paper-review-pipeline
Use when a mostly complete ML conference paper needs self-review, pre-submission QA, camera-ready checking, section-by-section critique, citation-risk inspection, or rebuttal/review-response drafting. Skip this for initial drafting and use `paperreview` only when the user explicitly wants external submission.
Best use case
paper-review-pipeline is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Use when a mostly complete ML conference paper needs self-review, pre-submission QA, camera-ready checking, section-by-section critique, citation-risk inspection, or rebuttal/review-response drafting. Skip this for initial drafting and use `paperreview` only when the user explicitly wants external submission.
Teams using paper-review-pipeline should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/paper-review-pipeline/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How paper-review-pipeline Compares
| Feature / Agent | paper-review-pipeline | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Use when a mostly complete ML conference paper needs self-review, pre-submission QA, camera-ready checking, section-by-section critique, citation-risk inspection, or rebuttal/review-response drafting. Skip this for initial drafting and use `paperreview` only when the user explicitly wants external submission.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
# Paper Review Pipeline (ML Top Conferences)
Run a *two-view* paper review for ML conference submissions:
1) **Section-by-section review** (Abstract → Intro → Method → Experiments → …) with concrete edits.
2) **Prioritized issue list** with **P0/P1/P2** severity, grouped by category, including recommended fixes and verification notes.
This skill also supports **rebuttal / review response**: parse reviewer comments, classify, choose a strategy, and draft a professional point-by-point response.
## Parity Guarantee (No-Omission)
This skill is a consolidation layer. It must **not** omit any distinctive workflow, constraints, or output formats from the legacy skills it replaces.
Use:
- `references/parity-matrix.md` as the feature-parity contract and regression scenarios.
- `references/modules/` for full imported workflows and checklists.
## Execution Modes
This skill supports two modes:
- **Default: `targeted`** — run only the most relevant tracks based on the user request and inputs, but **always** produce the Final Synthesis.
- **Optional: `full-parallel`** — run **all tracks** as independent outputs (acceptable redundancy), then produce the Final Synthesis.
Trigger `full-parallel` when the user says: “全量”, “并行”, “run all tracks”, “run every skill”, “逐个 skill 测试”, “full pipeline”.
Protocol + required report template:
- `references/full-parallel-protocol.md`
- `references/report-template.md`
## Optional External Second Opinion (`paperreview`)
If the user provides a near-final or final PDF, complete the local review first and then explicitly ask whether they also want an external second opinion via `paperreview`.
- Ask only when the input is clearly a near-final or final PDF.
- Do **not** auto-submit to external services. Get explicit confirmation first.
- If the user agrees, hand off to `paperreview` as a follow-up step; otherwise finish with the local pipeline result only.
## When to Use
- Pre-submission quality check (ICML/ICLR/NeurIPS/AAAI).
- After a draft is “mostly done” but clarity/logic is shaky.
- When you suspect citation problems (missing, inconsistent, unverified, or claim-to-citation mismatch).
- Before sending to advisor/collaborators for feedback (to reduce “obvious issues”).
- After receiving reviews: draft rebuttal and a revision plan.
## When NOT to Use
- If the user wants **new research** or **new experiments** invented: require the user to provide results/artifacts.
- If the user asks for **verbatim PDF-to-LaTeX copying** or large-scale reformatting without source.
- If the task is **pure BibTeX generation from memory**: do not do it; use verified metadata workflows.
## Non-Negotiable Guardrails
1) **No hallucinated citations.**
- If a citation cannot be verified, mark it as `[CITATION NEEDED]` / placeholder and tell the user explicitly.
- Do not fabricate authors/years/venues/DOIs.
2) **Do not change technical meaning.**
- When proposing edits, preserve claims and numbers unless the user provides corrected data.
3) **Preserve LaTeX semantics when editing source.**
- Do not break `\\cite{}`, `\\ref{}`, `\\label{}`, math environments, figures/tables, or bibliography hooks.
4) **Respect blind review constraints (if applicable).**
- Avoid identity-revealing self-citations, acknowledgments, or repo links unless the user confirms it is camera-ready.
