Validate Aurora YAML module files for field definitions and naming conventions.
npx skills add https://github.com/avvale/aurora-front --skill aurora-schema
Please help me install this Agent Skill. Skill: aurora-schema Source: https://github.com/avvale/aurora-front/tree/main/.claude/skills/aurora-schema Command: npx skills add https://github.com/avvale/aurora-front --skill aurora-schema
This Skill helps teams validate and edit Aurora YAML schemas, ensuring field names, descriptions, types, and module semantics adhere to DDD best practices.
Use this skill to validate a module file such as my-context/order.aurora.yaml and fix any naming or description gaps.
High-intent search queries and answers about installing and using this skill.
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.
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