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Simulate human research communities with multi-agent AI collaboration
Passed 15 of 22 test cases with the skill, and 5 of 22 without it.
and 9 more in the full report
10 fixed5 passed either way7 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
Provide the exact Markdown section header hierarchy used at the top of a standard Research Town review output report.
| Checked | Without skill | With skill |
|---|---|---|
| Begins the report section with ## Review Summary | Missed | Passed |
The instructions your model reads when the skill loads.
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.
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.
| Agent Role | Expertise | Behavior |
|---|---|---|
| Principal Investigator | Broad domain knowledge, research vision | Sets research direction, evaluates proposals |
| Domain Expert | Deep knowledge in a specific area | Provides technical depth, identifies related work |
| Methodologist | Statistical and experimental design expertise | Critiques methods, suggests improvements |
| Reviewer | Journal review experience, quality standards | Evaluates novelty, significance, rigor |
| Devil's Advocate | Critical thinking, identifying weaknesses | Challenges assumptions, finds counterexamples |
| Synthesizer | Cross-disciplinary knowledge | Connects ideas across fields, identifies patterns |
# Research Town interaction structure
class ResearchTownSession:
def __init__(self, agents, topic):
self.agents = agents
self.topic = topic
self.rounds = []
def run_brainstorming(self, num_rounds=3):
"""Structured brainstorming with multiple agents."""
ideas = []
for round_num in range(num_rounds):
round_ideas = []
test cases passed, vs 5 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.