv run python -B "$SKILL_DIR/scripts/audit.py" <paper> --mode polish ...
   

2. If blockers exist, stop and report them.
3. Only proceed into polishing if the precheck is safe.

Output Contract

For deep-review, the final issue schema is:

{
  "title": "short issue title",
  "quote": "exact quote from paper",
  "explanation": "why this matters and what remains problematic",
  "comment_type": "methodology|claim_accuracy|presentation|missing_information",
  "severity": "major|moderate|minor",
  "confidence": "high|medium|low",
  "source_kind": "script|llm",
  "source_section": "methods",
  "related_sections": ["results", "appendix"],
  "root_cause_key": "shared-normalized-key",
  "review_lane": "claims_vs_evidence",
  "gate_blocker": false,
  "quote_verified" 

Always prefer:

• exact quotes over vague paraphrase
• evidence-backed findings over style commentary
• issue bundle + roadmap over raw script dumps

References

File	Purpose
references/REVIEW_CRITERIA.md	top-level audit scoring and mapping
references/DEEP_REVIEW_CRITERIA.md	deep-review-specific issue taxonomy (16 dimensions) and leniency rules
references/CONSOLIDATION_RULES.md	deduplication and root-cause merge policy
references/ISSUE_SCHEMA.md	canonical JSON schema
references/REVIEW_LANE_GUIDE.md	section lanes and cross-cutting lanes
references/SUBAGENT_TEMPLATES.md	reviewer task templates
references/QUICK_REFERENCE.md	CLI and mode cheat sheet

Scripts

Script	Purpose
scripts/audit.py	Phase 0 audit and mode entrypoint
scripts/prepare_review_workspace.py	create deep-review workspace
scripts/build_claim_map.py	extract headline claims and closure targets
scripts/consolidate_review_findings.py	deduplicate comment JSONs
scripts/verify_quotes.py	verify exact quote presence
scripts/render_deep_review_report.py	render final Markdown report
scripts/diff_review_issues.py	compare old vs new issue bundles

Reviewer Lanes

Committee agents (deep-review default):

• committee_editor_agent.md
• committee_theory_agent.md
• committee_literature_agent.md
• committee_methodology_agent.md
• committee_logic_agent.md

Default deep-review lanes live in agents/:

• section_reviewer_agent.md
• claims_evidence_reviewer_agent.md
• notation_consistency_reviewer_agent.md
• evaluation_fairness_reviewer_agent.md
• self_consistency_reviewer_agent.md
• prior_art_reviewer_agent.md
• synthesis_agent.md
• editor_in_chief_agent.md — EIC desk-reject screener (used in gate mode)

Specialized deep-review agents (read their files for activation criteria):

• critical_reviewer_agent.md — devil's advocate with C3-C5 checks
• domain_reviewer_agent.md — domain expertise with A1-A7 assessments
• methodology_reviewer_agent.md — methodology rigor with B3-B10 checks
• literature_reviewer_agent.md — evidence-based literature verification (optional, --literature-search)

Examples

• “Review this manuscript like a serious conference reviewer and tell me the biggest validity risks.”
• “Run a quick audit on paper.tex and tell me what blocks submission.”
• “Gate this IEEE submission and separate blockers from recommendations.”
• “Re-audit this revision against my previous report.”