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Enables AI agents to run rigorous, evidence-graded research and decision workflows.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "research.md" yet — see the docs or source repo.
Use research.md to design a competitor research workflow for a new SaaS product. Make it phase-gated: define questions, collect evidence, grade evidence quality, form conclusions, and include review checkpoints for each step.
A structured competitor research plan with phases, evidence standards, review checkpoints, and output templates.
Use research.md to research a decision on entering the Southeast Asian market. Distinguish high- and low-quality evidence, list key assumptions, open questions, risks, and the confidence level of the current conclusion.
An evidence-graded decision analysis showing assumptions, risks, evidence gaps, and conclusion confidence.
Use research.md to constrain an AI research agent workflow so it must complete literature search, evidence grading, peer-review-style self-checks, and uncertainty downgrading before giving recommendations.
An executable AI research agent protocol that makes recommendations more rigorous, traceable, and calibrated.
Access research workflows, retrieval tools, and knowledge resources for faster analysis.
Connect to a research MCP server for finding, organizing, and analyzing information.
Search papers, summarize research, manage notes, and sync findings to GitHub.
Read, write, and summarize local research notes more efficiently.
Search papers, download PDFs, extract metadata, and generate bibliographies.
Aggregate MCP servers and route tools intelligently for efficient parallel work.