Is this documentation format useful for human developers?

Absolutely. While optimized for AI comprehension, the standardized structure makes it an excellent resource for human onboarding and technical reference.

Can I exclude certain files from the context?

Yes, you can use a .contextignore file, which functions similarly to a .gitignore, to prevent the AI from indexing build artifacts or sensitive files.

What is a .context.md file?

A .context.md file is a specialized markdown document with YAML frontmatter that provides structured metadata and descriptions about a specific part of a codebase for AI tools.

How does context inheritance work in this skill?

The skill follows a proximity precedence model where context files in subdirectories inherit settings from parent directories but can override specific values locally.

Codebase Context Documentation

Name: Codebase Context Documentation
Author: seanchatmangpt

byseanchatmangpt

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Learning & Documentation

Generates and maintains AI-optimized project documentation using the Codebase Context Specification.

About

The Codebase Context skill implements a structured documentation framework designed to make complex projects instantly navigable for AI agents and human developers alike. By creating a hierarchical network of .context.md files featuring YAML frontmatter, it defines architectural patterns, module relationships, and development conventions. This skill ensures that your project's technical debt is minimized through standardized onboarding guides and persistent context accumulation, allowing Claude to provide more accurate, domain-specific assistance based on the actual rules of your codebase.

Key Features

Standardized YAML frontmatter for machine-readable architecture metadata
Hierarchical .context.md generation for project and module levels
Integration of external documentation via .contextdocs.md references
Support for .contextignore to filter irrelevant build and IDE files
Automated context inheritance and proximity-based override rules
3 GitHub stars

Use Cases

Establishing a structured knowledge base for legacy codebase onboarding
Creating AI-friendly project maps to improve code generation accuracy
Documenting complex architectural decisions and data-flow patterns

About

Key Features

Standardized YAML frontmatter for machine-readable architecture metadata
Hierarchical .context.md generation for project and module levels
Integration of external documentation via .contextdocs.md references
Support for .contextignore to filter irrelevant build and IDE files
Automated context inheritance and proximity-based override rules
3 GitHub stars

Use Cases

Establishing a structured knowledge base for legacy codebase onboarding
Creating AI-friendly project maps to improve code generation accuracy
Documenting complex architectural decisions and data-flow patterns