Scalable Data Schema

What it does

This skill allows AI agents to define and manage data schemas that can evolve over time. It supports adding new fields, changing data types, and handling backward compatibility when updating a schema. The agent can then use these schemas for structured data storage, retrieval, and validation.

When to use it

• Building a knowledge base: Define a schema for storing facts about entities (e.g., people, places, events) that needs to be updated as new information becomes available.
• Creating a product catalog: Manage the structure of product data, easily adding attributes like color or size without breaking existing records.
• Processing user input: Define schemas for structured forms or questionnaires and validate user responses against them.
• Managing logs: Create flexible schemas to capture different types of log events with varying fields.

Key capabilities

• Schema definition (specifying field names, data types)
• Schema evolution (adding/removing fields)
• Backward compatibility handling
• Data validation based on schema rules

Example prompts

• "Define a schema for 'customer' with fields: name (string), email (string), and order_total (number)."
• "Add a new field 'address' to the customer schema, making it an object containing street, city, and zip code."
• “Update the ‘order_total’ field in the customer schema from number to decimal.”

Tips & gotchas

This skill requires some understanding of data structures and schema design principles. Be mindful of potential breaking changes when evolving schemas; careful planning is needed for backward compatibility.