Custom Format Validators

What it does

This skill enables AI agents to validate data against user-defined schemas using the z-schema library. It ensures input conforms to specific structural rules and formats before processing, preventing errors from malformed data.

When to use it

• Validating complex JSON payloads that exceed standard built-in format checks.
• Enforcing strict business logic constraints on incoming API requests.
• Ensuring data integrity for downstream systems that require precise schema adherence.
• Cleaning and sanitizing unstructured user inputs before storage or analysis.

Key capabilities

• Integration with the z-schema library for robust validation.
• Support for custom format definitions beyond standard types.
• Ability to define complex object structures and nested rules.
• Real-time error reporting for non-compliant data entries.

Example prompts

• "Validate this JSON response against a schema requiring an 'email' field in RFC 5322 format."
• "Check if the incoming user profile data matches our custom organization hierarchy schema."
• "Run validation on this dataset to ensure all dates are ISO 8601 compliant and numeric fields are positive integers."

Tips & gotchas

Ensure your custom schema definitions are syntactically correct before attempting validation, as malformed schemas will cause the agent to fail immediately. This skill is best suited for structured data; unstructured text may require preprocessing before validation can occur effectively.