An automated AI research-paper writer based off Google's PaperOrchestra paper's implementation through a skills - benchmark + autoraters using any coding agent (Claude Code, Cursor, Antigravity, Cline, Aider). No API keys, no LLM SDKs.

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

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,