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databricks-genie-agents

Skill

Create, manage, and query Databricks Genie Agents — curated, per-data natural-language agents (formerly Genie Spaces): build, export/import, migrate across workspaces, and ask questions of a *specific* Agent via the Conversation API. For general data questions or finding data across your workspace,

0Productivity

Install

git clone https://github.com/databricks/databricks-agent-skills.git ~/.claude/skills/databricks-genie-agents

What is databricks-genie-agents?

Create, manage, and query Databricks Genie Agents — curated, per-data natural-language agents (formerly Genie Spaces): build, export/import, migrate across workspaces, and ask questions of a *specific* Agent via the Conversation API. For general data questions or finding data across your workspace, use databricks-data-discovery (Genie One) instead.

What this can do

Capabilities declared in this component's own frontmatter — not inferred.

Inherit all session tools

Declares no tool restrictions — inherits every session tool

~88 tokens of context used while enabled, before you invoke anything

Documentation

README · ~8 min read

Databricks Genie Agents

Create, manage, and query Genie Agents (formerly Genie Spaces) - natural language interfaces for SQL-based data exploration.

Overview

Genie Agents allow users to ask natural language questions about structured data in Unity Catalog. The system translates questions into SQL queries, executes them on a SQL warehouse, and presents results conversationally.

A Genie Agent is a curated agent scoped to specific data — its tables, sample questions, and instructions are authored for a particular business area. This is distinct from Genie One / the general "ask Genie" data-discovery path (see the databricks-data-discovery skill), which answers questions across your data without a curated, per-scope agent.

Creating a Genie Agent

Step 1: Understand the Data

Before creating a Genie Agent, explore the available tables to:

  • Select relevant tables — typically gold layer (aggregated KPIs) and sometimes silver layer (cleaned facts) or metric views
  • Understand the story — what business questions can this data answer? What insights can users discover?

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