aurora-schema

To validate Aurora YAML schemas, analyze module files to ensure field definitions, descriptions, types, and module semantics meet defined DDD standards. This enforces frontmatter requirements, mandatory fields, and naming conventions for consistency.

Aurora YAML module validation checks field names, descriptions, types, and semantic alignment against DDD best practices. You need it to enforce consistency and correct documentation across repositories during module design and reviews.

Fix naming and description gaps in YAML schemas by editing and refining them to conform to DDD best practices. Apply standardized patterns from type-reference.md and examples.md to improve module documentation and inter-module consistency.

Yes, you can apply schema validation across repositories. The validation mechanism analyzes Aurora YAML module files to enforce frontmatter requirements, mandatory fields, naming conventions, and semantic guidance consistently across all your modules.

Linting Aurora YAML schemas is limited to enforcing field definitions, frontmatter requirements, naming conventions, and semantic alignment with related references. It focuses on structural and descriptive consistency rather than runtime behavior or business logic validation.