Does it require a specific project structure to work?

No, it is designed to analyze what currently exists, regardless of the framework. It documents the reality of your codebase rather than enforcing a specific template.

What documentation does the map skill generate?

It generates seven specialized markdown files: ARCHITECTURE.md, CONVENTIONS.md, STACK.md, STRUCTURE.md, TESTING.md, INTEGRATIONS.md, and CONCERNS.md.

Should I run this on every commit?

It is best used during initial setup and whenever significant changes are made to the project's structure or technology stack to keep the AI's context current.

How does this improve AI-generated code?

By creating a structured map of your project, it provides Claude with explicit instructions on your naming conventions, patterns, and dependencies, leading to fewer errors and more consistent output.

Codebase Context Mapper

Name: Codebase Context Mapper
Author: mikemyl

bymikemyl

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학습 및 문서화

Analyzes and maps your entire codebase to generate structured documentation that provides high-fidelity context for AI development agents.

The Codebase Context Mapper is a foundational tool for the Verified Development workflow that automatically scans your project to produce a comprehensive set of markdown documents. By extracting details on architecture, coding conventions, technology stacks, and technical debt into the .verified/codebase/ directory, it ensures that Claude Code agents have a source of truth to follow. This eliminates hallucinations regarding project structure and ensures that commands like /plan and /implement adhere strictly to your existing patterns and dependencies.

주요 기능

01Comprehensive mapping of directory layouts and module boundaries

02Discovery of external integrations and authentication methods

031 GitHub stars

04Automated extraction of architecture and package structures

05Identification of project-specific coding and testing conventions

06Detection of technical debt, risks, and missing infrastructure

사용 사례

01Providing ground-truth context to ensure AI-generated code follows existing patterns

02Onboarding AI agents to large, complex, or legacy codebases

03Standardizing project documentation for both human developers and AI tools

주요 기능

01Comprehensive mapping of directory layouts and module boundaries

02Discovery of external integrations and authentication methods

031 GitHub stars

04Automated extraction of architecture and package structures

05Identification of project-specific coding and testing conventions

06Detection of technical debt, risks, and missing infrastructure

사용 사례

01Providing ground-truth context to ensure AI-generated code follows existing patterns

02Onboarding AI agents to large, complex, or legacy codebases

03Standardizing project documentation for both human developers and AI tools