How is research quality validated?

The skill includes a Research Confidence Assessment that rates findings from 0.0 to 1.0 based on evidence comprehensiveness, historical context, and the clarity of documented claims.

How do I trigger the research skill?

The skill is invoked within the Claude Code environment using the /ai-eng/research command followed by your research objective.

What is the benefit of the parallel discovery phase?

Parallel discovery allows multiple locator agents to simultaneously scan for code files, documentation, and implementation patterns, significantly reducing the total time required for the initial investigation.

Does it provide actionable outputs?

Every research cycle concludes with a Synthesis Phase that generates a structured report containing immediate action items, long-term considerations, and risk assessments.

Can this skill search external information?

Yes, for complex research tasks, the workflow can trigger a web-search-researcher agent to gather external context, security standards, or library documentation.

Comprehensive Research Orchestrator

Name: Comprehensive Research Orchestrator
Author: v1truv1us

byv1truv1us

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开发者工具

Orchestrates multi-phase investigations across codebases, documentation, and external sources to drive informed technical decision-making.

关于

The Comprehensive Research Skill provides a systematic framework for deep-dive technical investigations within Claude Code. By coordinating specialized agents for discovery, analysis, and synthesis, it transforms raw codebases and fragmented documentation into structured, actionable insights. It employs a rigorous multi-phase methodology—ranging from parallel discovery of patterns to sequential deep analysis with evidence chains—ensuring that architectural decisions are grounded in historical context and verified evidence rather than assumptions. This skill is essential for minimizing rework and avoiding historical mistakes in complex software environments.

主要功能

2 GitHub stars
Hierarchical caching strategy for optimized performance and reduced latency
Automated research confidence scoring (0.0 to 1.0) to assess investigation depth
Sequential deep analysis with specific file and line-level evidence chains
Multi-agent parallel discovery for rapid codebase and documentation scanning
Structured synthesis reports including synopsis, architectural insights, and recommendations

使用场景

Synthesizing external web context with internal implementation patterns for new feature planning
Investigating historical architectural decisions and constraints in existing documentation
Analyzing complex legacy codebases before implementing major refactors

关于

主要功能

2 GitHub stars
Hierarchical caching strategy for optimized performance and reduced latency
Automated research confidence scoring (0.0 to 1.0) to assess investigation depth
Sequential deep analysis with specific file and line-level evidence chains
Multi-agent parallel discovery for rapid codebase and documentation scanning
Structured synthesis reports including synopsis, architectural insights, and recommendations

使用场景

Synthesizing external web context with internal implementation patterns for new feature planning
Investigating historical architectural decisions and constraints in existing documentation
Analyzing complex legacy codebases before implementing major refactors