PaperOrchestra High-Rigor Multi-Agent Academic Writing Framework

Problem Context

Academic writing often fails at engineering details: ideas remain scattered in chat, citations are not verified item by item, long-form generation drifts in context, and the final draft can contain unsupported claims or invented references. In submission-bound workflows, this creates high risk because the output may look complete but not be auditable.

How It Works and Where It Applies

This skill structures paper production as a reviewable multi-agent pipeline. It begins with an interview-style skeleton pass to clarify assumptions, data, and scope, persisting verified facts into idea.md. The next step compiles inputs into a metadata.json master plan and separates macro-background search from micro verification by DOI or arXiv ID. Sections are written into sections/ to reduce context drift, and a reviewer agent checks numerical literalism, evidence coverage, and zero-hallucination compliance.

It is best suited for human-in-the-loop paper drafting, such as technical reports, conference abstracts, survey drafts, or reorganizing existing methodological notes. It is not a substitute for experiments, real data sources, formal academic review, or domain-specific validation when key evidence is missing.