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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.
Extract claims from drafts and find high-quality academic citations fast.
Connect to a research MCP server for finding, organizing, and analyzing information.
Conduct structured multi-step web research and generate comprehensive reports automatically.
Turn research questions into evidence-backed answers from Hacker News and arXiv.
Search papers, download PDFs, extract metadata, and generate bibliographies.
Conduct iterative deep web research, parsing, and synthesis through MCP clients.