Agent Eval Coverage Audit

    2

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

    $5

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    Works with the AI Agents you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIMuseMuseDotsDots+21 more

    agent-eval-coverage-audit

    Example session with this skill installed

    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.

    • Read your context and instructions
    • Compiled the agent-eval-coverage-audit

    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.
    3. Update eval-config.json

    Connects securely to your tools. The creator never sees your data.

    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

    • 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 use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

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    Listed7 months ago

    What's inside

    Frequently Asked Questions

    This skill identifies gaps in your evaluation suites, such as missing edge cases, "false green" indicators, and insufficient testing gates, providing a structured remediation plan to prevent production failures.

    The tool is framework-agnostic and supports any agent using JSON-based evaluation configurations, including popular tools like Promptfoo, LangSmith, and custom Python or PowerShell environments.

    Setup is straightforward; once installed, you simply point the skill to your existing evaluation datasets and configuration files to begin the automated analysis.

    You will receive the core audit logic compatible with Python and PowerShell, along with templates for generating executive Markdown reports and machine-readable JSON for your CI/CD pipeline.

    While the skill performs a deep-dive audit of your evaluation surface, it does not store your proprietary traces or data; it process configurations locally to identify architectural and coverage gaps.

    New in Agents & Orchestration