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databricks-genie

Create Databricks Genie Spaces for natural language SQL queries on Unity Catalog data.

38|12|Updated Apr 24, 2026
Search Tags:#data exploration#databricks#unity catalog#genie spaces#natural language sql#conversational querying#sql automation
One-click install
npx skills add https://github.com/databrickslabs/coding-agents-databricks-apps --skill databricks-genie-databrickslabs
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: databricks-genie
Source: https://github.com/databrickslabs/coding-agents-databricks-apps/tree/main/.claude/skills/databricks-genie
Command: npx skills add https://github.com/databrickslabs/coding-agents-databricks-apps --skill databricks-genie-databrickslabs

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill eliminates the need for end users to write complex SQL to explore Unity Catalog data, by enabling the creation of natural language conversational interfaces for SQL-based data exploration.

Core Features & Use Cases

  • Create and manage Databricks Genie Spaces connected to Unity Catalog tables for natural language querying.
  • Use the Genie Conversation API to ask natural language questions and receive SQL-generated results with context retention for follow-up queries.
  • Curate Genie Spaces with sample questions and custom instructions to improve query accuracy for business-specific metrics and terminology. A common use case is a data team building a Genie Space for sales data, adding sample questions like "What were total sales last month?", and letting business users explore data without SQL expertise.

Quick Start

Use the databricks-genie skill to create a Genie Space connected to your Unity Catalog sales tables and test it with a sample question about monthly total revenue.

Frequently Asked Questions about databricks-genie

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I query Unity Catalog data using natural language instead of raw SQL?▼

You can query Unity Catalog data using natural language by creating Databricks Genie Spaces, which translate conversational questions into SQL queries automatically. This eliminates the need for end users to write raw SQL for data exploration.

How do I build a conversational SQL explorer for business users in Databricks?▼

Build a conversational SQL explorer by creating a Genie Space connected to your Unity Catalog tables, adding custom instructions and sample questions to improve query accuracy for business-specific metrics. Business users can then explore data without SQL expertise.

Does querying Genie Spaces via the Conversation API retain context for follow-up questions?▼

Yes, querying Genie Spaces via the Conversation API retains conversational context for follow-up queries. This allows users to ask ad-hoc natural language questions and receive SQL-generated results with context retention for multi-turn data analysis.

Do I need to manually configure a SQL warehouse to run natural language queries in Genie Spaces?▼

No, you do not need to manually configure a SQL warehouse for natural language queries. Genie Spaces auto-detect available SQL warehouses in your environment for query execution against Unity Catalog data.

What is the best way to improve natural language SQL accuracy for business-specific metrics in Databricks?▼

The best way to improve natural language SQL accuracy is to curate your Genie Space with custom instructions and sample questions tailored to your business-specific metrics and terminology. This guides the conversational querying engine to generate more precise SQL.

Can I programmatically query a Databricks Genie Space for ad-hoc data analysis?▼

Yes, you can programmatically query a Databricks Genie Space for ad-hoc data analysis using the Conversation API. This enables automated natural language SQL exploration of Unity Catalog data without requiring end users to write raw SQL.

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