William-Lu-stack/Flawless

This issue reveals a fundamental data consistency flaw within the `ops skill registry` of an AI SRE AgenticOps platform. The system stores skills using normalized IDs but attempts to retrieve, update, or delete them using raw, unnormalized IDs. This inconsistency renders core management functions like `upsert`, `delete`, and `export_package` unreliable, effectively making existing skills unmanageable if their IDs are not pre-normalized. Such defects directly impede an operator's ability to manage the AI agents' capabilities, leading to operational friction, data integrity issues, and potential service degradation. For B2B SaaS, this represents a critical usability and reliability failure, demanding immediate architectural correction to ensure predictable system behavior and maintain operational trust.