/research-paper-writing

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]
metadata:
  zorro:
    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.

┌─────────────────────────────────────────────────────────────┐
│                    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)     │
│                                                             │
└─────────────────────────────────────────────────────────────┘

---

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 update — commit with descriptive messages. Git log is the experiment history.

Proactivity and Collaboration

**Default: Be proactive. Draft first, ask with the draft.**

| Confidence Level | Action | |-----------------|--------| | **High** (clear repo, obvious contribution) | Write full draft, deliver, iterate on feedback | | **Medium** (some ambiguity) | Write draft with flagged uncertainties, continue | | **Low** (major unknowns) | Ask 1-2 targeted questions via `clarify`, then draft |

| Section | Draft Autonomously? | Flag With Draft | |---------|-------------------|-----------------| | Abstract | Yes | "Framed contribution as X — adjust if needed" | | Introduction | Yes | "Emphasized problem Y — correct if wrong" | | Methods | Yes | "Included details A, B, C — add missing pieces" | | Experiments | Yes | "Highlighted results 1, 2, 3 — reorder if needed" | | Related Work | Yes | "Cited papers X, Y, Z — add any I missed" |

**Block for input only when**: target venue unclear, multiple contradictory framings, results seem incomplete, explicit request to review first.

---

Phase 0: Project Setup

**Goal**: Establish the workspace, understand existing work, identify the contribution.

Step 0.1: Explore the Repository

# Understand project structure
ls -la
find . -name "*.py" | head -30
find . -name "*.md" -o -name "*.txt" | xargs grep -l -i "result\|conclusion\|finding"

Look for:

• `README.md` — project overview and claims
• `results/`, `outputs/`, `experiments/` — existing findings
• `configs/` — experimental settings
• `.bib` files — existing citations
• Draft documents or notes

Step 0.2: Organize the Workspace

Establish a consistent workspace structure:

workspace/
  paper/               # LaTeX source, figures, compiled PDFs
  experiments/         # Experiment runner scripts
  code/                # Core method implementation
  results/             # Raw experiment results (auto-generated)
  tasks/               # Task/benchmark definitions
  human_eval/          # Human evaluation materials (if n