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Found 86 Skills
Manage Lakebase Postgres Autoscaling projects, branches, and endpoints via Databricks CLI. Use when asked to create, configure, or manage Lakebase Postgres databases, projects, branches, computes, or endpoints.
Databricks integration. Manage Workspaces. Use when the user wants to interact with Databricks data.
Databricks SQL query optimizer: analyzes a slow SQL query, rewrites it for speed using SQL-level optimizations only, validates byte-for-byte result equivalence, and benchmarks both versions with statistical significance testing. Use this skill whenever the user wants to optimize, speed up, tune, or benchmark a SQL query on Databricks. Trigger on: "/databricks-sql-autotuner", "optimize this SQL", "make this query faster", "tune my Databricks query", "benchmark SQL on Databricks", "speed up this spark SQL", "SQL performance on Databricks", "EXPLAIN this query", "why is my query slow on Databricks", "SQL query optimization Databricks", or whenever a user pastes a SQL query and mentions performance, slowness, or runtime.
Discover available tools and resources in Databricks workspace. Use when: (1) User asks 'what tools are available', (2) Before writing agent code, (3) Looking for MCP servers, Genie spaces, UC functions, or vector search indexes, (4) User says 'discover', 'find resources', or 'what can I connect to'.
Design the UX of custom-code Databricks Apps (AppKit/React) data screens — KPI/overview pages, reports, charts, tables, and Genie/chat data assistants — mapped to concrete AppKit components. Use when BUILDING or reviewing the UI of an AppKit/React app 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). A plain "create a dashboard" request means a managed AI/BI (Lakeview) dashboard → use databricks-aibi-dashboards, NOT this skill. Also NOT for non-data frontend (forms, settings, auth, marketing) or scaffolding/build/deploy (→ databricks-apps). Complements databricks-apps; use it alongside whenever a custom app has a chart, table, KPI, report, or Genie/chat/AI surface.
Develop and deploy Lakeflow Jobs on Databricks. Use when creating data engineering jobs with notebooks, Python wheels, or SQL tasks. Invoke BEFORE starting implementation.
GitHub Actions and CI/CD patterns for Databricks, including automated testing, deployment, and quality gates.
Unity Catalog metric views: define, create, query, and manage governed business metrics in YAML. Use when building standardized KPIs, revenue metrics, order analytics, or any reusable business metrics that need consistent definitions across teams and tools.
Expert knowledge for Azure Databricks development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using Unity Catalog, Lakeflow, Lakebase, Delta Sharing, Databricks SQL, or Model Serving workloads, and other Azure Databricks related development tasks. Not for Azure Synapse Analytics (use azure-synapse-analytics), Azure HDInsight (use azure-hdinsight), Azure Machine Learning (use azure-machine-learning), Azure Data Factory (use azure-data-factory).
Manage Databricks Model Serving endpoints via CLI. Use when asked to create, configure, query, or manage model serving endpoints for LLM inference, custom models, or external models.
Deploy agent to Databricks Apps using DAB (Databricks Asset Bundles). Use when: (1) User says 'deploy', 'push to databricks', or 'bundle deploy', (2) 'App already exists' error occurs, (3) Need to bind/unbind existing apps, (4) Debugging deployed apps, (5) Querying deployed app endpoints.
Creates, configures, and updates Databricks Lakeflow Spark Declarative Pipelines (SDP/LDP) using serverless compute. Handles streaming tables, materialized views, CDC, SCD Type 2, and Auto Loader ingestion patterns. Use when building data pipelines, working with Delta Live Tables, ingesting streaming data, implementing change data capture, or when the user mentions SDP, LDP, DLT, Lakeflow pipelines, streaming tables, or bronze/silver/gold medallion architectures.