How does it handle large-scale codebases efficiently?

The skill uses a Parallel Discovery Phase where multiple agents like codebase-locators and pattern-finders scan the repository simultaneously to map relevant areas quickly.

Can this skill be customized for specific domains?

Yes, for complex research tasks, it can conditionally trigger domain specialists like database experts, security scanners, or system architects to deepen the investigation.

What is the primary benefit of the Comprehensive Research skill?

It provides a systematic, multi-agent approach to technical investigation, ensuring that implementation decisions are grounded in evidence rather than assumptions, which reduces rework and technical debt.

How do I invoke this skill in Claude Code?

The skill is typically triggered by using the /ai-eng/research command, which initiates the context analysis and agent orchestration phases.

What does the Research Confidence Assessment mean?

It is a rating from 0.0 to 1.0 that tells you how thorough the research was. A high score indicates well-documented evidence, while a low score flags missing context or speculative findings.

Comprehensive Research Orchestrator

Name: Comprehensive Research Orchestrator
Author: v1truv1us

byv1truv1us

•

Productividad y Flujo de Trabajo

Conducts deep, multi-phase investigations across codebases and documentation using specialized AI agents for evidence-based decision making.

Acerca de

The Comprehensive Research Orchestrator is a sophisticated workflow skill designed to prevent surface-level analysis and architectural errors. By employing a multi-phase methodology—ranging from parallel discovery to sequential deep analysis—it systematically maps codebases, extracts historical rationale from documentation, and identifies implementation patterns. This skill is essential for developers tackling complex refactors or onboarding to large-scale systems, providing a structured synthesis of findings complete with evidence-based references and confidence scoring.

Características Principales

Multi-phase orchestration involving discovery, analysis, and synthesis
Evidence-based reporting with specific file and line-number references
2 GitHub stars
Integrated confidence assessment and risk identification metrics
Parallel agent execution for rapid codebase and pattern mapping
Historical context extraction to surface past architectural decisions

Casos de Uso

Synthesizing scattered documentation and ADRs into a single source of truth
Onboarding to massive or legacy codebases with minimal documentation
Conducting deep-dive analysis before major architectural refactoring

Acerca de

Características Principales

Multi-phase orchestration involving discovery, analysis, and synthesis
Evidence-based reporting with specific file and line-number references
2 GitHub stars
Integrated confidence assessment and risk identification metrics
Parallel agent execution for rapid codebase and pattern mapping
Historical context extraction to surface past architectural decisions

Casos de Uso

Synthesizing scattered documentation and ADRs into a single source of truth
Onboarding to massive or legacy codebases with minimal documentation
Conducting deep-dive analysis before major architectural refactoring