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deep-research

Decompose complex topics into subtopics for parallel agent research and synthesize findings into a unified report with ACM citations.

8|Updated Nov 29, 2025
Search Tags:#research#information retrieval#delegation#multi-agent#synthesis#topic decomposition
One-click install
npx skills add https://github.com/Pyroxin/opinionated-claude-skills --skill deep-research-pyroxin
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: deep-research
Source: https://github.com/Pyroxin/opinionated-claude-skills/tree/main/opinionated-research/skills/deep-research
Command: npx skills add https://github.com/Pyroxin/opinionated-claude-skills --skill deep-research-pyroxin

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill tackles complex research topics by breaking them down, delegating to specialized agents, and synthesizing findings into a comprehensive report, ensuring diverse perspectives and cross-referenced insights.

Core Features & Use Cases

  • Topic Decomposition: Breaks down complex subjects into manageable subtopics.
  • Parallel Agent Delegation: Assigns subtopics to specialized research agents for efficient, concurrent investigation.
  • Cross-Referencing & Synthesis: Weaves together findings from multiple agents to reveal overarching themes and connections.
  • Use Case: Researching the "impact of quantum computing on cybersecurity" by delegating subtopics like "quantum algorithms for code-breaking," "post-quantum cryptography standards," and "current cybersecurity vulnerabilities" to different agents, then synthesizing their reports into a unified analysis.

Quick Start

Use the deep-research skill to investigate the topic of "sustainable urban planning models".

Frequently Asked Questions about deep-research

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

FAQPage Schema
How do I research complex topics requiring diverse sources and cross-referencing?▼

Researching complex topics is handled by decomposing the subject into subtopics, delegating them to specialized agents for concurrent investigation, and synthesizing the findings into a unified report with cross-referenced insights.

What is multi-agent topic decomposition for information retrieval?▼

Topic decomposition is the process of breaking down a complex subject into manageable subtopics, assigning each to a specialized research agent for parallel information retrieval, and then weaving together their findings to reveal overarching themes.

How do I synthesize findings from parallel research agents into a unified report?▼

Synthesizing findings from parallel agents involves cross-referencing the diverse perspectives gathered during concurrent investigation and weaving them together into a comprehensive unified report with ACM citations.

Can I use parallel agent delegation for investigating broad subjects like sustainable urban planning?▼

Parallel agent delegation supports investigating broad subjects by assigning specialized subtopics to different agents concurrently, ensuring diverse perspectives and cross-referenced insights for thorough complex topic analysis.

What is the best way to cross-reference multiple information sources for a research synthesis?▼

The best way to cross-reference multiple sources is using an orchestrated multi-agent approach that delegates subtopics to specialized agents, ensuring cross-referenced insights are synthesized into a unified report with ACM citations.

When should I not use a multi-agent decomposition approach for research?▼

Multi-agent decomposition is not suited for simple, single-source lookups, as the overhead of orchestrating parallel agents and synthesizing cross-referenced findings is designed specifically for complex topics requiring diverse sources.

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