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Found 50 Skills
Expert-level Databricks platform, Apache Spark, Delta Lake, MLflow, notebooks, and cluster management
Databricks CLI operations: auth, profiles, Unity Catalog, data exploration, jobs, pipelines, clusters, model serving, bundles and more. Contains up-to-date guidelines for all Databricks CLI tasks, useful for all Databricks-related tasks.
Configure Databricks CI/CD integration with GitHub Actions and Asset Bundles. Use when setting up automated testing, configuring CI pipelines, or integrating Databricks deployments into your build process. Trigger with phrases like "databricks CI", "databricks GitHub Actions", "databricks automated tests", "CI databricks", "databricks pipeline".
Build apps on Databricks Apps platform. Use when asked to create dashboards, data apps, analytics tools, or visualizations. Invoke BEFORE starting implementation.
Set up comprehensive observability for Databricks with metrics, traces, and alerts. Use when implementing monitoring for Databricks jobs, setting up dashboards, or configuring alerting for pipeline health. Trigger with phrases like "databricks monitoring", "databricks metrics", "databricks observability", "monitor databricks", "databricks alerts", "databricks logging".
Migrate an MLflow ResponsesAgent from Databricks Model Serving to Databricks Apps. Use when: (1) User wants to migrate from Model Serving to Apps, (2) User has a ResponsesAgent with predict()/predict_stream() methods, (3) User wants to convert to @invoke/@stream decorators.
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.
Create, configure, validate, deploy, run, and manage DABs — Declarative Automation Bundles (formerly Databricks Asset Bundles) — for Databricks resources including dashboards, jobs, pipelines, alerts, volumes, and apps
Databricks CLI operations: auth, profiles, data exploration, and bundles. Contains up-to-date guidelines for Databricks-related CLI tasks.
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.
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.
Create and configure Databricks Asset Bundles (DABs) with best practices for multi-environment deployments. Use when working with: (1) Creating new DAB projects, (2) Adding resources (dashboards, pipelines, jobs, alerts), (3) Configuring multi-environment deployments, (4) Setting up permissions, (5) Deploying or running bundle resources