Agent skills

Agent skills are instruction files that AI coding assistants load to perform Databricks development tasks. Databricks publishes its skills in the databricks/databricks-agent-skills repository and follows the open agent skills standard.

Skills tell your coding agent how Databricks works, including CLI conventions, authentication patterns, and resource names, so it generates correct code instead of guessing.

Install

Install the official Databricks agent skills with the following command:

Common

databricks aitools install

note

This requires the Databricks CLI to be installed. See Databricks CLI for installation instructions.

The CLI detects which coding agents you have installed. For agents with plugin support (Claude Code, Codex CLI, GitHub Copilot), it installs the databricks plugin through the agent's own CLI. Agents without a headless plugin install (Cursor, OpenCode, Antigravity) get raw skill files linked from a shared location (~/.databricks/aitools/skills/).

The following flags are available for the databricks aitools install command:

Option	Description
`--scope`	Install scope: `project` or `global` (default: `global`, or prompt when interactive)
`--agents`	Target specific agents (comma-separated, e.g. `--agents cursor,claude-code`)
`--skills`	Install specific skills only (comma-separated)
`--skills-only`	Force raw skill files for every agent instead of the plugin
`--path`	Write resolved skill files to a directory (no agents, no state)
`--experimental`	Include experimental skills

Run databricks aitools install --help for the full list of options.

Manage

List, update, or remove skills

databricks aitools list
databricks aitools update
databricks aitools uninstall

update fetches the latest release and auto-installs new skills. Pass --check to preview without downloading, --no-new to skip auto-installing new skills, --no-prune to keep skills that were removed from the manifest, or --force to re-download even if versions match.

uninstall removes the plugin or skill files. Pass --keep-marketplace to keep the marketplace registration when removing a plugin.

All commands accept --scope to control scope: install and uninstall take project or global; update and list also accept both (list defaults to both).

Alternative install methods

You can also install Databricks skills with the Skills CLI (e.g. npx skills add databricks/databricks-agent-skills) or directly from Cursor chat with /add-plugin databricks. That said, databricks aitools install is the recommended method — it's maintained by Databricks and always installs the latest stable versions.

Available skills

Run databricks aitools list to see available skills and their install status.

Skill	Description
`databricks-agent-bricks`	Create Agent Bricks: Knowledge Assistants for document Q&A and Supervisor Agents for multi-agent orchestration.
`databricks-ai-functions`	Use Databricks built-in AI Functions (`ai_classify`, `ai_extract`, `ai_summarize`, `ai_query`, and more).
`databricks-aibi-dashboards`	Create Databricks AI/BI dashboards. Use when creating, updating, or deploying Lakeview dashboards.
`databricks-app-design`	Design the UX of custom-code Databricks Apps (AppKit/React) data screens mapped to concrete AppKit components.
`databricks-apps`	Build apps on the Databricks Apps platform.
`databricks-apps-python`	Python backend for Databricks Apps — FastAPI (default), Flask, Dash, Streamlit, Gradio, Reflex.
`databricks-core`	CLI operations and the parent/entry-point skill: authentication, profile selection, and bundles.
`databricks-dabs`	Create, configure, validate, deploy, run, and manage Declarative Automation Bundles (DABs).
`databricks-data-discovery`	Discover, explore, and query Databricks data via Genie — the CLI equivalent of the Genie One MCP.
`databricks-dbsql`	Databricks SQL (DBSQL) advanced features and SQL warehouse capabilities.
`databricks-docs`	Databricks documentation reference via `llms.txt` index.
`databricks-execution-compute`	Execute code and manage compute: run Python/Scala/SQL/R via serverless, classic, or interactive clusters.
`databricks-genie-agents`	Create, manage, and query Databricks Genie Agents (formerly Genie Spaces) for natural-language data exploration.
`databricks-iceberg`	Apache Iceberg tables on Databricks — Managed Iceberg, External Iceberg Reads, IRC, Iceberg v3, and more.
`databricks-jobs`	Develop and deploy Lakeflow Jobs via DABs, Python SDK, or the CLI.
`databricks-lakebase`	Databricks Lakebase Postgres: projects, scaling, connectivity, synced tables, and Data API.
`databricks-lakeflow-connect`	Build managed ingestion pipelines into Databricks using Lakeflow Connect.
`databricks-metric-views`	Unity Catalog metric views: define, create, query, and manage governed business metrics in YAML.
`databricks-ml-training`	Train ML or custom-agent models with MLflow tracking and Unity Catalog registration.
`databricks-mlflow-evaluation`	MLflow 3 GenAI agent evaluation.
`databricks-model-serving`	Databricks Model Serving endpoint lifecycle and ops.
`databricks-pipelines`	Develop Lakeflow Spark Declarative Pipelines (formerly Delta Live Tables).
`databricks-python-sdk`	Databricks development guidance including Python SDK, Databricks Connect, CLI, and REST API.
`databricks-serverless-migration`	Migrate Databricks workloads from classic compute to serverless compute.
`databricks-spark-structured-streaming`	Comprehensive guide to Spark Structured Streaming for production workloads.
`databricks-synthetic-data-gen`	Generate realistic synthetic data using Spark + Faker, with serverless execution and multiple output formats.
`databricks-unity-catalog`	Unity Catalog governance, access control, and observability — grants, privilege model, RLS, and column masks.
`databricks-unstructured-pdf-generation`	Build RAG / unstructured-document evaluation datasets and demo documents on Databricks.
`databricks-vector-search`	Databricks Vector Search endpoints and indexes for RAG and semantic search.
`databricks-zerobus-ingest`	Build Zerobus Ingest clients for near real-time data ingestion into Databricks Delta tables via gRPC.

The following skills are experimental. They install only when you pass --experimental, and databricks aitools list shows them under "Experimental skills":

Skill	Description
`databricks-ai-runtime`	Databricks AI Runtime (`air`) CLI for submitting and managing GPU training workloads on serverless compute.
`spark-python-data-source`	Build custom Python data sources for Apache Spark using the PySpark DataSource API.

Where to next

With Databricks agent skills installed, your coding agent has the context it needs to build and deploy.

To give your agent further context, install the Docs MCP Server.
Ready to start building? Read about how templates can help you quickly scaffold your project.