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deep-researcher

Classify queries and run parallel subagent research to generate cited reports.

25|5|Updated Feb 16, 2026
Search Tags:#research#investigation#multi-agent#data gathering#synthesis#competitive analysis
One-click install
npx skills add https://github.com/Nimbalyst/skills --skill deep-researcher-nimbalyst
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: deep-researcher
Source: https://github.com/Nimbalyst/skills/tree/main/skills/research/deep-researcher
Command: npx skills add https://github.com/Nimbalyst/skills --skill deep-researcher-nimbalyst

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill tackles complex research tasks by leveraging multiple specialized AI agents to conduct thorough, parallel investigations, overcoming the limitations of single-agent research.

Core Features & Use Cases

  • Parallel Research: Conducts deep dives into topics using multiple subagents simultaneously.
  • Query Classification: Intelligently determines the best research strategy (breadth-first, depth-first, or simple factual).
  • Structured Synthesis: Consolidates findings from various agents into a comprehensive, well-organized report with citations.
  • Use Case: Use this skill to research the competitive landscape of a new product, analyze the technical underpinnings of a complex algorithm, or gather comprehensive data for a strategy memo.

Quick Start

Use the deep researcher skill to investigate the latest advancements in quantum computing.

Frequently Asked Questions about deep-researcher

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

FAQPage Schema
How do I conduct competitive analysis across multiple data sources simultaneously?▼

Complex investigations rely on classifying queries into breadth-first, depth-first, or simple factual types. This classification optimizes resource allocation and directs specialized subagents to gather and synthesize data efficiently.

What is the best way to investigate complex technical topics and gather comprehensive data?▼

Yes, deep research can handle competitive analysis and strategy memo data gathering by running specialized subagents in parallel. It classifies the query type to optimize the search strategy and synthesizes findings into a structured report.

How does query classification optimize parallel research?▼

Query classification optimizes parallel research by sorting inquiries into breadth-first, depth-first, or simple factual categories. This determines the resource allocation and search strategy used by the specialized subagents.

What limitations should I expect with multi-agent data synthesis?▼

Multi-agent data synthesis depends on filesystem artifacts to consolidate findings. Users should anticipate that highly obscure topics with minimal accessible data may yield less comprehensive structured reports despite parallel agent deployment.

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