## Inputs (Ask for the Minimum Needed)
### For pre-submission review
- Paper source: LaTeX section text, or the relevant excerpts pasted in chat (preferred), and optionally the PDF for context.
- Target venue + track (ICML/ICLR/NeurIPS/AAAI) and any required sections (e.g., limitations / broader impact / ethics).
- One-sentence contribution (if the user has it). If not, infer and ask for confirmation.
### For rebuttal / review response
- Reviewer comments (verbatim text, ideally grouped by reviewer).
- Rebuttal constraints: word/page limit, formatting (ICLR OpenReview vs PDF), and timeline.
- What the user is willing to change: “clarify only” vs “add experiments” vs “major rewrite”.
## Output Contract (Always Produce Both Views)
### View A — Section-by-Section Review
For each section, output:
- **What works** (1–3 bullets)
- **What’s missing / unclear** (P0/P1/P2 tagged bullets)
- **Concrete fixes** (rewrite suggestions or structural moves)
Use the checklists in:
- `references/section-review-checklist.md`
### View B — P0/P1/P2 Issue List (Prioritized)
Format each issue like:
- **Priority**: P0 (blocking) / P1 (important) / P2 (nice-to-have)
- **Category**: Narrative / Evidence / Experimental Design / Statistics / Reproducibility / Citations / Writing / Figures-Tables / Format
- **Where**: section name + a short quote anchor (or LaTeX label if available)
- **Problem**
- **Fix**
- **Verification** (if needed): what evidence/log/search is required before claiming it is correct
Use the taxonomy in:
- `references/p0-p2-taxonomy.md`
## Modules (Routing Rules)
Use these modules to preserve legacy feature parity:
- **Paper-level QA**: `references/modules/paper-self-review.md`
- **Rebuttal / review response**: `references/modules/review-response.md` and `references/rebuttal-workflow.md`
- **LaTeX + BibTeX toolbox**: `references/modules/academic-paper-helper.md`
- **Claim-level citation audit**: `references/modules/citation-validator.md` and `references/citation-integrity.md`
- **Anti-AI polish**: `references/modules/writing-anti-ai.md`
- **LaTeX rhythm pass**: `references/modules/latex-rhythm-refiner.md`
- **Literature discovery (extended)**: `references/modules/claude-scholar-ml-paper-writing.md`
## Workflow (Default)
### Pass 0 — Triage (5–10 minutes)
1) Identify the **one-sentence contribution** and confirm it with the user.
2) Extract the **top 3–7 claims** the paper relies on.
3) For each claim, note the current support:
- empirical result (table/figure)
- theorem/proof
- citation / prior work
- ablation / analysis
4) Flag immediate P0 risks (typical: missing baselines, unclear experimental protocol, unverified citations, paper “about X” but experiments test Y).
### Pass 1 — Section Review (primary deliverable)
Review sections in order (and check alignment between them):
1) Abstract
2) Introduction (motivation → gap → contribution bullets)
3) Related Work (positioning + not a bibliography dump)
4) Method (reproducible description + design justification)
5) Experiments / Results (fair baselines + full setup + statistical reporting)
6) Analysis / Ablations (claim-driven, not exploratory noise)
7) Limitations / Broader Impact / Ethics (venue-dependent)
8) Conclusion (tight restatement + constraints + future work without overclaim)
### Pass 2 — Consolidate into P0/P1/P2
Turn findings into an actionable issue list:
- P0 first (blockers / likely desk-reject causes)
- P1 next (acceptance probability movers)
- P2 last (polish)
### Pass 3 (Optional) — Revision Plan
If the user wants an execution plan, produce:
- 3–8 tasks with measurable acceptance criteria
- suggested order (dependency-aware)
- what can be done in parallel (writing vs experiments vs citations)
## Full-Parallel Workflow (Comprehensive)
When mode is `full-parallel`, do not collapse everything into one voice. Instead:
1) Run all tracks (A–G) as independent “mini-reviewers” and keep each track output visible.
