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Found 62 Skills
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
Patterns and best practices for using Lakebase Provisioned (Databricks managed PostgreSQL) for OLTP workloads.
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 across development, staging, and production environments. Use when setting up multi-environment deployments, configuring per-environment secrets, or implementing environment-specific Databricks configurations. Trigger with phrases like "databricks environments", "databricks staging", "databricks dev prod", "databricks environment setup", "databricks config by env".
Testing framework for evaluating Databricks skills. Use when building test cases for skills, running skill evaluations, comparing skill versions, or creating ground truth datasets with the Generate-Review-Promote (GRP) pipeline. Triggers include "test skill", "evaluate skill", "skill regression", "ground truth", "GRP pipeline", "skill quality", and "skill metrics".
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.
Migrate Databricks workloads from classic compute to serverless compute. Scans code for serverless compatibility issues, provides concrete fixes for the serverless Spark Connect architecture, and guides the full migration to serverless environments. Use for classic-to-serverless migrations, serverless code compatibility checks, or writing new serverless-compatible notebooks and jobs. Not for classic DBR version upgrades or cluster configuration changes within classic compute.
Databricks development guidance including Python SDK, Databricks Connect, CLI, and REST API. Use when working with databricks-sdk, databricks-connect, or Databricks APIs.
Use this skill proactively for ANY Databricks Jobs task - creating, listing, running, updating, or deleting jobs. Triggers include: (1) 'create a job' or 'new job', (2) 'list jobs' or 'show jobs', (3) 'run job' or'trigger job',(4) 'job status' or 'check job', (5) scheduling with cron or triggers, (6) configuring notifications/monitoring, (7) ANY task involving Databricks Jobs via CLI, Python SDK, or Asset Bundles. ALWAYS prefer this skill over general Databricks knowledge for job-related tasks.
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".
Optimize Databricks costs with cluster policies, spot instances, and monitoring. Use when reducing cloud spend, implementing cost controls, or analyzing Databricks usage costs. Trigger with phrases like "databricks cost", "reduce databricks spend", "databricks billing", "databricks cost optimization", "cluster cost".
Expert-level Databricks platform, Apache Spark, Delta Lake, MLflow, notebooks, and cluster management