Skill detail
research-paper-writing
End-to-end ML/AI research-paper writing pipeline.
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SKILL.md
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---
name: research-paper-writing
title: Research Paper Writing Pipeline
description: End-to-end pipeline for writing ML/AI research papers — from experiment design through analysis, drafting, revision, and submission. Covers NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Integrates automated experiment monitoring, statistical analysis, iterative writing, and citation verification.
version: 1.1.0
author: Orchestra Research
license: MIT
dependencies: [semanticscholar, arxiv, habanero, requests, scipy, numpy, matplotlib, SciencePlots]
platforms: [linux, macos, windows]
metadata:
hermes:
tags: [Research, Paper Writing, Experiments, ML, AI, NeurIPS, ICML, ICLR, ACL, AAAI, COLM, LaTeX, Citations, Statistical Analysis]
category: research
related_skills: [arxiv, ml-paper-writing, subagent-driven-development, plan]
requires_toolsets: [terminal, files]
---
# Research Paper Writing Pipeline
End-to-end pipeline for producing publication-ready ML/AI research papers targeting **NeurIPS, ICML, ICLR, ACL, AAAI, and COLM**. This skill covers the full research lifecycle: experiment design, execution, monitoring, analysis, paper writing, review, revision, and submission.
This is **not a linear pipeline** — it is an iterative loop. Results trigger new experiments. Reviews trigger new analysis. The agent must handle these feedback loops.
<!-- ascii-guard-ignore -->
```
┌─────────────────────────────────────────────────────────────┐
│ RESEARCH PAPER PIPELINE │
│ │
│ Phase 0: Project Setup ──► Phase 1: Literature Review │
│ │ │ │
│ ▼ ▼ │
│ Phase 2: Experiment Phase 5: Paper Drafting ◄──┐ │
│ Design │ │ │
│ │ ▼ │ │
│ ▼ Phase 6: Self-Review │ │
│ Phase 3: Execution & & Revision ──────────┘ │
│ Monitoring │ │
│ │ ▼ │
│ ▼ Phase 7: Submission │
│ Phase 4: Analysis ─────► (feeds back to Phase 2 or 5) │
│ │
└─────────────────────────────────────────────────────────────┘
```
<!-- ascii-guard-ignore-end -->
---
## When To Use This Skill
Use this skill when:
- **Starting a new research paper** from an existing codebase or idea
- **Designing and running experiments** to support paper claims
- **Writing or revising** any section of a research paper
- **Preparing for submission** to a specific conference or workshop
- **Responding to reviews** with additional experiments or revisions
- **Converting** a paper between conference formats
- **Writing non-empirical papers** — theory, survey, benchmark, or position papers (see [Paper Types Beyond Empirical ML](#paper-types-beyond-empirical-ml))
- **Designing human evaluations** for NLP, HCI, or alignment research
- **Preparing post-acceptance deliverables** — posters, talks, code releases
## Core Philosophy
1. **Be proactive.** Deliver complete drafts, not questions. Scientists are busy — produce something concrete they can react to, then iterate.
2. **Never hallucinate citations.** AI-generated citations have ~40% error rate. Always fetch programmatically. Mark unverifiable citations as `[CITATION NEEDED]`.
3. **Paper is a story, not a collection of experiments.** Every paper needs one clear contribution stated in a single sentence. If you can't do that, the paper isn't ready.
4. **Experiments serve claims.** Every experiment must explicitly state which claim it supports. Never run experiments that don't connect to the paper's narrative.
5. **Commit early, commit often.** Every completed experiment batch, every paper draft uRead the full source on GitHub (opens external page)