How does this skill improve research accuracy?

It uses a systematic multi-phase approach—Discovery, Analysis, and Synthesis—coordinating multiple specialized agents to cross-reference evidence and validate findings before presenting conclusions.

How does it handle large-scale codebases without timing out?

The skill utilizes a parallel discovery phase to map the environment quickly and a hierarchical caching strategy to store coordination patterns and results, improving efficiency in large projects.

What is the Research Confidence Assessment?

It is a self-diagnostic tool that rates the thoroughness of the investigation on a scale of 0.0 to 1.0, highlighting specific areas of uncertainty or missing evidence.

Can I use this for external research beyond the local codebase?

Yes, the skill can spawn a web-search-researcher agent to gather external context and documentation when the local codebase does not provide sufficient information.

Comprehensive Research Orchestrator

Name: Comprehensive Research Orchestrator
Author: v1truv1us

byv1truv1us

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Productivity & Workflow

Orchestrates multi-phase research across codebases and documentation to provide evidence-backed insights and architectural synthesis.

The Comprehensive Research skill transforms how developers investigate complex systems by automating a systematic, multi-agent research workflow. It coordinates parallel discovery agents to scan files and documentation, followed by sequential analysis agents that build evidence-based chains of reasoning. By moving beyond surface-level code reads, this skill ensures that implementation decisions are grounded in historical context, recognized patterns, and thorough architectural understanding, ultimately reducing rework and technical debt.

Key Features

012 GitHub stars

02Multi-phase orchestration including Parallel Discovery and Sequential Analysis

03Evidence-based findings with specific file and line-number references

04Structured synthesis reporting with actionable recommendations and risk assessments

05Quantitative Research Confidence Assessment (0.0 to 1.0 scale)

06Hierarchical caching strategy for optimized performance and response times

Use Cases

01Deep-diving into legacy codebases to uncover historical architectural decisions

02Synthesizing internal documentation with external knowledge for comprehensive technical discovery

03Performing impact analysis and pattern discovery before major system refactors

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add v1truv1us/ai-eng-system comprehensive-research

For use in Claude.ai and ChatGPT

Download Skill