Agent Eval Coverage Audit
by Roy Yuen
Audit your AI agent's evaluation coverage to identify missing release gates and production risks.
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Works with the AI tools you already use
See it in action
You say
Audit my Support Agent Pilot using .\sample-eval-config.json. The success goal is to resolve issues without escalation. Output the report and JSON to the current directory.
Your agent does
Audit Summary: 65% Coverage. CRITICAL GAP: Missing evaluation for 'Human Escalation' paths. REMEDIATION:
- Add adversarial test cases for prompt injection.
- Implement semantic similarity gates in CI.
- Update eval-config.json to include latency percentiles.
What you get
About this skill
What it does
This skill provides a professional-grade evaluation of your AI agent's testing infrastructure. It inspects evaluation configurations, sample datasets, CI/CD hooks, and policy checks to identify critical gaps in your release gates. It transforms technical debt into a structured remediation plan, ensuring your agent pilots are truly production-ready.
Why use this skill
Manual evaluation of your eval suite is meta-work that often gets skipped. This skill automates the process by analyzing your current test surface against industry best practices. Unlike simple prompts, it cross-references your system's success definitions with existing traces and configs to spot "false greens" and missing edge cases that could lead to production failures.
Supported tools
- Frameworks: Supports any JSON-based eval config (Promptfoo, LangSmith, etc.)
- Environments: PowerShell, Python 3.x
- Outputs: Generates executive-ready Markdown reports and machine-readable JSON for CI/CD integration
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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