Skill Freshness Audit

Ensures all Agent Skills stay current with official Databricks, MLflow, and platform documentation through systematic verification, staleness detection, and drift reporting.

When to Use

• Periodic skill audits (recommended: monthly for high-volatility, quarterly for medium)
• After a Databricks or MLflow platform release
• When a skill produces incorrect patterns during implementation
• When user says "audit skills", "check freshness", or "verify skills"
• Before major implementations to ensure skill accuracy

Freshness Metadata Schema

Every skill's frontmatter should include these three fields: