librarian

To maintain a living codebase knowledge base, use a Skill that auto-retrieves relevant architecture files before tasks and records design decisions after changes. It indexes markdown files using YAML frontmatter to provide faster, accurate AI context.

The best way to document architecture decisions after refactoring is to record non-obvious patterns and gotchas in a markdown knowledge base. The Skill verifies factual mismatches against the current repository and updates existing entries to keep knowledge accurate.

Auto-retrieval works by reading a central index file and selectively loading the most relevant architecture, decision, and documentation files before starting non-trivial tasks. This ensures the AI has the necessary context without manual searching.

Yes, you need YAML frontmatter to index your codebase knowledge base. The Skill requires YAML frontmatter-driven indexing to selectively retrieve and load the most relevant architecture and decision files for AI context.

Yes, you can use this knowledge management Skill for onboarding. It applies to onboarding by answering how and why questions about the codebase architecture, allowing new developers to quickly access verified design decisions and documentation.

You should not use a markdown knowledge base for codebase documentation when changes have trivial architectural impact. The Skill uses controlled recording to ensure only non-trivial design decisions and architectural changes are documented.