What is the Comprehensive Research skill?

It is an advanced capability for Claude Code that automates the process of investigating codebases and documentation using a structured, multi-phase agent workflow.

Can I adjust the depth of the research?

Yes, you can specify depth levels such as shallow, medium, or deep to balance the trade-off between execution speed and thoroughness.

How does it provide evidence for its findings?

The skill requires all analysis agents to cite specific file paths and line ranges for every claim made during the investigation phase.

What specialized agents are included?

The skill orchestrates specialized agents including codebase-locators, research-analyzers, pattern-finders, and conditional experts for web searches or security audits.

Does it support external knowledge investigation?

Yes, for complex research tasks, it can spawn domain-specific agents like the web-search-researcher to incorporate external context and best practices.

Comprehensive Research

Name: Comprehensive Research
Author: v1truv1us

byv1truv1us

Learning & Documentation

Conducts systematic, multi-phase investigations across codebases and documentation to generate structured, evidence-based insights.

About

This skill implements a disciplined, agent-based research orchestration workflow designed for deep codebase exploration and architectural analysis. By coordinating parallel discovery agents and sequential deep analyzers, it systematically identifies patterns, extracts historical context from documentation, and provides evidence-backed recommendations with file-specific references. It is particularly useful for onboarding to complex projects, auditing system architectures, or performing thorough due diligence before major implementation phases, ensuring that no technical debt or architectural constraint is overlooked.

Key Features

Multi-agent orchestration for parallel discovery and sequential analysis
Evidence-based findings with precise file and line-number references
0 GitHub stars
Synthesis of codebase patterns, architectural insights, and historical context
Incentive-based prompting to ensure high-quality, expert-level analysis
Configurable research depth levels ranging from shallow to deep investigation

Use Cases

Investigating historical design decisions within project documentation to inform new features
Analyzing complex legacy codebases to understand system architecture and dependencies
Conducting pre-implementation research and risk assessment for major refactors

About

Key Features

Multi-agent orchestration for parallel discovery and sequential analysis
Evidence-based findings with precise file and line-number references
0 GitHub stars
Synthesis of codebase patterns, architectural insights, and historical context
Incentive-based prompting to ensure high-quality, expert-level analysis
Configurable research depth levels ranging from shallow to deep investigation

Use Cases

Investigating historical design decisions within project documentation to inform new features
Analyzing complex legacy codebases to understand system architecture and dependencies
Conducting pre-implementation research and risk assessment for major refactors