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ml-paper-writing

Write publication-ready ML/AI/Systems papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM, OSDI, NSDI, ASPLOS, SOSP. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. Includes LaTeX templates, reviewer guidelines, and citation verification workflows.

Published by brycewang-stanford ↗brycewang-stanford/Auto-Empirical-Research-Skills

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ml-paper-writing

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Use ml-paper-writing to draft a publication-ready paper for [target venue] based on [research repository and results], focusing on [main contribution]; ensure all citations are fetched programmatically via the documented dependencies and mark any unverifiable references with an explicit LaTeX placeholder.

Required inputs

  • [target venue]
  • [research repository and results]
  • [main contribution]

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SKILL.md

name: ml-paper-writingdescription: Write publication-ready ML/AI/Systems papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM, OSDI, NSDI, ASPLOS, SOSP. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. Includes LaTeX templates, reviewer guidelines, and citation verification workflows.version: 1.1.0author: Orchestra Researchlicense: MITtags: [Academic Writing, NeurIPS, ICML, ICLR, ACL, AAAI, COLM, OSDI, NSDI, ASPLOS, SOSP, LaTeX, Paper Writing, Citations, Research, Systems]dependencies: [semanticscholar, arxiv, habanero, requests]

ML Paper Writing for Top AI & Systems Conferences

Expert-level guidance for writing publication-ready papers targeting NeurIPS, ICML, ICLR, ACL, AAAI, COLM (ML/AI venues) and OSDI, NSDI, ASPLOS, SOSP (Systems venues). This skill combines writing philosophy from top researchers (Nanda, Farquhar, Karpathy, Lipton, Steinhardt) with practical tools: LaTeX templates, citation verification APIs, and conference checklists.

Core Philosophy: Collaborative Writing

Paper writing is collaborative, but Claude should be proactive in delivering drafts.

The typical workflow starts with a research repository containing code, results, and experimental artifacts. Claude's role is to:

  1. Understand the project by exploring the repo, results, and existing documentation
  2. Deliver a complete first draft when confident about the contribution
  3. Search literature using web search and APIs to find relevant citations
  4. Refine through feedback cycles when the scientist provides input
  5. Ask for clarification only when genuinely uncertain about key decisions

Key Principle: Be proactive. If the repo and results are clear, deliver a full draft. Don't block waiting for feedback on every section—scientists are busy. Produce something concrete they can react to, then iterate based on their response.

⚠️ CRITICAL: Never Hallucinate Citations

This is the most important rule in academic writing with AI assistance.

The Problem

AI-generated citations have a ~40% error rate. Hallucinated references—papers that don't exist, wrong authors, incorrect years, fabricated DOIs—are a serious form of academic misconduct that can result in desk rejection or retraction.

The Rule

NEVER generate BibTeX entries from memory. ALWAYS fetch programmatically.

Action✅ Correct❌ Wrong
Adding a citationSearch API → verify → fetch BibTeXWrite BibTeX from memory
Uncertain about a paperMark as [CITATION NEEDED]Guess the reference
Can't find exact paperNote: "placeholder - verify"Invent similar-sounding paper

When You Can't Verify a Citation

If you cannot programmatically verify a citation, you MUST:

% EXPLICIT PLACEHOLDER - requires human verification