What problem does it solve? Writing Snowflake semantic view DDL by hand is error-prone, and invalid definitions are often only discovered after deployment. This Skill guides the full lifecycle of building semantic views and validates every DDL statement against a live Snowflake connection before it is finalized. ## Core Features & Use Cases - Semantic View Authoring: Drafts CREATE or ALTER SEMANTIC VIEW statements following the official Snowflake syntax, including dimensions, facts, metrics, synonyms, and comments. - CLI-Based Validation: Executes DDL through the Snowflake CLI (snow sql) against a temporary validation view, iterating until the statement succeeds before applying the real name. - Metadata Enrichment: Reads existing Snowflake table and column comments as the preferred source for synonyms and comments, and uses SELECT DISTINCT queries to discover relationships and data types. - Use Case: A data engineer needs to expose a star schema of orders and customers as a governed semantic layer. The Skill drafts the semantic view, validates it on a temporary name, applies the final DDL, and confirms it with a sample SEMANTIC_VIEW query. ## Quick Start Ask the assistant to create and validate a Snowflake semantic view for your fact and dimension tables using your configured Snowflake CLI connection.
