Databricks

databricks-app-design

Design 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,

databricks-apps

Build 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-core

Databricks 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 lo

databricks-dabs

Create, 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-jobs

Develop 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.

databricks-lakebase

Databricks Lakebase Postgres: projects, scaling, connectivity, Lakebase synced tables, and Data API. Use when asked about Lakebase databases, OLTP storage, or connecting apps to Postgres on Databricks.

databricks-model-serving

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-pipelines

Develop Lakeflow Spark Declarative Pipelines (formerly Delta Live Tables) on Databricks. Use when building batch or streaming data pipelines with Python or SQL. Invoke BEFORE starting implementation.

databricks-serverless-migration

Migrate Databricks workloads from classic compute to serverless compute. Use when migrating notebooks, jobs, pipelines, or Scala JARs (`spark_jar_task`) from classic clusters to serverless, checking if existing code is serverless-compatible, or writing new serverless-compatible c

databricks-vector-search

Databricks Vector Search endpoints and indexes for RAG and semantic search; covers index types, search modes, end-to-end RAG patterns