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    agent-eval-coverage-audit

    by Roy Yuen

    Audit your AI agent's evaluation coverage to identify missing release gates and production risks.

    Updated May 2026
    70 views
    Security scanned

    $5

    One-time purchase

    ⚡ Also available via Agensi MCP — your AI agent can load this skill on demand via MCP. Learn more →

    Included in download

    • Identify blind spots in agent evaluation suites before production release.
    • Generate client-ready audit reports in Markdown and JSON formats.
    • Includes example output and usage patterns
    • Instant install
    • One-time purchase

    See it in action

    Audit Summary: 65% Coverage.
CRITICAL GAP: Missing evaluation for 'Human Escalation' paths.
REMEDIATION:
1. Add adversarial test cases for prompt injection.
2. Implement semantic similarity gates in CI.
3. Update eval-config.json to include latency percentiles.

    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

    📖 Learn more: Best Testing & QA Skills for Claude Code →

    Use Cases

    • Identify blind spots in agent evaluation suites before production release.
    • Generate client-ready audit reports in Markdown and JSON formats.
    • Verify if CI/CD hooks adequately enforce safety and quality policies.
    • Analyze execution traces to improve success definitions and test datasets.

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    Security Scanned

    Passed automated security review

    Permissions

    No special permissions declared or detected

    Tags

    ai-testing
    llmops
    quality-assurance
    compliance
    audit

    Creator

    Roy Yuen

    Frequently Asked Questions

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