How does the setup process work?

The skill leads you through a sequence of single questions to define your project name, domain, and goals, then triggers sub-agents to build the infrastructure and knowledge systems automatically.

Can I use this for any field of study?

Yes, the Research Project Scaffolder is domain-agnostic and has been designed to work with everything from computational biology to theoretical physics and climate modeling.

What are the required agent archetypes?

To ensure academic rigor, the system mandates at least one 'Skeptic' to provide adversarial review and one 'Calculator' to focus on executable logic and data.

Does it support external data tools?

Yes, the skill includes a dedicated phase for installing and configuring MCP (Model Context Protocol) servers to connect your agents to external datasets and tools.

What is a multi-agent research collaboration?

It is a structured digital environment where multiple AI agents with specialized roles, such as a Skeptic for adversarial review or a Calculator for logic, work together to solve complex research problems.

Research Project Scaffolder

Name: Research Project Scaffolder
Author: Meme-Theory

byMeme-Theory

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Ciencia de Datos y ML

Scaffolds complete multi-agent research collaborations with specialized agent roles, knowledge tooling, and structured session planning.

This skill automates the complex process of setting up a multi-agent research environment within Claude Code. It guides users through a 'Session 0' pipeline to define project domains, research questions, and infrastructure needs, subsequently generating a tailored ecosystem of specialized AI agents—such as Skeptics, Calculators, and Workhorses. The skill handles everything from initializing Git repositories and knowledge indexing to configuring Model Context Protocol (MCP) servers, ensuring a rigorous, domain-specific framework for collaborative academic or industrial research.

Características Principales

01Model Context Protocol (MCP) server installation and configuration

02Automated Git initialization and project infrastructure scaffolding

037 GitHub stars

04Integrated knowledge indexing and domain-specific schema setup

05Automated multi-agent team generation with specialized archetypes

06Interactive serial-questioning pipeline for project definition

Casos de Uso

01Setting up rigorous multi-agent workflows for data-driven industrial analysis

02Creating structured collaborative environments for theoretical research and modeling

03Initializing complex, domain-specific academic research projects with AI collaborators

Características Principales

01Model Context Protocol (MCP) server installation and configuration

02Automated Git initialization and project infrastructure scaffolding

037 GitHub stars

04Integrated knowledge indexing and domain-specific schema setup

05Automated multi-agent team generation with specialized archetypes

06Interactive serial-questioning pipeline for project definition

Casos de Uso

01Setting up rigorous multi-agent workflows for data-driven industrial analysis

02Creating structured collaborative environments for theoretical research and modeling

03Initializing complex, domain-specific academic research projects with AI collaborators