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deep-research

Orchestrate parallel AI agents to research topics and synthesize reports with confidence scoring.

Updated Feb 10, 2026
Search Tags:#research#information gathering#ai agents#reporting#synthesis#topic exploration
One-click install
npx skills add https://github.com/eysenfalk/git-review --skill deep-research-eysenfalk
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: deep-research
Source: https://github.com/eysenfalk/git-review/tree/main/.claude/skills/deep-research
Command: npx skills add https://github.com/eysenfalk/git-review --skill deep-research-eysenfalk

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill automates in-depth research on any topic by leveraging parallel AI agents, synthesizing findings into a comprehensive, credible report.

Core Features & Use Cases

  • Automated Research: Conducts deep dives into user-specified topics.
  • Parallel Agent Execution: Utilizes multiple AI agents simultaneously for efficiency.
  • Synthesized Reporting: Consolidates findings into a structured report with confidence scoring and source credibility ratings.
  • Use Case: A product manager needs to understand the competitive landscape for a new feature. They can use this Skill to get a comprehensive overview of existing solutions, market trends, and potential challenges.

Quick Start

Use the deep-research skill to research the impact of quantum computing on cybersecurity.

Frequently Asked Questions about deep-research

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

FAQPage Schema
How do I automate information gathering for a comprehensive research report?▼

You can automate information gathering by using parallel AI agents to conduct deep research on specified topics, which then synthesize findings into a comprehensive report featuring confidence scoring and source credibility ratings.

How does parallel AI agent execution work for topic exploration?▼

Parallel AI agent execution deploys multiple agents simultaneously to gather data based on a user-approved research plan, ensuring efficient information synthesis and comprehensive topic exploration before generating a final report.

What is the best way to get a competitive landscape analysis using AI research agents?▼

The best way to get a competitive landscape analysis is using AI research agents to conduct an initial clarifying interview, approve a research plan, and synthesize parallel findings into a comprehensive report with source credibility ratings.

Do I need to provide a research plan before AI agents start gathering information?▼

Yes, you need to review and approve a generated research plan after an initial clarifying interview, ensuring the parallel AI agents gather information aligned with your specific topic exploration requirements before final synthesis.

Can I trust the source credibility and confidence scoring in AI synthesized reports?▼

You can trust synthesized reports because they include explicit confidence scoring and source credibility ratings, evaluating the reliability of data gathered by parallel AI agents during the automated research process.

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