Snowflake - OpenClaw Skill — OpenClaw Tutorials

Install Command

npx clawhub@latest install snowflake

Installation Guide

Check Environment

Make sure Node.js 22+ and OpenClaw are installed. Run openclaw --version in your terminal to verify.

Run Installation

Run the install command above in your terminal. ClawHub will automatically download and install Snowflake to the ~/.openclaw/skills/ directory.

Verify Installation

Run openclaw skills list to check your installed skills and confirm Snowflake appears in the list.

Configure (Optional)

Follow the configuration instructions in the description below to add skill settings to ~/.config/openclaw/openclaw.json5.

Manual Installation: Copy the Skill folder to ~/.openclaw/skills/ or the skills/ directory in your project root. Make sure the folder contains a SKILL.md file.

SQL data warehouse querying Database and schema browsing Cortex AI intelligent analytics

Detailed Description

Snowflake MCP server enables AI to directly connect to Snowflake data warehouses, execute SQL queries and data analysis, and leverage Snowflake Cortex AI features for intelligent data exploration.

Core Features

SQL Query (run_query): Execute SQL queries in Snowflake, supporting complex analytical queries, CTEs, and window functions
Schema Browsing: List available databases, schemas, and tables, view table structures and column definitions
Data Exploration: Get table row counts, sample data, and basic statistics
Cortex Integration: Leverage Snowflake Cortex AI for data analysis, text summarization, and semantic search
Stage Management: Browse and manage files in Snowflake Stages

Configuration

{
  "mcpServers": {
    "snowflake": {
      "command": "npx",
      "args": ["-y", "@snowflake/mcp-server"],
      "env": {
        "SNOWFLAKE_ACCOUNT": "your-account",
        "SNOWFLAKE_USER": "your-username",
        "SNOWFLAKE_PASSWORD": "your-password",
        "SNOWFLAKE_WAREHOUSE": "COMPUTE_WH"
      }
    }
  }
}

Use Cases

Data analysis: Describe analysis needs in natural language, AI generates and executes SQL queries
Report generation: Query data and generate business reports and insights
Data quality checks: Check tables for null values, duplicates, and anomalous data
Data modeling: Explore table relationships and optimize data model designs

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