Data Validation

What it does

This skill enables AI agents to rigorously check input data against predefined schemas, formats, and business rules before processing. It ensures that information like emails, phone numbers, or structured JSON objects meets specific quality standards, preventing errors downstream in the workflow.

When to use it

• Pre-processing user inputs: Validate form submissions or chat messages containing critical details (e.g., dates, IDs) before passing them to a database or API.
• Ensuring data consistency: Standardize varied input formats across different sources so downstream tools receive uniform data structures.
• Security and integrity checks: Detect malformed requests or suspicious patterns that violate expected protocols before execution.
• Automated error reporting: Provide immediate, clear feedback to users when their input fails validation criteria rather than causing silent failures.

Key capabilities

• Schema enforcement against JSON, XML, or custom structures.
• Format verification for common data types (email, phone, URL, dates).
• Custom rule application for business logic constraints.
• Structured error reporting detailing exactly why validation failed.

Example prompts

• "Validate this list of customer emails against a standard RFC 5322 format and return only the valid ones."
• "Check if the following JSON payload matches our order schema and highlight any missing required fields."
• "Run a data validation check on these phone numbers to ensure they follow the E.164 international standard."

Tips & gotchas

Ensure your validation rules are specific enough to catch errors but flexible enough to handle legitimate variations in user input. Overly strict constraints may lead to high rejection rates for valid data, so test edge cases before deploying in production environments.