op-skill-lifecycle

Managing the AI skill lifecycle with validation gates involves using reproducible steps for creation, updates, deprecation, and repair. This structured governance approach ensures changes are minimal, validated, and easily reversible before deployment.

Scaffolding new AI skills from predefined templates requires a structured creation process that ensures proper definition and integration. This approach validates the skill against security rules before deployment, minimizing integration risks.

Deprecating or deleting AI skills safely requires a systematic removal process managed through explicit governance workflows. This ensures the skill ecosystem remains healthy and functional without leaving broken dependencies behind.

Skill repair requires root-cause analysis and patching to address broken workflows at their origin. This governance approach fixes the underlying issue rather than applying temporary overrides, maintaining ecosystem functionality.

You need Python3 installed to automate skill management and governance workflows using the provided scripts. This dependency supports the execution of reproducible lifecycle steps and validation logic.

Explicit validation gates for skill updates should be used when maintaining a healthy skill ecosystem. They enforce minimal diffs, security rules, and require explicit reviewer approval for security-sensitive modifications during governance workflows.