Research Town GuideSAFE

🔬 A curated collection of 23,000+ agent skills for empirical research across 8 social science disciplines. | 精选 23,000+ AI Agent 技能库,覆盖8大社会科学学科的实证研究。CoPaper.AI 20分钟完成一篇可复现的规范实证论文,并支持用户上传 Skills。-- Maintained by CoPaper.AI from Stanford REAP.

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: research-town-guide
description: "Simulate human research communities with multi-agent AI collaboration"
metadata:
  openclaw:
    emoji: "🏘️"
    category: "research"
    subcategory: "methodology"
    keywords: ["multi-agent", "simulation", "research community", "AI agents", "peer review", "collaboration"]
    source: "wentor-research-plugins"
---

# 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

### Agent Types

| 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 |

### Interaction Protocols

```python
# 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 = []
            for agent in self.agents:
                # Each agent generates ideas given prior context
                idea = agent.generate_idea(
                    topic=self.topic,
                    prior_ideas=ideas,
                    round=round_num
                )
                round_ideas.append(idea)

            # Cross-pollination: agents react to each other's ideas
            for agent in self.agents:
                reactions = agent.react_to_ideas(round_ideas)
                ideas.extend(reactions)

            self.rounds.append(round_ideas)
        return ideas

    def run_peer_review(self, paper_draft):
        """Simulate peer review with multiple reviewers."""
        reviews = []
        for reviewer in self.agents:
            if reviewer.role == "reviewer":
                review = reviewer.review_paper(
                    paper_draft,
                    criteria=["novelty", "significance",
                             "methodology", "clarity", "reproducibility"]
                )
                reviews.append(review)

        # Meta-review: aggregate and identify consensus
        meta_review = self.aggregate_reviews(reviews)
        return meta_review
```

## Setting Up a Research Town Session

### Defining Agent Profiles

```yaml
# agents.yaml - Agent configuration
agents:
  - name: "Prof. ML Expert"
    role: principal_investigator
    expertise: ["machine learning", "deep learning", "optimization"]
    style: "rigorous, quantitative, focused on scalability"
    publication_venues: ["NeurIPS", "ICML", "JMLR"]
    h_index: 45

  - name: "Dr. Biology Specialist"
    role: domain_expert
    expertise: ["structural biology", "protein engineering", "bioinformatics"]
    style: "experimental, emphasizes biological validity"
    publication_venues: ["Nature", "Cell", "PNAS"]
    h_index: 32

  - name: "Dr. Statistics"
    role: methodologist
    expertise: ["causal inference", "experimental design", "Bayesian methods"]
    style: "rigorous, demands proper statistical justification"
    publication_venues: ["JASA", "Biometrika", "Statistical Science"]
    h_index: 28

  - name: "Reviewer Alpha"
    role: reviewer
    expertise: ["interdisciplinary research", "computational biology"]
    style: "constructive but demanding, focuses on reproducibility"
    review_experience: 200

  - name: "Skeptic"
    role: devils_advocate
    expertise: ["philosophy of science", "replication crisis", "research methods"]
    style: "challenges assumptions, demands strong evidence"
```

### Running an Idea Generation Session

```python
from research_town import ResearchTown, Agent

# Initialize agents from profiles
town = ResearchTown.from_config("agents.yaml")

# Define research topic

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

What the source does

Filesystem

none-observed

Network

none-observed

Shell

none-observed

Dependencies

pinned

Secrets in source

none-found

Findings (0)

No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.