Can this skill identify specific code patterns?

Yes, it includes a specialized codebase-pattern-finder agent specifically designed to recognize recurring implementation styles and architectural patterns.

How does the multi-phase methodology work?

It follows a four-phase process: Scope Definition, Parallel Discovery (finding files), Sequential Analysis (understanding logic), and Synthesis (creating documentation).

What is the primary benefit of the Comprehensive Research skill?

It automates the tedious process of manual codebase investigation, ensuring that complex problems are solved using all available context from code, docs, and historical decisions.

What does the Research Confidence Assessment indicate?

It provides a score from 0.0 to 1.0 based on evidence coverage. A score above 0.8 indicates comprehensive evidence and documented claims, while lower scores highlight areas requiring more investigation.

Comprehensive Research Orchestrator

Name: Comprehensive Research Orchestrator
Author: v1truv1us

byv1truv1us

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Herramientas para Desarrolladores

Orchestrates multi-phase investigations across codebases and documentation to provide deep technical insights and evidence-based recommendations.

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The Comprehensive Research skill provides a systematic framework for deep-dive technical analysis within Claude Code. It implements a sophisticated multi-agent orchestration workflow—spanning parallel discovery, sequential analysis, and final synthesis—to ensure no stone is left unturned. By automating the identification of code patterns, historical architectural decisions, and relevant documentation, it helps developers ground their implementation decisions in evidence rather than assumptions, significantly reducing rework and technical debt.

Características Principales

Parallel discovery agents for simultaneous codebase, pattern, and documentation scanning.
Built-in confidence assessment (0.0 to 1.0) to quantify research reliability and identify gaps.
Structured research synthesis providing synopsis, architectural insights, and actionable recommendations.
Sequential deep analysis that builds evidence chains with specific file and line references.
Hierarchical caching strategy to optimize performance and reduce redundant analysis across sessions.
2 GitHub stars

Casos de Uso

Investigating technical debt by synthesizing historical documentation with current implementation realities.
Onboarding to unfamiliar codebases by automatically mapping core patterns and architectural constraints.
Pre-implementation research for complex features to ensure alignment with existing system patterns.

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Características Principales

Parallel discovery agents for simultaneous codebase, pattern, and documentation scanning.
Built-in confidence assessment (0.0 to 1.0) to quantify research reliability and identify gaps.
Structured research synthesis providing synopsis, architectural insights, and actionable recommendations.
Sequential deep analysis that builds evidence chains with specific file and line references.
Hierarchical caching strategy to optimize performance and reduce redundant analysis across sessions.
2 GitHub stars

Casos de Uso

Investigating technical debt by synthesizing historical documentation with current implementation realities.
Onboarding to unfamiliar codebases by automatically mapping core patterns and architectural constraints.
Pre-implementation research for complex features to ensure alignment with existing system patterns.