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v1truv1us
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comprehensive-research

Orchestrate parallel discovery and sequential analysis agents to investigate codebases and documentation.

7|3|Updated Nov 28, 2025
Search Tags:#code analysis#documentation#research#discovery#multi-agent#synthesis
One-click install
npx skills add https://github.com/v1truv1us/ai-eng-system --skill comprehensive-research
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: comprehensive-research
Source: https://github.com/v1truv1us/ai-eng-system/tree/main/plugins/ai-eng-system/skills/comprehensive-research
Command: npx skills add https://github.com/v1truv1us/ai-eng-system --skill comprehensive-research

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill tackles the challenge of conducting thorough research by systematically orchestrating multiple specialized agents to investigate codebases, documentation, and external sources, preventing missed information and ensuring well-founded solutions.

Core Features & Use Cases

  • Multi-Phase Research: Coordinates discovery, analysis, and synthesis across various information domains.
  • Evidence-Based Findings: Ensures all insights are backed by specific file:line references or documentation citations.
  • Use Case: When starting a new feature, use this Skill to understand the existing authentication flow, identify relevant code modules, and review past architectural decisions documented in the project.

Quick Start

Use the comprehensive-research skill to investigate how the user profile management system is implemented.

Frequently Asked Questions about comprehensive-research

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

FAQPage Schema
How do I conduct deep research across codebases and documentation to understand an existing system?▼

Codebase and documentation research is conducted by orchestrating a multi-phase process with parallel discovery and sequential analysis agents. This approach ensures you receive comprehensive, evidence-based insights backed by specific file and line references.

What's the best way to synthesize actionable recommendations from complex code analysis?▼

Synthesizing actionable recommendations from code analysis requires coordinating discovery and synthesis agents across multiple information domains. This multi-agent approach identifies patterns and prevents missed information during complex problem-solving.

Can I use automated code analysis to investigate past architectural decisions before starting a new feature?▼

Automated code analysis can investigate past architectural decisions by systematically researching project documentation and relevant code modules. It coordinates specialized agents to review documented decisions and provide evidence-based findings.

Does parallel discovery research require specific dependencies to analyze external knowledge?▼

Parallel discovery research requires no external dependencies to analyze external knowledge. It operates independently using internal scripts and references to orchestrate specialized agents for investigating codebases and documentation.

What are the limitations of using multi-agent research for complex problem-solving?▼

The limitations of multi-agent research for complex problem-solving depend on the scope of the codebase and documentation available. While it coordinates specialized agents to prevent missed information, the quality of synthesized recommendations relies on the evidence present in the analyzed sources.

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