{"skill":{"slug":"data-model","displayName":"Data Model","summary":"Deep data modeling workflow—grain, facts and dimensions, keys, slowly changing dimensions, normalization trade-offs, and analytics query patterns. Use when d...","tags":{"latest":"1.0.0"},"stats":{"comments":0,"downloads":239,"installsAllTime":1,"installsCurrent":1,"stars":0,"versions":1},"createdAt":1774601247165,"updatedAt":1774601518790},"latestVersion":{"version":"1.0.0","createdAt":1774601247165,"changelog":"- Initial release of the \"data-model\" skill for analytics and warehouse design.\n- Introduces a six-stage workflow covering grain, conformed dimensions, facts & measures, SCD strategies, key management, and performance considerations.\n- Provides checklists and best practices for schema design, additive measures, dimension conformance, and SCD policy selection.\n- Offers guidance for handling common pitfalls (fan/chasm traps, late-arriving facts) and adapting to event-based pipelines.\n- Designed to support both star and snowflake schema reviews and implementations.","license":"MIT-0"},"metadata":null,"owner":{"handle":"clawkk","userId":"s170g5yz1q3ksjnn4gz6v24af983h1mh","displayName":"clawkk","image":"https://avatars.githubusercontent.com/u/265748372?v=4"},"moderation":null}