A Claude Code plugin providing a full academic workflow: 13-agent deep research, 12-agent paper writing, 7-agent peer review, and a 10-stage end-to-end orchestrator with integrity gates. Emphasises "AI as copilot, not pilot."
When to use
After a Docent research run — feed the output into /ars-write for a paper draft, then /ars-review for structured peer critique. Use /ars-revision when you have reviewer feedback to work through.
An open-source AI research agent CLI. Runs multi-stage research pipelines using any major LLM provider via a unified interface. Docent uses Feynman as its highest-quality research backend (`--backend feynman`).
When to use
When you need the highest-quality long-form research brief and have an LLM API key. Feynman handles the full synthesis pipeline autonomously — Docent orchestrates it and collects the output.
A web-based research notebook that grounds AI answers in the sources you upload. Docent's `to-notebook` command pushes any run's sources + report into a notebook your collaborators can open in their browser.
When to use
When you need to hand a research artifact to a non-technical collaborator — an advisor, a co-author, a reviewer — or want audio overviews and flashcards generated from your research output.
Install / accesscopy & paste
$docent studio to-notebook --output-file research/output.md
Inside Docent
3 patterns
Open-source patterns adopted from the ecosystem. Full attribution in docs/ecosystem.md.
Open a GitHub issue with the tool name, a one-line description, and the install command. Include: name, author, repo/site, license, and when to reach for it alongside Docent.