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paper-review-pipeline

Audit ML conference papers and draft point-by-point rebuttals.

425|37|Updated Mar 6, 2026
Search Tags:#qa#latex#ml#paper-review#pre-submission#rebuttal#citation-audit
One-click install
npx skills add https://github.com/cnfjlhj/ai-collab-playbook --skill paper-review-pipeline
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: paper-review-pipeline
Source: https://github.com/cnfjlhj/ai-collab-playbook/tree/main/skills/full/paper-review-pipeline
Command: npx skills add https://github.com/cnfjlhj/ai-collab-playbook --skill paper-review-pipeline

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Provide a structured, auditable pre-submission quality-assurance and rebuttal workflow for near-final machine learning conference papers, reducing desk-reject risks and improving clarity, reproducibility, and citation integrity.

Core Features & Use Cases

  • Two-view review: section-by-section edits plus a consolidated prioritized P0/P1/P2 issue list with verification notes and recommended fixes.
  • Rebuttal & response drafting: parse reviewer comments, classify issues, choose strategies, and produce point-by-point rebuttal drafts and a minimal revision plan.
  • Citation integrity & LaTeX safety: explicit no-hallucination rules, citation audits, and edits that preserve LaTeX semantics (\cite{}, math, labels).
  • Execution modes: targeted runs for relevant tracks or full-parallel mode to run all audit tracks independently and synthesize results.
  • Use Case: final QA before ICML/ICLR/NeurIPS/AAAI submission, camera-ready checking, or drafting a structured rebuttal to reviewer comments.

Quick Start

Run a section-by-section pre-submission review on my near-final ML paper draft and produce a consolidated P0/P1/P2 action list plus a draft point-by-point rebuttal.

Frequently Asked Questions about paper-review-pipeline

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I perform a pre-submission QA check on my ML conference paper?▼

Pre-submission QA for ML conference papers is performed by running section-by-section critiques on near-final LaTeX drafts or PDFs to produce a consolidated P0/P1/P2 prioritized issue list with recommended fixes and verification notes.

What is the best way to draft a point-by-point rebuttal for ICLR or NeurIPS reviewer comments?▼

Drafting a point-by-point rebuttal for ICLR or NeurIPS involves parsing reviewer comments, classifying issues, selecting response strategies, and generating structured rebuttal drafts alongside a minimal revision plan.

Can I audit citations in my LaTeX paper without hallucinating references?▼

Citation audits in LaTeX papers can be performed safely using explicit no-hallucination guardrails that verify references while preserving LaTeX semantics like \cite{}, math, and labels during edits.

Does pre-submission paper review work with LaTeX excerpts and PDFs?▼

Pre-submission paper review works with both LaTeX excerpts and PDFs, executing section-level edits and full-parallel audit tracks independently before synthesizing results into a prioritized action list.

How do I prioritize issues found during a machine learning paper review?▼

Issues found during machine learning paper review are prioritized into a consolidated P0/P1/P2 issue list, categorizing critical desk-reject risks separately from minor clarity and reproducibility improvements.

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