Browse published AI skills maintained by databricks-solutions. Compare skill descriptions and follow each source repository for setup instructions.
Indexed skills
16Repository stars across indexed skills
2,123Repository forks
492Average repository stars per indexed skill
133Repository stars may repeat across skills from the same repository.
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'...
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, job...
Python development guidance with code quality standards, error handling, testing practices, and environment management. Use when writing, reviewing, or modifying Python code (.py files) or Jupyter notebooks (.i...
Create and manage Databricks Agent Bricks: Knowledge Assistants (KA) for document Q&A, Genie Spaces for SQL exploration, and Multi-Agent Supervisors (MAS) for multi-agent orchestration. Use when building conver...
MLflow 3 GenAI evaluation for agent development. Use when (1) writing mlflow.genai.evaluate() code, (2) creating @scorer functions, (3) building evaluation datasets from traces, (4) using built-in scorers (Guid...
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 patt...
Generate realistic synthetic data using Faker and Spark, with non-linear distributions, integrity constraints, and save to Databricks. Use when creating test data, demo datasets, or synthetic tables.
A brief one-sentence description of what this skill helps with.
Generate synthetic PDF documents for RAG and unstructured data use cases. Use when creating test PDFs, demo documents, or evaluation datasets for retrieval systems.
Databricks development guidance including Python SDK, Databricks Connect, CLI, and REST API. Use when working with databricks-sdk, databricks-connect, or Databricks APIs.
Manage Unity Catalog resources including catalogs, schemas, and tables. Handles discovery, creation, updates, and deletions with proper naming conventions and governance. Use when exploring catalogs, creating s...
Execute code on Databricks clusters using MCP Command Execution API. Supports stateless quick validation and stateful iterative development. Use when testing Python/SQL code on clusters, debugging pipelines, or...
Package and deploy Databricks Asset Bundles with proper parameterization, multi-environment support, and serverless compute. Handles project structure, databricks.yml generation, validation, and deployment. Use...
End-to-end machine learning pipelines on Databricks including data exploration, feature engineering, model training with hyperparameter optimization, MLflow experiment tracking, model registration to Unity Cata...
Production data engineering pipelines following medallion architecture (Bronze/Silver/Gold layers) with data ingestion, transformation, quality checks, Delta Lake optimization, and orchestration. Use when build...
Code verification and testing for the Human Evaluation Workshop. Use when (1) running tests after code changes, (2) writing new unit tests (pytest/vitest), (3) writing E2E tests with Playwright/TestScenario, (4...
The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.