Agent Documentation Management
Enforces documentation standards, structure, and best practices for agent-focused repositories.
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Key features
- Enforces strict frontmatter requirements and document structure templates.
- Defines clear guidelines for hierarchical category placement and organization.
- Includes a step-by-step workflow for refactoring and reorganizing documents.
- Provides critical guidance and anti-patterns for embedding Python code in documentation.
- Manages auto-generated index and metadata files via project-specific tooling.
Use cases
- Creating a new documentation page for an agent-focused feature.
- Refactoring existing documentation by moving files between categories.
- Updating a document's metadata to improve its discoverability by an AI agent.
FAQ
What does the Agent Documentation Management skill do?
This skill makes Claude an expert at managing documentation for agent-focused repositories. It enforces strict standards for structure, YAML frontmatter, and category organization to ensure all documentation is consistent, discoverable, and maintainable.
How does this skill improve my workflow?
It prevents common documentation errors like inconsistent formatting and outdated code snippets. By providing clear templates, refactoring workflows, and automating index generation, it reduces manual effort and ensures agents rely on high-quality, accurate information.
What specific capabilities does it provide?
It provides strict frontmatter and document structure templates, guidance on avoiding code-in-docs anti-patterns, clear rules for category organization, and tooling to manage auto-generated index and metadata files, keeping navigation up-to-date.
When should I use this skill?
Use this skill whenever you are creating new documents, updating metadata, choosing categories, or reorganizing files within a project's agent documentation. It's essential for maintaining the integrity and structure of the knowledge base.
What is the most critical rule this skill enforces?
Its most critical rule is to NEVER embed Python functions with business logic in documentation. This prevents AI agents from copying stale code that can cause bugs, instead pointing them to the canonical, tested implementation in the source code.
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