Agent skills for AI coding assistants

Agent skills are task-specific instruction files that AI coding assistants like Claude and GitHub Copilot can load to perform Azure Databricks development tasks. Skills package domain-specific knowledge, best practices, and workflows into a format optimized for AI consumption. To learn how to extend Genie Code in the Azure Databricks workspace, see Extend Genie Code with agent skills.

Skills follow the open Agent Skills standard. Each skill is a Markdown file with front-matter metadata that describes when and how the skill should be used. AI coding assistants automatically discover and load relevant skills based on the task at hand.

Install AI tools

Install Azure Databricks AI tools using the Databricks CLI aitools command group. The CLI detects supported coding agents and installs Azure Databricks skills and plugins so your agent can work effectively with Azure Databricks resources such as bundles, jobs, and SQL.

# Install skills for all detected agents
databricks aitools install

# Install skills for a specific agent
databricks aitools install --agents claude-code

# Install skills into the current project instead of globally
databricks aitools install --scope project

# Install specific skills
databricks aitools install --skills bundles,sql

Use databricks aitools list, databricks aitools update, and databricks aitools uninstall to manage installed AI tools. For all options, see aitools command group.

To install skills from arbitrary GitHub repositories that are not distributed through the Databricks CLI, use the Skills CLI, an open-source package manager for agent skills. The Skills CLI scans a GitHub repository for skill files and installs them into your project so that your AI coding assistant can discover and use them automatically.

AI tools and managed MCP servers

AI tools and managed MCP servers solve different halves of the same problem, and most setups use both:

  AI tools Managed MCP servers
What it provides Knowledge: Azure Databricks patterns, APIs, and workflows the agent applies as it writes code. Tools: callable endpoints an agent uses to query Unity Catalog data, run SQL, search indexes, and call Genie Agents.
Where it runs Locally, in your coding agent's skills or plugin directory. Hosted by Azure Databricks, governed by Unity Catalog.
How to set it up databricks aitools install Add the server URL to your client's MCP configuration and authenticate. See Connect MCPs to AI assistants and coding agents.

Skills teach the agent how to work; MCP servers let it do the work. A skill can direct an agent to a managed MCP server for a task, but the skill is not itself an MCP server, and installing AI tools does not configure MCP servers for you. If you want your agent to both write correct Azure Databricks code and read live workspace data, install AI tools and connect the managed MCP servers you need.

Skill repositories

GitHub repository Description Skills
Azure Databricks agent skills Officially maintained core skills and plugins for Azure Databricks development across compute, orchestration, storage, and apps. These skills are installed using databricks aitools install. Among others, includes skills for Agent Bricks, AI Functions, AI/BI Dashboards, Databricks Apps, Bundles, Databricks CLI, Databricks Lakehouse, Genie, Iceberg, Lakebase, Lakeflow Jobs, metric views, MLflow evaluation, Model Serving, Python SDK, Lakeflow pipelines, serverless migration, Structured Streaming, synthetic data, Unity Catalog, AI Search, and Zerobus ingest
AI Dev Kit skills Curated community skills covering Azure Databricks development patterns. Deprecated. These skills are available with Azure Databricks agent skills. To avoid conflicts, uninstall AI Dev Kit skills before installing Databricks agent skills. See Uninstall AI Dev Kit.
Databricks app template skills Task-specific skills embedded inside Databricks app templates for agents (LangGraph, LangChain, OpenAI Agents SDK), App Kits (Lakebase, Genie, Analytics), and chatbot/data app frameworks (Streamlit, Dash, Gradio, Shiny, Flask, Node.js). Quickstart, deploy, modify-agent, add-tools, create-tools, discover-tools, migrate-from-model-serving, run-locally, load-testing, supervisor APIs
MLflow skills Skills for instrumenting, debugging, and evaluating LLM agents with MLflow. MLflow onboarding, MLflow agent, instrumenting tracing, retrieving and analyzing traces, querying metrics, agent evaluation, chat session analysis, MLflow doc search

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