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databricks-model-serving
Cursor Skill
Databricks Model Serving (ops) plus MLflow model development (dev): manage serving endpoints, train and register models to Unity Catalog with @prod aliases, batch-score via spark_udf, build custom PyFunc / ResponsesAgent models, and discover Foundation Model API endpoints.
Databricks skills for the CLI, Apps, Lakebase, Model Serving, Lakeflow Jobs, Spark Declarative Pipelines, Declarative Automation Bundles (DABs), and classic-to-serverless migration.
Skills10
databricks-app-designDesign the UX of Databricks data apps — dashboards, KPI pages, reports, charts, tables, and Genie/chat data assistants — mapped to concrete AppKit components. Use when BUILDING or reviewing any UI that displays data or answers data questions: choosing genre, layout, charts, KPIs, semantic color, required states (loading/empty/error), IBCS notation, and AI-result trust (showing generated SQL/sources for Genie/chat). NOT for authoring managed AI/BI (Lakeview) dashboards (→ databricks-aibi-dashboards), non-data frontend (forms, settings, auth, marketing), or scaffolding/build/deploy (→ databricks-apps). Complements databricks-apps; use it alongside whenever the app has a dashboard, chart, table, KPI, report, or Genie/chat/AI surface.
databricks-appsBuild apps on Databricks Apps platform. Use when asked to create dashboards, data apps, analytics tools, or visualizations. Evaluates data access patterns (analytics vs Lakebase synced tables) before scaffolding. Invoke BEFORE starting implementation.
databricks-coreDatabricks CLI operations and the parent/entry-point skill for all Databricks work: authentication, profile selection, data exploration, bundles, and Genie natural-language data Q&A. Load this first for any Databricks task (CLI, auth, profiles, exploring catalogs/tables), then load the matching product skill. Contains up-to-date guidelines for Databricks-related CLI tasks.
databricks-dabsCreate, configure, validate, deploy, run, and manage Declarative Automation Bundles (DABs, formerly Databricks Asset Bundles). Use when working with Databricks resources via DABs including dashboards, jobs, pipelines, alerts, volumes, and apps.
databricks-jobsDevelop and deploy Lakeflow Jobs on Databricks via DABs, Python SDK, or the CLI. Use when creating data engineering jobs with notebooks, Python wheels, SQL, dbt, or pipelines. Invoke BEFORE starting implementation.
Rules2
databricks-routingRouting for any Databricks task -- CLI, auth, profiles, data exploration, Jobs/Lakeflow, Spark Declarative Pipelines (formerly DLT), Apps/AppKit, Asset Bundles/DABs, Model Serving, Lakebase/Postgres, Vector Search/RAG, Genie, and classic-to-serverless migration. Apply when the request is Databricks-related so the Databricks skills are loaded instead of ad hoc commands.
readme
Commands2
databricks-doctorRead-only Databricks health check: CLI, profiles, auth validity via one API call. Add `full` after the command to also check compute and recent job failures.
databricks-setupSet up Databricks CLI auth: install check, then an OAuth / PAT / service-principal profile (workspace or account-level), then verify.