Does this skill help with project documentation?

Yes, the final phase (Knowledge Capture) focuses on storing discoveries in memory and updating PATTERNS.md or documentation files to ensure findings are never lost.

How does this differ from the default development workflow?

While the default workflow focuses on implementation and building, the Investigation Workflow is optimized specifically for research, discovery, and building a mental model of existing systems.

When does this skill auto-activate?

It activates automatically when Claude detects research-oriented prompts like 'explain the architecture', 'analyze how this works', or 'research why the system fails'.

What are Parallel Deep Dives?

This feature allows the system to deploy multiple specialized agents simultaneously to analyze different modules or architectural layers, significantly speeding up the information-gathering process.

Investigation Workflow

Name: Investigation Workflow
Author: rysweet

byrysweet

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생산성 및 워크플로

Provides a systematic 6-phase process for analyzing and documenting complex codebases and architectures using parallel agent deployment.

소개

The Investigation Workflow skill transforms how developers explore unfamiliar systems by shifting focus from implementation to deep understanding. It utilizes a structured six-phase framework—spanning scope definition, parallel exploration, and knowledge capture—to automate discovery. By prioritizing strategic analysis over random code wandering and verifying findings through practical testing, it reduces total message counts by up to 40% while ensuring all insights are stored permanently in the system's memory for future reference.

주요 기능

Practical verification and hypothesis testing
Automated knowledge capture to persistent memory
Parallel agent deployment for multi-module deep dives
6-Phase structured exploration framework
Efficiency optimization targeting 30-40% fewer messages
16 GitHub stars

사용 사례

Reverse-engineering undocumented APIs and internal logic flows
Onboarding to a legacy codebase or large system architecture
Root cause analysis for complex system-wide failures

소개

주요 기능

Practical verification and hypothesis testing
Automated knowledge capture to persistent memory
Parallel agent deployment for multi-module deep dives
6-Phase structured exploration framework
Efficiency optimization targeting 30-40% fewer messages
16 GitHub stars

사용 사례

Reverse-engineering undocumented APIs and internal logic flows
Onboarding to a legacy codebase or large system architecture
Root cause analysis for complex system-wide failures