Skill Ownership

Rules without owners decay. Nobody reviews them, nobody notices when the policy they encode is superseded, and when they misfire there is no one to ask. Ownership assigns a person who understands the underlying policy — not the engineer who typed it — and pairs them with a review cadence so the rule is revisited on a schedule rather than when something breaks. The most common failure is owning the mechanism instead of the meaning: the engineering team owns all the skills, which guarantees the policy content ages.

Skill Owner and Review Cadence are fields on the skill record in the AI Skill Manager, so accountability and review frequency are properties of the rule rather than conventions held elsewhere. The Skills Registry exposes the current set as a live feed, which makes an inventory of unowned or overdue skills a query.