skill-vetter

To vet AI skills before installation, apply a stepwise protocol covering source validation, mandatory code review, permission scope evaluation, and risk classification to generate a structured report for deployment.

An AI skill risk assessment includes source checks across repositories like ClawdHub and GitHub, frontmatter validation for SKILL.md files, code review, and permission scope evaluation to classify deployment risks.

Yes, you can use this vetting workflow on AI skills sourced from ClawdHub, GitHub, and other third-party repositories to perform source validation and generate a structured risk assessment report.

To check for mandatory frontmatter, inspect the candidate skill's SKILL.md file to ensure it contains the required name and description fields before proceeding with permission scope evaluation and risk classification.

The best way to generate a security compliance report is running a structured vetting protocol that performs source validation, code review, and risk classification, yielding a shareable report for team review.

You should not rely solely on an automated vetting protocol when a candidate skill lacks a valid SKILL.md with mandatory frontmatter, as source validation and permission scope evaluation require structured input.