Eval Audit

What it does

The eval-audit skill enables AI agents to systematically review and assess evaluation datasets or criteria. It helps ensure that testing frameworks are robust, unbiased, and aligned with specific performance goals before deployment.

When to use it

• Validating the quality and diversity of a dataset used for training or testing an AI model.
• Checking whether evaluation metrics accurately reflect real-world success criteria.
• Identifying potential biases or edge cases within existing test suites.
• Auditing compliance with internal standards or external regulatory requirements for model assessment.

Key capabilities

• Systematic review of evaluation datasets and criteria.
• Assessment of metric alignment with performance goals.
• Detection of bias or gaps in testing frameworks.
• Structured reporting on audit findings.

Example prompts

• "Run an audit on our current LLM benchmark dataset to check for representation bias."
• "Evaluate whether our success metrics for the customer support bot align with user satisfaction surveys."
• "Audit this set of unit tests for a code generation model to identify missing edge cases."

Tips & gotchas

Ensure you have access to the full evaluation data and clear definitions of success criteria before running an audit. The skill relies on structured input; unorganized or ambiguous datasets may yield incomplete results.