PaperOrchestra

A pluggable skill pack that lets any coding agent in Claude Code, Cursor, Antigravity, Cline, Aider, OpenCode, etc. which can run the PaperOrchestra multi-agent pipeline for turning unstructured research materials into a submission-ready LaTeX paper.

Song, Y., Song, Y., Pfister, T., Yoon, J. PaperOrchestra: A Multi-Agent Framework for Automated AI Research Paper Writing. arXiv:2604.05018, 2026. https://arxiv.org/pdf/2604.05018

Click to read the paper on arXiv

Why this exists

The paper defines a five-agent pipeline

• Outline
• Plotting
• Literature Review
• Section Writing
• Content Refinement

that substantially outperforms single-agent and tree-search baselines on the PaperWritingBench benchmark (50–68% absolute win margin on literature review quality; 14–38% on overall quality). The paper ships the exact prompts for every agent in Appendix F.

This repo turns those prompts, schemas, halt rules, and verification pipelines into a set of host-agent-executable skills. There are no API keys, no SDK dependencies, no embedded LLM calls. The skills are instruction documents plus deterministic helpers; your coding agent does all LLM reasoning and web search using its own tools.

How skills work here

Each skill is:

• SKILL.md — a dense instruction document the host agent reads and follows.
• references/ — reference material: verbatim paper prompts (Appendix F), JSON schemas, rubrics, halt rules, example outputs.
• scripts/ — purely deterministic local helpers: JSON schema validation, Levenshtein fuzzy matching, BibTeX formatting, dedup, LaTeX sanity checks, coverage gates. No network, no LLM, no API keys.

Everything else (LLM reasoning, web search, Semantic Scholar lookups, LaTeX compilation) is delegated to the host agent by instruction. See skills/paper-orchestra/references/host-integration.md for per-host invocation (Claude Code, Cursor, Antigravity, Cline, Aider).

The seven skills

Skill	Paper step	# LLM calls	Role
paper-orchestra	orchestrator	—	Top-level driver. Coordinates the other six.
outline-agent	Step 1	1	Idea + log + template + guidelines → structured outline JSON (plotting plan, lit review plan, section plan).
plotting-agent	Step 2	~20–30	Execute plotting plan; render plots & conceptual diagrams; optional VLM-critique refinement loop; caption everything.
literature-review-agent	Step 3	~20–30	Web-search candidates; Semantic Scholar verify (Levenshtein > 70, cutoff, dedup); draft Intro + Related Work with ≥90% citation integration.
section-writing-agent	Step 4	1	One single multimodal call: draft remaining sections, build tables from experimental log, splice figures.
content-refinement-agent	Step 5	~5–7	Simulated peer review; accept/revert per strict halt rules; safety constraints prevent gaming the evaluator.
paper-writing-bench	§3	—	Reverse-engineer raw materials (Sparse/Dense idea, experimental log) from an existing paper to build benchmark cases.
paper-autoraters	App. F.3	—	Run the paper's own autoraters: Citation F1 (P0/P1), LitReview quality (6-axis), SxS paper quality, SxS litreview quality.

Steps 2 and 3 run in parallel (see skills/paper-orchestra/references/pipeline.md).