Query Lakehouse with AI agents
You can use the atlan-lakehouse agent skill to query your Lakehouse metadata using natural language in AI coding agents like Claude Code. The skill detects your platform, Snowflake via Cortex Code, Databricks via Genie Code, or Python via PyIceberg, and generates the appropriate SQL automatically.
This guide shows you how to install the skill and start querying Lakehouse with natural language.
Prerequisites
Before you begin, make sure you have:
- Enabled Lakehouse for your tenant. See Enable Lakehouse.
- Installed Claude Code on your machine.
- Access to clone the Lakehouse Solutions repository from GitHub.
Install agent skill
-
Clone the Lakehouse Solutions repository to your machine:
git clone https://github.com/atlanhq/lakehouse-solutions.git -
Create the Claude Code skills directory if it doesn't already exist:
mkdir -p ~/.claude/skills -
Symlink the skill into your Claude Code skills directory:
ln -s /path/to/lakehouse-solutions/skills/atlan-lakehouse ~/.claude/skills/atlan-lakehouseReplace
/path/to/lakehouse-solutionswith the actual path where you cloned the repository.
Query Lakehouse using natural language
Once the skill is installed, Claude Code activates it automatically when you ask Lakehouse-related questions.
-
Open Claude Code in your terminal:
claude -
Ask a question in natural language. For example:
- "What percentage of my Snowflake tables have descriptions?"
- "Which assets are missing owners?"
- "Show me lineage gaps in the sales domain."
The skill identifies your platform and runs the appropriate SQL query in your Lakehouse.
See also
- Lakehouse use cases: Explore what you can analyze with Lakehouse data
- Gold layer reference: Learn about the curated views the skill queries
- Lakehouse Solutions repository: Browse all available skills and solutions