skill-improver

To audit SKILL.md files for style guide compliance, use an automated refactoring process that validates metadata, section ordering, and decision logic against a normative LLM-first style guide. This normalizes structural inconsistencies across a skill registry.

LLM-first skill standardization is the process of normalizing metadata, section structure, and decision logic in SKILL.md files. You need it when a repository contains legacy skills with inconsistent formatting that degrades AI agent performance.

To refactor branching logic in skill definitions into decision tables, apply an automated refactoring tool that converts prose-based conditions into compact decision tables and moves explanatory text to references for improved agent readability.

Yes, skill auditing requires access to the skill registry and local style guide references to perform accurate structural validation and content migration for LLM-first skill compliance.

The best way to normalize metadata across a legacy skill registry is to run an automated audit that validates all SKILL.md files against a normative style guide and applies structural improvements to ensure operational standards are met.