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.
---
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 `$ARGUMENTS` and infer the mode if the user did not provide one. State the inferred mode before running commands if you had to infer it.
### `quick-audit`
1. Run:
```bash
uv run python -B "$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:
```bash
uv run python -B "$SKILL_DIR/scripts/prepare_review_workspace.py" <paper> --output-dir ./review_results
```
This creates:
- `full_text.md`
- `metadata.json`
- `section_index.json`
- `claim_map.json`
- `paper_summary.md`
- `sections/*.md`
- `comments/`
- `references/` (minimal copies for reviewer agents)
- `committee/` (committee reviewer artifacts)
#### Phase 2: Phase 0 automated audit
Run:
```bash
uv run python -B "$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:
```bash
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 "$SKILL_DIR/scripts/render_deep_review_report.py" <review_dir>
```
Consolidation rules:
- merge exact duplicates
- keep distinct paper-level consequences separate even if they share a root cause
- preserve singleton findings unless clearly false positive
- assign `comment_type`, `severity`, `confidence`, and `root_cause_key`
#### Phase 5: Present result
Summarize:
- 1 short paragraph overall assessment
- counts of major / moderate / minor issues
- 3 highest-priority revision items
- path to `review_report.md` and `final_issues.json`
### `gate`
1. Run:
```bash
uv run python -B "$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:
```bash
uv run python -B "$SKILL_DIR/scripts/audit.py" <paper> --mode re-audit --previous-report <path> ...
```
3. If both old and new `final_issues.json` bundles are available, also run:
```bash
uv run python -B "$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:
```bash
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:
```json
{
"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.”
When Claude will use it
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.
---
name: auto-empirical-research-skills
description: Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows, or an end-to-end run from raw data all the way to a finished Word (.docx) manuscript, without reading the entire repository at once.
---
---
name: Full-empirical-analysis-skill
description: Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml + matplotlib/seaborn. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step pipeline an applied economist or quantitative social scientist runs on every paper — (1) data cleaning, (2) variable construction & transformation, (3) descriptive statistics & Table 1, (4) statistical diagnostic tests, (5) baseline empirical modeling, (6) robustness battery, (7) further analysis (mechanism, heterogeneity, mediation, moderation), (8) publication-ready tables & figures. **Also covers two parallel domain modes that share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation via `zepid` / hand-rolled `pandas`, IPTW + g-formula + TMLE doubly-robust triplet via `zepid` / `econml` / `lifelines`, Mendelian randomization via `pymr` / `mrtool` (or `rpy2` → `MendelianRandomization`/`TwoSampleMR`), KM / AFT / Cox survival via `lifelines`, E-value sensitivity, principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `econml.dml` / `doubleml`, S/T/X/R/DR meta-learners via `econml.metalearners` / `causalml`, causal forest via `econml.grf` / `causalml`, Dragonnet / TARNet / CEVAE neural causal via `causalml`, BCF via `pymc-bart` / `bcf-py`, matrix completion, CATE distribution + policy tree via `econml.policy` / `policytree-py`, off-policy evaluation, conformal causal via `mapie`, fairness audit via `fairlearn`, DAG learning via `causal-learn` / `cdt` / LLM-assisted). Prescribes which library to reach for at each step, shows the canonical code, and links to deeper `references/` files for variant-specific patterns. Use when the user asks for a **complete empirical analysis** in Python, wants to replicate an applied-economics paper from scratch, needs a reproducible workflow that is NOT opinionated on any single vertical package (contrast with StatsPAI), wants explicit control over every estimator and diagnostic, or asks "how do I write a full empirical pipeline in Python?". Also triggers when the user names a specific classical step in isolation — "winsorize at 1/99%", "run Breusch-Pagan", "build a Table 1 balance table", "do a placebo test", "event study plot", "mediation analysis" — and wants it wired into the broader pipeline. Mode A triggers on "target trial emulation", "IPTW", "TMLE", "Mendelian randomization", "STROBE", "公共健康", "流行病学". Mode B triggers on "DML", "double machine learning", "causal forest", "meta-learner", "Dragonnet", "BCF", "policy tree", "conformal causal", "fairness audit", "因果机器学习".
---
