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    🤖 AI Agent Auditor

    🤖 AI Agent Auditor

    by Martin Gunderman

    Analyzes AI agents for performance, reliability, security, and optimization opportunities.

    Updated Jun 2026
    Security scanned
    Claude Code

    $7

    · or 35 credits

    30-day refund guarantee

    Secure checkout via Stripe

    Included in download

    • Evaluate hallucination rates and factual consistency before go-live.
    • Identify unused or redundant tools in your agentic workflows.
    • terminal automation included
    • Ready for Claude Code
    • Instant install

    Sample input

    Run an audit on our customer support agent directory. Include the cost logs and system prompts. We've been seeing high latency and some wrong pricing info in the responses.

    Sample output

    Audit Report: Support Agent v2.1

    Score: 64/100 (Needs Improvement)

    High Severity Issues:

    • AGENT-HALL-001: Pricing Hallucinations. RAG context is using outdated cache.
    • AGENT-COST-002: GPT-4o used for basic greetings. Action: Implement Semantic Caching and route simple queries to GPT-4o-mini.

    About This Skill

    Automated AI Agent Quality Assurance

    The AI Agent Auditor is a specialized diagnostic skill designed for developers, CTOs, and AI agencies who need to validate the production-readiness of their AI agents. While basic logging tells you when an agent fails, this skill investigates why by dissecting the underlying architecture, tool definitions, and prompt sequences.

    What it does

    • Architecture Analysis: Scans configurations for tool usage efficiency, dead-tool identification, and multi-agent topology.
    • Prompt Chain Auditing: Evaluates instruction coherence, redundancy, and context window optimization.
    • Hallucination Assessment: Maps factual error rates and identifying high-risk contexts for model fabrication.
    • Cost & Reliability Scoring: Analyzes token consumption and latency metrics to identify cost-saving opportunities through caching or model routing.

    Why use this skill?

    Prompting an AI is not enough for complex system audits. This skill provides a systematic framework—complete with specialized scoring algorithms and severity levels—to transform raw logs and configs into a professional audit report. It supports major frameworks like LangChain, AutoGen, and CrewAI, giving you a structured roadmap to move from 70% reliability to 99% production-grade stability.

    The Output

    You receive a comprehensive Audit Report featuring a performance score (0-100), categorized issue logs (Critical to Info), and a concrete remediation roadmap with estimated effort and potential ROI in cost savings.

    Use Cases

    • Evaluate hallucination rates and factual consistency before go-live.
    • Identify unused or redundant tools in your agentic workflows.
    • Reduce API costs by up to 50% via model routing and caching audits.
    • Generate executive-ready reports for client delivery milestones.
    • Audit prompt chains for token efficiency and instruction leakage.

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    Security Scanned

    Passed automated security review

    Permissions

    Terminal / Shell

    Tags

    ai-agents
    audit
    reliability
    cost-optimization
    llmops
    quality-assurance

    Claude Code,Hermes, Openclaw

    Creator

    Martin Gunderman

    Frequently Asked Questions

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