What happens if the evidence is incomplete?

The skill explicitly flags '△ Caveats,' documenting any assumptions made, remaining information gaps, and unknowns to ensure you never mistake a guess for a fact.

What sources does this skill analyze to understand code?

It examines direct source code, official documentation, READMEs, test files, git commit history, and external technical references to build a holistic understanding of the system.

How does the confidence tracking system work?

The skill uses a 5-level scale (0-5) ranging from initial gathering to fully synthesized conclusions. It provides a visual progress bar and explicitly states its certainty level before delivering findings.

Can this skill help with security audits?

Yes, it is designed for deep-dive technical research, including security and performance analysis, by tracing execution paths and identifying potential edge cases or vulnerabilities.

How does it handle complex execution paths?

It follows a 'trail' investigation pattern, tracing function calls, imports/exports, and dependency maps to understand how different parts of the system interact.

Codebase Analysis & Architecture Investigation

Name: Codebase Analysis & Architecture Investigation
Author: outfitter-dev

byoutfitter-dev

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Herramientas para Desarrolladores

Conducts evidence-based investigations of software architectures to provide high-confidence technical insights and pattern recognition.

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The Codebase Analysis skill transforms how you explore complex repositories by replacing guesswork with a structured, evidence-based methodology. It systematically gathers data from source code, documentation, test suites, and git history to map out architectures, identify recurring patterns, and solve technical mysteries with a verifiable confidence rating. Whether you're onboarding to a new project or conducting a deep-dive technical audit, this skill ensures every conclusion is backed by direct observation and transparent reasoning, including explicit tracking of assumptions and knowledge gaps.

Características Principales

16 GitHub stars
Cross-referenced verification to distinguish facts from inferences
Architectural mapping and systemic pattern recognition
Evidence-based investigation with 0-5 confidence level tracking
Multi-source analysis across code, docs, tests, and git history
Transparent investigation trails that document gaps and caveats

Casos de Uso

Onboarding and exploring large or unfamiliar codebases
Performing technical research for refactoring, security, or performance audits
Mapping system architecture and identifying core design patterns

Acerca de

Características Principales

16 GitHub stars
Cross-referenced verification to distinguish facts from inferences
Architectural mapping and systemic pattern recognition
Evidence-based investigation with 0-5 confidence level tracking
Multi-source analysis across code, docs, tests, and git history
Transparent investigation trails that document gaps and caveats

Casos de Uso

Onboarding and exploring large or unfamiliar codebases
Performing technical research for refactoring, security, or performance audits
Mapping system architecture and identifying core design patterns