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:

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/).

Options for databricks aitools install:

Option	Description
`--agents`	Agents to install for (comma-separated, e.g. claude-code,cursor)
`--experimental`	Include experimental skills
`--path`	Write resolved skill files to this directory (no agents, no state)
`--scope`	Install scope: project or global (default: global, or prompt when interactive)
`--skills`	Specific skills to install (comma-separated)
`--skills-only`	Force raw skill files for every agent instead of the plugin
`--debug`	enable debug logging
`--output`, `-o`	output type: text or json (default text)
`--profile`, `-p`	~/.databrickscfg profile
`--target`, `-t`	bundle target to use (if applicable)

Note that --skills-only and --path cannot be combined.

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 (KA) for document Q&A and Supervisor Agents for multi-agent orchestration (MAS).
`databricks-ai-functions`	Use Databricks built-in AI Functions (ai_classify, ai_extract, ai_summarize, ai_mask, ai_translate, ai_fix_grammar, ai_gen, ai_analyze_sentiment, ai_similarity, ai_parse_document, ai_prep_search, ai_query, ai_forecast) to add AI capabilities directly to SQL and PySpark pipelines without managing model endpoints.
`databricks-aibi-dashboards`	Create Databricks AI/BI dashboards.
`databricks-app-design`	Design the UX of custom-code Databricks Apps (AppKit/React) data screens — KPI/overview pages, reports, charts, tables, and Genie/chat data assistants — mapped to concrete AppKit components.
`databricks-apps`	Build apps on Databricks Apps platform.
`databricks-apps-python`	Python backend for Databricks Apps — FastAPI (default), Flask, Dash, Streamlit, Gradio, Reflex. Default for a new Databricks App is `databricks-apps` (AppKit — Node/TypeScript/React) — reach for it first. Use this skill only when the user asks for a Python backend, extends an existing Python app, or the team is Python-only.
`databricks-core`	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`	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 on Databricks: run Python/Scala/SQL/R via serverless, classic, or interactive clusters, and create/resize/delete clusters and SQL warehouses.
`databricks-iceberg`	Apache Iceberg tables on Databricks — Managed Iceberg tables, External Iceberg Reads (fka Uniform), Compatibility Mode, Iceberg REST Catalog (IRC), Iceberg v3, Snowflake interop, PyIceberg, OSS Spark, external engine access and credential vending.
`databricks-jobs`	Develop and deploy Lakeflow Jobs on Databricks via DABs, Python SDK, or the CLI.
`databricks-lakebase`	Databricks Lakebase Postgres: projects, scaling, connectivity, Lakebase 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 models on Databricks.
`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) on Databricks.
`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 (strongly recommended).
`databricks-unity-catalog`	Unity Catalog governance, access control, and observability.
`databricks-unstructured-pdf-generation`	Build RAG / unstructured-document evaluation datasets and demo documents (e.g. for Knowledge Assistant) on Databricks: generate synthetic PDFs locally, upload to Unity Catalog volumes, and pair each document with test questions for retrieval evaluation.
`databricks-vector-search`	Databricks Vector Search endpoints and indexes for RAG and semantic search; covers index types, search modes, end-to-end RAG patterns
`databricks-zerobus-ingest`	Build Zerobus Ingest clients for near real-time data ingestion into Databricks Delta tables via gRPC.

The following skills are experimental. Install them by adding --experimental to databricks aitools install:

Skill	Description
`databricks-ai-runtime`	Databricks AI Runtime (`air`) CLI — the command-line tool for submitting and managing GPU training workloads on Databricks serverless compute.
`databricks-genie`	Create and query Databricks Genie Spaces for natural language SQL exploration.
`spark-python-data-source`	Build custom Python data sources for Apache Spark using the PySpark DataSource API — batch and streaming readers/writers for external systems.

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.