Works with the AI Agents you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIMuseMuseDotsDots+21 more

    🤖 AI Agent Auditor

    2

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

    $7

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    🤖 AI Agent Auditor

    🤖 AI Agent Auditor

    Example session with this skill installed

    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.

    • Read your context and instructions
    • Compiled the ai agent auditor

    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 RAG context is using outdated cache.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.

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

    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.

    • Requires access to log files or architecture configs for deep analysis.
    • Cannot execute live agents in real-time.
    • Scores are advisory based on industry heuristics.

    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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    Trust & safety

    Security scan

    Security scan passed 5 months ago

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    Listed5 months ago
    Updated24 days ago

    What's inside

    Frequently Asked Questions

    The Auditor works with major orchestration frameworks including LangChain, AutoGen, and CrewAI, and is compatible with agents built using OpenAI’s Assistants API or custom Python-based architectures.

    Simply provide access to your agent’s configuration files, tool definitions, and a sample of recent execution logs; the Auditor will then process these against its diagnostic scoring algorithms.

    The audit focuses on identifying logic loops, redundant prompts, and context window mismanagement—the primary drivers of high token costs and latency—providing specific suggestions for model routing and caching.

    You receive full access to the AI Agent Auditor skill, including the specialized scoring framework, issue categorization logic, and the automated reporting template for generating remediation roadmaps.

    Yes, your purchase includes performance updates and compatibility patches to ensure the skill remains effective as new LLM models and framework versions are released.

    Unlike standard logging, this skill performs an 'inside-out' analysis of the prompt chain and tool topology to find systemic architectural flaws rather than just recording individual run errors.

    New in Agents & Orchestration