2) Then produce the Final Synthesis:
- View A: consolidated section-by-section review
- View B: consolidated P0/P1/P2 issue list
- minimal revision plan
- conflicts & resolutions between tracks
Use:
- `references/full-parallel-protocol.md`
- `references/report-template.md`
## Rebuttal / Review Response Module
When reviews arrive, do:
1) Parse and classify each comment: **Major / Minor / Clarification / Missing baseline / Missing experiment / Writing / Citation / Misunderstanding**.
2) Choose a strategy per item: **Accept + change**, **Clarify**, **Defend**, **Add experiment**, **Defer (explain constraints)**.
3) Draft point-by-point responses with:
- gratitude + precise restatement
- what changed (or why not)
- where to find it (section/figure/table)
- evidence-based tone (no overpromising)
Use:
- `references/rebuttal-workflow.md`
## Citation Integrity Module (Hard Requirement)
Before submission, ensure:
- every non-trivial factual claim has a citation or empirical evidence
- every citation key resolves in the bibliography
- any newly added citations are verified (paper exists; BibTeX not fabricated)
If deep citation audit is requested, follow:
- `references/citation-integrity.md`Related Skills
paperreview
Use when the user explicitly wants to upload a final or near-final PDF to paperreview.ai for an external second opinion. Skip this for local paper critique, which should go through `paper-review-pipeline` first.
academic-paper-helper
学术论文写作助手,专门用于 LaTeX 论文编写、BibTeX 管理、格式化、学术写作规范检查。适用于 AI/ML 研究论文、会议投稿(NeurIPS、ICML、ICLR 等)
writing-anti-ai
This skill should be used when the user asks to "remove AI writing patterns", "humanize this text", "make this sound more natural", "remove AI-generated traces", "fix robotic writing", or needs to eliminate AI writing patterns from prose. Supports both English and Chinese text. Based on Wikipedia's "Signs of AI writing" guide, detects and fixes inflated symbolism, promotional language, superficial -ing analyses, vague attributions, AI vocabulary, negative parallelisms, and excessive conjunctive phrases.
xhs-note-creator
小红书笔记素材创作技能。当用户需要创建小红书笔记素材时使用这个技能。技能包含:根据用户的需求和提供的资料,撰写小红书笔记内容(标题+正文),生成图片卡片(封面+正文卡片),以及发布小红书笔记。
xhs-longform-private-publisher
This skill should be used when the user wants to publish an existing Markdown article to Xiaohongshu as a private longform post, keep the original wording and structure, insert inline images in order, use one-click layout, and verify the result in note manager.
timestamped-video-summary
Generate a detailed, professional video content summary from timestamped subtitles/transcripts (e.g., lines starting with 00:00 / 1:23:45). Enforce strict per-segment structure (timestamp range + bold segment title + 2-paragraph body: first-person creator summary + expert 【导师评注】 critique with uncertainty handling). Use when the user provides time-coded subtitles and asks for a规范化纪要/内容纪要/逐段总结, and optionally wants a clean PDF export (do NOT include the full raw transcript in the PDF unless explicitly requested).
skills-governance
Use when auditing a large local skill collection, identifying duplicate or imported skills, comparing skill roots, or deciding what to keep, disable, or archive across Codex and adjacent agent skill directories.
skill-governance-loop
Use when the user asks to review a skill, analyze skill quality, update a skill version, or run a repeatable keep/disable/archive decision loop from real failures instead of abstract best practices.
skill-creator
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations.
session-recovery-codex
Use when recovering a Codex session, especially if the user provides a Codex session id or wants recent Codex sessions listed before resuming work.
research-lead-sidecar
Use when the user wants multi-agent division of labor for research-led work and the lead should stay on the critical path while 1-2 bounded sidecars handle low-coupling tasks. Do not use this for tiny tasks, fully sequential debugging, or overlapping refactors.
question-refiner
Use when a research question is still vague and must be clarified into a structured deep-research brief before actual literature research or execution. Skip this if the user already has a concrete paper draft or a ready-to-run research specification.