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deep-research

Decompose user queries into parallel research threads and synthesize findings into reports.

1|Updated Mar 1, 2026
Search Tags:#planning#documentation#research#analysis#multi-agent#synthesis
One-click install
npx skills add https://github.com/rudironsoni/SharpClaw --skill deep-research-rudironsoni
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: deep-research
Source: https://github.com/rudironsoni/SharpClaw/tree/main/.github/skills/deep-research
Command: npx skills add https://github.com/rudironsoni/SharpClaw --skill deep-research-rudironsoni

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates complex research tasks by breaking them down into parallel sub-agent investigations, synthesizing findings, and producing detailed reports or briefs, minimizing manual effort and context window limitations.

Core Features & Use Cases

  • Decomposition: Analyzes user queries to create a structured research plan with multiple threads.
  • Parallel Execution: Dispatches multiple research agents concurrently to gather information from various sources.
  • File-Based Findings: Stores intermediate findings in files for persistence and later synthesis.
  • Synthesis: Consolidates information from all agents into a coherent final output (brief or report).
  • Use Case: Researching complex architectural patterns for a new software system, comparing different machine learning models for a specific task, or gathering in-depth market analysis on a niche industry.

Quick Start

Use the deep-research skill to research the best practices for rate limiting in ASP.NET Core.

Frequently Asked Questions about deep-research

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate complex research and synthesize findings into a comprehensive report?▼

Automate complex research by decomposing user queries into parallel sub-agent investigations, executing targeted searches, and synthesizing findings into detailed briefs or reports.

What is multi-agent research decomposition and how does it handle technical or domain analysis?▼

Multi-agent research decomposition analyzes user queries to create a structured research plan, dispatching multiple agents concurrently to handle technical, domain, or hybrid research complexities.

Can I use parallel research agents to minimize context window limitations during analysis?▼

Yes, parallel research agents minimize context window limitations by executing targeted searches concurrently and storing intermediate findings in files for persistence before final synthesis.

What's the best way to compare different machine learning models or gather market analysis using autonomous agents?▼

Use a planning-execution-synthesis workflow to compare machine learning models or gather market analysis, orchestrating autonomous multi-agent research that consolidates information into a coherent output.

Does this multi-agent synthesis approach work without external API dependencies for storing intermediate findings?▼

The multi-agent synthesis approach uses file-based storage for intermediate findings, ensuring persistence during research without relying solely on active context window retention.

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