
ar9av/paperorchestra 是从 https://github.com/ar9av/paperorchestra. 导入的 Agent Skill 候选人
概览
ar9av/paperorchestra 是从 https://github.com/ar9av/paperorchestra. 导入的 Agent Skill 候选人
概述(AI 翻译)
一个可插拔的技能包,允许任何编码代理在Claude代码中,光标, Antigravity、Cline、Aider、OpenCode 等,这些都能运行 PaperOrchestra 多代理流水线 将非结构化研究材料转化为可提交的LaTeX论文。
宋,Y.,宋,Y.,菲斯特,T.,尹,J. PaperOrchestra:一个用于自动化人工智能研究论文写作的多代理框架。 arXiv:2604.05018,2026年。
点击阅读arXiv上的论文
为什么会有这样的存在
论文定义了五代理管道
- 大纲
- 绘图
- 文献综述
- 章节写作
- 内容精炼
在PaperWritingBench基准上,这远超单一代理和树状搜索基线(文献综述质量绝对胜率50–68%;整体质量14–38%)。论文附录F中提供了每个代理的具体提示。
这个仓库将这些提示、模式、停止规则和验证流程转化为一套主机-代理-可执行技能。没有API密钥,没有SDK依赖,没有嵌入式LLM调用。
本页由 AI 翻译,技术细节请以原文为准。
README
以下为原文 README 节选,完整内容请查看原文。
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.
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. |
推荐工具
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安装
npx skillfish add ar9av/paperorchestra