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research-town-guide
Simulate human research communities with multi-agent AI collaboration
skills/research/methodology/research-town-guide/SKILL.md
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# Research Town Guide
Simulate human research communities using multi-agent AI systems. Research Town creates virtual research environments where AI agents take on the roles of researchers, reviewers, editors, and collaborators to generate, critique, refine, and peer-review research ideas through structured multi-agent interaction.
## Overview
Research Town is an open-source framework for simulating the social dynamics of academic research communities. Rather than using a single AI model for idea generation or paper writing, Research Town instantiates multiple specialized agents -- each with a defined expertise profile, publication history, and behavioral model -- that interact through the same social structures as human researchers: lab meetings, peer review, conference discussions, and collaborative writing.
The key insight behind Research Town is that research quality emerges from the social process of science, not just individual brilliance. Peer review, adversarial critique, iterative refinement through rebuttal, and cross-disciplinary fertilization are all processes that can be simulated with multi-agent systems. By modeling these interactions, Research Town produces research outputs that have been stress-tested through simulated peer review before a human researcher ever sees them.
This approach is particularly valuable for three research tasks: (1) generating novel research ideas by simulating brainstorming sessions between agents with diverse expertise, (2) stress-testing research proposals by subjecting them to simulated peer review, and (3) identifying gaps in the literature by having agents independently survey and then synthesize findings from different subfields.
## Architecture
