meta-adopt-skill

Securely evaluate external AI skills by running an automated audit that inspects skill metadata, script logic, and dependencies to flag high-risk behaviors like unauthorized API calls before integration. This prevents malicious or redundant tools from entering your workflow.

Skill overlap analysis prevents functional redundancy by comparing new external AI skills against your existing catalog to identify conflicting capabilities and naming conventions. This governance step ensures your workflow remains efficient and avoids duplicate tooling.

Standardize adopted AI skills by enforcing quality gates that require external tools to match your project's specific naming conventions and documentation standards before they are cataloged. This governance ensures consistency across your automated workflows.

Yes, the skill audit process detects arbitrary command execution by performing systematic audits of script logic and dependency requirements. It automatically flags these high-risk behaviors during the integration evaluation phase.

Yes, you can evaluate AI skills with no external dependencies. The framework systematically audits the provided metadata and script logic to ensure architectural fit and security, regardless of whether the skill requires external packages.