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Found 5 Skills
Produces Go/No-Go deployment checklists with SQL verification queries, rollback procedures, and monitoring plans. Use when PRs touch production data, migrations, or risky data changes.
Create Databricks AI/BI dashboards. Must use when creating, updating, or deploying Lakeview dashboards as Databricks Dashboard have a unique json structure. CRITICAL: You MUST test ALL SQL queries via CLI BEFORE deploying. Follow guidelines strictly.
Build AI that answers questions about your database. Use when you need text-to-SQL, natural language database queries, a data assistant for non-technical users, AI-powered analytics, plain English database search, or a chatbot that talks to your database. Covers DSPy pipelines for schema understanding, SQL generation, validation, and result interpretation.
Audit a Databricks Lakeview dashboard's widgets against the SQL that actually powers them, producing a findings document of claim vs. compute.
Read, create, edit, and delete the cube definitions behind an org's RevOS semantic model, through the RevOS MCP server. Use this whenever the user wants to change what the semantic model contains, even if they never say "cube": model a new table or source stream ("make HubSpot tickets queryable"), add or fix a dimension or measure ("add a revenue sum", "the close date should be a time field"), join two cubes ("link deals to companies"), rename or remove a field, delete a cube, see how a cube is defined ("show me the SQL behind crm_deals"), or find out why the semantic model or a table suddenly fails to load. Read it before any api_write or api_delete on the `cubes` resource. For answering business questions from the data, use query-semantic-model instead; for audiences, use segments.