Install
openclaw skills install @symbolscience/emergence-paper-orchestraHigh-rigor, multi-agent scholarly writing framework based on the PaperOrchestra methodology.
openclaw skills install @symbolscience/emergence-paper-orchestraThis skill transforms raw ideas and unstructured data into high-rigor, submission-ready manuscripts. It functions as a Research Partner that proactively clarifies, critiques, and anchors content in verifiable evidence.
The process is designed for Human-in-the-Loop collaboration over potentially "narrow" IM channels (linear chat).
The agent initiates an Interview Mode to capture tacit knowledge. Every user response is used to auto-update idea.md.
Synthesize all inputs into a JSON Master Plan (stored in metadata.json).
Draft strictly section-by-section into the sections/ directory to prevent context drift.
Critical evaluation pass focusing on "Numerical Literalism" and "Zero Hallucination" compliance.
| Role | Persona Goal | Recommended System Prompt Hook |
|---|---|---|
| Orchestrator | Global Consistency | "Maintain the Master Plan. Ensure Section 4 answers the hypothesis in Section 1." |
| Search Agent | Verification & Discovery | "Find narrow queries documenting exact limitations of prior work." |
| Section Writer | High-Density Composition | "Adopt a dense, objective, and technical tone. No flourishes." |
| Reviewer | Critical Evaluation | "Act as a harsh conference reviewer. Identify every unsupported claim." |
| Partner | Critique & Refine | "Challenge the user's premises. If an idea is vague, ask for data-backed specifics." |
idea.md ground truth.scaffold.sh to initialize the environment:
idea.md: Methodology and user-provided context.metadata.json: Master Plan & verified Citation bank.content.md: The assembled final output.If you use this framework for scientific publications, please cite the original PaperOrchestra team:
@misc{song2026paperorchestramultiagentframeworkautomated,
title={PaperOrchestra: A Multi-Agent Framework for Automated AI Research Paper Writing},
author={Yiwen Song and Yale Song and Tomas Pfister and Jinsung Yoon},
year={2026},
eprint={2604.05018},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2604.05018},
}