Paper Audit Skill v4.4 Skill

Paper Audit Skill v4.4

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
peer_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
paragraph-level related-work rewriting
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.
For literature findings, judge whether the gap is evidence-backed and fairly positioned; do not rewrite the prose inside paper-audit.

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:

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

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

Literature focus means:

verify whether the literature is thematically synthesized or merely enumerated
verify whether contradictions are acknowledged rather than flattened
verify whether the claimed gap is genuine instead of manufactured by selective citation
do not rewrite the related-work prose; hand that off to the format-specific writing skill when needed

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", "gap is fake", "选择性引用"
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:

references/REVIEW_CRITERIA.md
references/DEEP_REVIEW_CRITERIA.md
references/CHECKLIST.md
references/CONSOLIDATION_RULES.md
references/ISSUE_SCHEMA.md

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

formula / derivation errors
notation inconsistency
prose vs formal object mismatch
numerical inconsistency
missing justification
overclaim or claim inaccuracy
ambiguity that can mislead a careful reader
underspecified methods / missing information
internal contradiction
self-consistency of standards
table structure violations
abstract structural incompleteness
theory contribution deficiency
qualitative methodology opacity
pseudo-innovation / straw man
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`

Run:

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

Present a concise report:
- Submission Blockers first
- then Quality Improvements
- then checklist items
- mark quick-audit findings with [Script] provenance
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.

If the user explicitly wants a submission-style reviewer report (for example: “SCI reviewer”, “journal review report”, “Summary / Major Issues / Minor Issues / Recommendation”, or “审稿报告”), keep the same deep-review evidence pipeline but make peer_review_report.md the Primary View in the combined CLI summary while keeping review_report.md as the richer evidence bundle.

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:

agents/committee_editor_agent.md
- write: committee/editor.md
- write: comments/committee_editor.json
agents/committee_theory_agent.md
- write: committee/theory.md
- write: comments/committee_theory.json
agents/committee_literature_agent.md
- write: committee/literature.md
- write: comments/committee_literature.json
agents/committee_methodology_agent.md
- write: committee/methodology.md
- write: comments/committee_methodology.json
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
identify the Primary View selected by --report-style
path to review_report.md, peer_review_report.md, and final_issues.json

`gate`

Run:

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

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.
Report PASS/FAIL.
Present EIC screening results first (verdict + score + justification).
List blockers next.
Keep advisory items separate from blockers.
For IEEE pseudocode checks, make it explicit which issues are mandatory and which are only IEEE-safe recommendations.

`re-audit`

Requires --previous-report PATH.

Run:

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

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>

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`

Run the audit precheck:

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

If blockers exist, stop and report them.
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": true
}

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.”
“Audit only the literature positioning and tell me whether the claimed gap is real or fabricated by selective citation.”

Agent Skills: Paper Audit Skill v4.4

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