skill-freshness-audit

Audit Agent Skills for staleness against official documentation and upstream lineage.

1|Updated Jan 22, 2026

Search Tags:#MLflow #skill audit #upstream sync #documentation verification #freshness check #AI dev kit

Ready to run

npx skills add https://github.com/prashsub/vibe_coding_lakehouse_starter_repo --skill skill-freshness-audit-prashsub

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: skill-freshness-audit
Source: https://github.com/prashsub/vibe_coding_lakehouse_starter_repo/tree/main/data_product_accelerator/skills/admin/skill-freshness-audit
Command: npx skills add https://github.com/prashsub/vibe_coding_lakehouse_starter_repo --skill skill-freshness-audit-prashsub

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill ensures that all Agent Skills remain current with official documentation and upstream changes, preventing outdated information and potential implementation errors.

Core Features & Use Cases

Systematic Auditing: Regularly checks skills against live documentation and upstream sources.
Staleness Detection: Identifies skills that haven't been verified recently based on their volatility.
Upstream Lineage Tracking: Monitors changes in dependent repositories like ai-dev-kit.
Use Case: Before a major platform release, run this skill to identify and update all potentially affected skills, ensuring your AI agents are built on the latest patterns and APIs.

Quick Start

Use the skill-freshness-audit skill to check for stale skills.

Frequently Asked Questions about skill-freshness-audit

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I check if my MLflow agent skills are outdated against official documentation?▼

To check if MLflow agent skills are outdated, perform a systematic freshness audit to verify skill content against live official documentation and track upstream lineage. This process identifies staleness and reports drift to ensure your skills match current APIs.

What is upstream lineage tracking for AI dev kit skills?▼

Upstream lineage tracking for AI dev kit skills is the process of monitoring changes in dependent repositories to detect when updates occur. It classifies skill volatility and reports drift to prevent implementation errors from outdated information.

How do I detect staleness in Databricks agent skills before a platform release?▼

To detect staleness in Databricks agent skills before a platform release, run a freshness audit that checks skills against live documentation. It identifies skills that haven't been verified recently based on their classified volatility levels.

What is the best way to automate documentation verification for agent skills?▼

The best way to automate documentation verification for agent skills is performing a systematic skill freshness audit. This approach regularly checks skills against live documentation sources, identifies staleness, and reports drift across frameworks like Databricks and MLflow.

Does the skill freshness audit work with custom AI dev kit repositories?▼

The skill freshness audit works with custom AI dev kit repositories by tracking upstream lineage and monitoring changes in dependent repositories. It systematically verifies skills against official documentation to identify staleness and report drift.