agent-eval-coverage-audit
Audit your AI agent's evaluation coverage to identify missing release gates and production risks.
- 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.
$5
· or 25 creditsSecure checkout via Stripe
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
Sample input
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.
Sample output
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.
agent-eval-coverage-audit
Audit your AI agent's evaluation coverage to identify missing release gates and production risks.
$5
· or 25 creditsSecure checkout via Stripe
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
Sample input
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.
Sample output
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
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.
Known Limitations
- Cannot evaluate dynamic runtime performance without trace files. - Static analysis is limited by the depth of provided success definitions. - Does not fix code; identifies gaps only.
How to Install
mkdir -p ~/.claude/skills && curl -sL https://www.agensi.io/api/install/agent-eval-coverage-audit -o /tmp/agent-eval-coverage-audit.zip && unzip -o /tmp/agent-eval-coverage-audit.zip -d ~/.claude/skills && rm /tmp/agent-eval-coverage-audit.zipFree skills install directly. Paid skills require purchase - use the download button above after buying.
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Compatible with SKILL.md-compatible agents