# Databricks agent skills

Every skill in <https://github.com/databricks/databricks-agent-skills> release **v0.2.11**, published 2026-08-17 and checked 2026-08-18: 30 skills.

This public catalog describes the skills that Databricks publishes.

| Skill | Version | What it covers |
|---|---|---|
| `databricks-agent-bricks` | 0.1.0 | Create Agent Bricks: Knowledge Assistants (KA) for document Q&A and Supervisor Agents for multi-agent orchestration (MAS). |
| `databricks-ai-functions` | 0.2.0 | Use Databricks built-in AI Functions (ai_classify, ai_extract, ai_summarize, ai_mask, ai_translate, ai_fix_grammar, ai_gen, ai_analyze_sentiment… |
| `databricks-aibi-dashboards` | 0.2.1 | Create Databricks AI/BI dashboards. |
| `databricks-app-design` | 0.1.0 | Design the UX of custom-code Databricks Apps (AppKit/React) data screens — KPI/overview pages, reports, charts, tables, and Genie/chat data… |
| `databricks-apps` | 0.1.2 | Build apps on Databricks Apps platform. |
| `databricks-apps-python` | 0.1.0 | Python backend for Databricks Apps — FastAPI (default), Flask, Dash, Streamlit, Gradio, Reflex. |
| `databricks-core` | 0.1.0 | Databricks CLI operations and the parent/entry-point skill for Databricks CLI use: authentication, profile selection, and bundles. |
| `databricks-dabs` | — | Create, configure, validate, deploy, run, and manage Declarative Automation Bundles (DABs, formerly Databricks Asset Bundles). |
| `databricks-data-discovery` | 0.1.0 | Discover, explore, and query Databricks data via Genie — the CLI equivalent of the Genie One MCP. |
| `databricks-dbsql` | 0.1.0 | Databricks SQL (DBSQL) advanced features and SQL warehouse capabilities. |
| `databricks-docs` | 0.1.0 | Databricks documentation reference via llms.txt index. |
| `databricks-execution-compute` | 0.1.0 | Execute code and manage compute on Databricks: run Python/Scala/SQL/R via serverless, classic, or interactive clusters, and create/resize/delete… |
| `databricks-genie-agents` | 0.1.0 | Create, manage, and query Databricks Genie Agents — curated, per-data natural-language agents (formerly Genie Spaces): build, export/import, migrate… |
| `databricks-iceberg` | 0.1.0 | Apache Iceberg tables on Databricks — Managed Iceberg tables, External Iceberg Reads (fka Uniform), Compatibility Mode, Iceberg REST Catalog (IRC)… |
| `databricks-jobs` | 0.2.0 | Develop and deploy Lakeflow Jobs on Databricks via DABs, Python SDK, or the CLI. |
| `databricks-lakebase` | 0.1.0 | Databricks Lakebase Postgres: projects, scaling, connectivity, Lakebase synced tables, and Data API. |
| `databricks-lakeflow-connect` | 0.1.0 | Build managed ingestion pipelines into Databricks using Lakeflow Connect. |
| `databricks-metric-views` | 0.1.0 | Unity Catalog metric views: define, create, query, and manage governed business metrics in YAML. |
| `databricks-ml-training` | 0.1.0 | Train ML models on Databricks. |
| `databricks-mlflow-evaluation` | 0.1.0 | MLflow 3 GenAI agent evaluation. |
| `databricks-model-serving` | 0.4.0 | Databricks Model Serving endpoint lifecycle and ops. |
| `databricks-pipelines` | 0.3.0 | Develop Lakeflow Spark Declarative Pipelines (formerly Delta Live Tables) on Databricks. |
| `databricks-python-sdk` | 0.1.0 | Databricks development guidance including Python SDK, Databricks Connect, CLI, and REST API. |
| `databricks-serverless-migration` | 0.1.0 | Migrate Databricks workloads from classic compute to serverless compute. |
| `databricks-spark-structured-streaming` | 0.1.0 | Comprehensive guide to Spark Structured Streaming for production workloads. |
| `databricks-synthetic-data-gen` | 0.1.0 | Generate realistic synthetic data using Spark + Faker (strongly recommended). |
| `databricks-unity-catalog` | 0.3.0 | Unity Catalog governance, access control, and observability. |
| `databricks-unstructured-pdf-generation` | 0.1.0 | Build RAG / unstructured-document evaluation datasets and demo documents (e.g. |
| `databricks-vector-search` | 0.1.0 | Databricks Vector Search endpoints and indexes for RAG and semantic search; covers index types, search modes, end-to-end RAG patterns |
| `databricks-zerobus-ingest` | 0.1.0 | Build Zerobus Ingest clients for near real-time data ingestion into Databricks Delta tables via gRPC. |