Databricks

Databricks

Databricks skills for the CLI, Apps, Lakebase, Model Serving, Lakeflow Jobs, Spark Declarative Pipelines, Declarative Automation Bundles (DABs), and classic-to-serverless migration.

Created by DatabricksVerified by CursorView Source
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.