quality-checks

What it does

This skill enables AI agents to perform automated validation and verification tasks on generated outputs, data entries, or code snippets. It ensures consistency and accuracy by comparing results against predefined rules, schemas, or reference datasets before final submission.

When to use it

• Automated testing: Run regression tests on software builds or API responses immediately after generation.
• Data integrity: Validate user-submitted forms or imported CSV files against strict business logic constraints.
• Content moderation: Check generated articles or social media posts for policy violations, hallucinations, or formatting errors.
• Code review: Automatically lint and test code snippets before merging them into a repository.

Key capabilities

• Executes predefined validation rules and schemas on arbitrary inputs.
• Compares output against golden datasets to detect drift or anomalies.
• Generates detailed error reports highlighting specific failures in the checked content.
• Supports both synchronous checks (blocking) and asynchronous batch processing.

Example prompts

• "Run a quality check on this JSON response to ensure all required fields match our schema."
• "Validate the generated SQL query against our security rules before execution."
• "Check this article for factual consistency with the provided source documents."

Tips & gotchas

Ensure your validation rules are up-to-date, as outdated schemas may cause false positives on legitimate new data. For large-scale operations, configure asynchronous processing to avoid latency bottlenecks during peak usage.