/ml-paper-writing

id: ml-paper-writing
name: ml-paper-writing
version: 1.0.0
description: |-
  Write publication-ready ML/AI papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM.
stages: ["publication"]
tools: ["read_file", "search_project", "write_file"]
summary: |-
  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. Includes LaTeX templates, revie...
primaryIntent: writing
intents: ["writing", "research"]
capabilities: ["visualization-reporting"]
domains: ["cs-ai"]
keywords: ["academic writing", "neurips", "icml", "iclr", "acl", "aaai", "colm", "latex", "paper writing", "citations", "research", "ml-paper-writing"]
source: builtin
status: verified
upstream:
  repo: dr-claw
  path: skills/ml-paper-writing
  revision: 8322dc4ef575affaa374aa7922c0a0971c6db7d7
resourceFlags:
  hasReferences: true
  hasScripts: false
  hasTemplates: true
  hasAssets: false
  referenceCount: 5
  scriptCount: 0
  templateCount: 50
  assetCount: 0
  optionalScripts: false

ml-paper-writing

Canonical Summary

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. Includes LaTeX templates, revie...

Trigger Rules

Use this skill when the user request matches its research workflow scope. Prefer the bundled resources instead of recreating templates or reference material. Keep outputs traceable to project files, citations, scripts, or upstream evidence.

Resource Use Rules

• Read from `references/` only when the current task needs the extra detail.
• Reuse files under `templates/` instead of recreating equivalent structure from scratch when the user asks for the matching deliverable.

Execution Contract

• Resolve every relative path from this skill directory first.
• Prefer inspection before mutation when invoking bundled scripts.
• If a required runtime, CLI, credential, or API is unavailable, explain the blocker and continue with the best manual fallback instead of silently skipping the step.
• Do not write generated artifacts back into the skill directory; save them inside the active project workspace.

Upstream Instructions

ML Paper Writing for Top AI Conferences

Expert-level guidance for writing publication-ready papers targeting **NeurIPS, ICML, ICLR, ACL, AAAI, and 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.

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 citation | Search API → verify → fetch BibTeX | Write BibTeX from memory | | Uncertain about a paper | Mark as `[CITATION NEEDED]` | Guess the reference | | Can't find exact paper | Note: "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
\cite{PLACEHOLDER_author2024_verify_this}  % TODO: Verify this citation exists

**Always tell the scientist**: "I've marked [X] citations as placeholders that need verification. I could not confirm these papers exist."

Recommended: Install Exa MCP for Paper Search

For the best paper search experience, install **Exa MCP** which provides real-time academic search:

**Claude Code:**

claude mcp add exa -- npx -y mcp-remote "https://mcp.exa.ai/mcp"

**Cursor / VS Code** (add to MCP settings):

{
  "mcpServers": {
    "exa": {
      "type": "http",
      "url": "https://mcp.exa.ai/mcp"
    }
  }
}

Exa MCP enables searches like:

• "Find papers on RLHF for language models published after 2023"
• "Search for transformer architecture papers by Vaswani"
• "Get recent work on sparse autoencoders for interpretability"

Then verify results with Semantic Scholar API and fetch BibTeX via DOI.

---

Workflow 0: Starting from a Research Repository

When beginning paper writing, start by understanding the project:

Project Understanding:
- [ ] Step 1: Explore the repository structure
- [ ] Step 2: Read README, existing docs, and key results
- [ ] Step 3: Identify the main contribution with the scientist
- [ ] Step 4: Find papers already cited in the codebase
- [ ] Step 5: Search for additional relevant literature
- [ ] Step 6: Outline the paper structure together
- [ ] Step 7: