What is the Multi-Agent Research skill?

It is a specialized capability for Claude that implements Anthropic's internal research systems for conducting deep, multi-faceted technical investigations efficiently.

Does it work with third-party search tools?

Yes, it is designed to orchestrate tools like Exa Search and documentation-specific MCPs to gather comprehensive data from across the web and official docs.

How does this skill improve research speed?

It utilizes parallel tool execution, allowing Claude to trigger multiple search queries simultaneously rather than waiting for sequential results, which can be up to 90% faster.

What is 'findings compression'?

Findings compression is a strategy used to distill massive amounts of research data into a concise summary containing the core claim, evidence, source, and relevance for the next step in development.

When should I use this skill over standard search?

Use this skill when your research has three or more independent dimensions to explore, requires synthesizing more than ten sources, or involves complex architectural comparisons.

Multi-Agent Research System

Name: Multi-Agent Research System
Author: auldsyababua

byauldsyababua

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Productivity & Workflow

Implements Anthropic’s production-grade research patterns for efficient, multi-faceted information gathering and synthesis.

This skill brings Anthropic's specialized multi-agent research methodologies to Claude, enabling the execution of complex, high-breadth research tasks with precision. It provides a structured framework for assessing query complexity, performing parallelized tool execution for 90% faster results, and implementing a 'wide-to-narrow' search strategy to ensure no critical context is missed. By utilizing extended thinking strategies and findings compression, it distills high-volume data from dozens of sources into actionable, decision-critical context for developers and architects.

Key Features

01Progressive search refinement from broad exploration to narrow specifics

02Extended thinking and interleaved evaluation strategies

03Findings compression to extract decision-critical information from large datasets

040 GitHub stars

05Query complexity scaling to allocate resources based on task depth

06Parallel tool execution for high-speed multi-source validation

Use Cases

01Performing time-sensitive technical landscape explorations with parallelized search

02Comparing multiple technical architectures or API alternatives simultaneously

03Synthesizing information across 10+ documentation sources for complex integrations

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add auldsyababua/instructor-workflow multi-agent-research

For use in Claude.ai and ChatGPT

Download Skill