Data Type Converter

What it does

This skill enables AI agents to transform data between different formats, ensuring compatibility across various systems and applications. It handles the conversion of raw inputs into structured types required for further processing or storage.

When to use it

• Integrating data from legacy systems that output non-standard formats into modern databases.
• Preparing unstructured text logs into JSON objects for machine learning model ingestion.
• Converting API responses from XML to JSON for frontend application consumption.
• Normalizing date and number formats from user inputs before storing them in a central repository.

Key capabilities

• Converts data between multiple standard types (e.g., string, integer, float, boolean).
• Transforms complex structures like arrays and objects into simpler or different formats.
• Handles edge cases such as null values, empty strings, and type mismatches gracefully.
• Supports batch conversion for large datasets without manual intervention.

Example prompts

• "Convert this list of CSV rows into a JSON array."
• "Transform the following XML response into a Python dictionary."
• "Change these unstructured text notes into structured key-value pairs."

Tips & gotchas

Ensure input data is clean and well-formed to avoid unexpected conversion errors. Test with sample data that includes edge cases like missing values or special characters before deploying at scale.