skill-improver

Automating AI skill quality review involves using an agent to iteratively assess and refine SKILL.md files. This process targets YAML frontmatter correction, documentation clarity, and structural integrity through a structured validation loop.

Fixing YAML frontmatter and structural issues in AI skills is best handled by an iterative refinement loop. This approach categorizes issue severity and guides the AI through automated corrections to ensure operational standards are met.

Yes, the skill-reviewer agent is required to execute the validation and improvement loop for SKILL.md files. It identifies critical, major, and minor issues, while the plugin-dev plugin is also required to run the automation.

Iterative refinement for AI skill documentation works by running automated quality checks that catch missing frontmatter or vague descriptions. The AI undergoes a structured loop to fix categorized issues and verify improvements before deployment.

Yes, you can use an automated loop to catch missing frontmatter and vague descriptions before deploying AI skills. This automated quality review validates definitions and ensures every skill meets strict operational standards prior to release.