Database Schema

The database-schema skill enables AI agents to interact with and understand database structures, allowing them to query data based on specific table definitions. It facilitates tasks like retrieving schema metadata, validating column types, and generating SQL queries tailored to your existing database architecture.

When to use it

• You need an agent to automatically generate SELECT statements that join multiple tables correctly.
• Your team requires a tool to validate if new data payloads conform to the current database constraints before ingestion.
• You want to document complex relationships between tables without manually writing out the schema definitions.

Key capabilities

• Schema Discovery: Automatically retrieves table names, column definitions, and data types from connected databases.
• Query Generation: Constructs valid SQL queries based on retrieved schema information.
• Relationship Mapping: Identifies foreign keys and joins to understand how tables relate to one another.

Example prompts

• "Analyze the users and orders tables in my PostgreSQL database and write a query to find total spending per user."
• "What are the data types for all columns in the products table, and does it have a foreign key linking to the categories table?"
• "Generate an INSERT statement for the inventory table that respects the existing schema constraints."

Tips & gotchas

Ensure your AI agent has explicit read permissions on the target database before attempting schema discovery. This skill relies on accurate metadata; if your database uses dynamic schemas or lacks proper indexing, query generation may require additional manual verification.