# Ml Paper Writing

Write publication-ready ML/AI papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM.

- Repository: https://github.com/Orchestra-Research/AI-Research-SKILLs (14176 stars)
- Category: Data and ML
- License: MIT
- Last updated: 2026-10-11
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill ml-paper-writing -g -a claude-code -y`
Codex: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill ml-paper-writing -g -a codex -y`
Cursor: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill ml-paper-writing -g -a cursor -y`
Gemini CLI: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill ml-paper-writing -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add Orchestra-Research/AI-Research-SKILLs`

## Description

Write publication-ready ML/AI papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. For systems venues (OSDI, NSDI, ASPLOS, SOSP), use systems-paper-writing instead.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# ML Paper Writing for Top AI Conferences

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

**For systems venues (OSDI, NSDI, ASPLOS, SOSP)**, use the [systems-paper-writing](../systems-paper-writing/) skill, which provides paragraph-level structural blueprints, writing patterns, venue-specific checklists, and LaTeX templates for systems conferences.

## 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.

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## ⚠️ 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.**

The rest: https://github.com/Orchestra-Research/AI-Research-SKILLs/blob/HEAD/20-ml-paper-writing/ml-paper-writing/SKILL.md