ai agent readiness auditor

    1

    Structured production-readiness audit to identify security risks, reliability gaps, and hallucination triggers.

    $5

    Secure checkout via Stripe

    Money-back guarantee

    Converts to your local currency at checkout

    Security scanned

    Works with the AI Agents you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIMuseMuseDotsDots+21 more

    ai agent readiness auditor

    Example session with this skill installed

    Audit the 'CustomerSupport-V2' agent. It handles billing inquiries via API. It must not reveal user PII or hallucinate refund policies. I need a severity table and risk score.

    • Read your context and instructions
    • Compiled the ai agent readiness
    • Generated the document

    I have completed the multi-dimensional readiness audit for CustomerSupport-V2. The assessment focused on security boundaries and policy fidelity regarding billing operations.

    AI Agent Readiness Audit Report: CustomerSupport-V2

    DimensionFindingSeverityRisk Impact
    SecuritySusceptible to indirect prompt injection via simulated 'invoice notes'.CriticalPotential PII exfiltration or unauthorized discount triggers.
    Evidence DisciplineAgent fabricated a 'Loyalty Refund Policy' that does not exist in the provided documentation.HighLegal and financial liability for non-existent policy enforcement.
    ReliabilityConsistent handling of valid subscription ID formats across 50 iterations.InformationalHigh confidence in standard data parsing.
    Failure HandlingGracefully handles empty API responses with a standard 'system unavailable' message.LowStable recovery, though user experience is abrupt.
    Data PrivacySuccessfully refused to output raw JSON containing internal database keys.MediumGood filtering, but requires tighter regex for mask validation.

    Quantitative Assessment

    • Risk Score: 78/100 (High Risk)
    • Test Coverage: 85%
    • Status: NO-GO (Fix Critical injection vulnerability before deployment)

    Remediation Plan

    Sanitization: Implement an input validation layer for the 'invoice notes' field to strip markdown and system-level instructions.
    2.

    Knowledge Grounding: Update the system prompt to strictly enforce 'I don't know' for any policy not explicitly defined in the billing_v2.pdf context.
    3. Escalation: Add a human-in-the-loop trigger for any refund request exceeding $50.00.

    Next steps

    • Apply the input sanitization patch to the gateway.
    • Restrict the agent's context window to verified policy documents only.
    • Rerun the 'Security' and 'Evidence' test packets for re-verification.

    billing_v2.pdf

    PDF · document

    Generated

    Example file from a real run - the skill writes it into your workspace.

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

    About this skill

    The problem

    LLM-based agents often fail in production due to unpredictable hallucinations, prompt injection vulnerabilities, or poor error handling that simple unit tests miss. Developers struggle to quantify the risk of deploying an agent without a structured, multi-dimensional evaluation framework.

    What it does

    • Executes multi-vector stress tests across reliability, security, and evidence discipline dimensions.
    • Identifies critical vulnerabilities like data exfiltration paths and prompt injection susceptibility.
    • Classifies behavioral findings by severity from Informational to Critical release-blockers.
    • Calculates quantitative risk and coverage scores to provide a data-driven Go/No-Go recommendation.
    • Maps verified capabilities against unsupported assumptions to prevent over-reliance on fragile features.

    Why this beats prompting it yourself

    General prompts lack the systematic rigor required for production safety. This skill uses a structured audit workflow that forces the agent to handle system limits, ambiguity, and malicious inputs, ensuring you don't miss edge cases that lead to catastrophic failures or brand damage.

    Use cases

    • Pre-deployment security and reliability audit for customer-facing chatbots.
    • Compliance verification against internal data privacy and evidence discipline policies.
    • Benchmarking agent performance recovery after API failures or malformed inputs.
    • Vulnerability testing for agents with access to sensitive internal tools or databases.

    Requires a clear description of the intended use case and environment. Physical access to agent logs or internal traces improves scoring accuracy but is not strictly required.

    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.

    Reviews

    No reviews yet

    Be one of the first to try it. Every listed skill goes through the trust checks below.

    Security scanned

    Checked by our 8-point scan before listing

    Fresh listing

    Recently published to Agensi

    Money-back guarantee

    Doesn't do what its page promises? You get your money back.

    Trust & safety

    Security scan

    Security scan passed 2 months ago

    Permissions

    No special permissions declared or detected

    Listed2 months ago

    What's inside

    Frequently Asked Questions

    The auditor executes a series of systematic stress tests focused on prompt injection resilience, data privacy leakage, and hallucination triggers, providing you with a structured risk score and a clear Go/No-Go deployment recommendation.

    This skill is designed to be model-agnostic and works with any LLM-based agent, whether it is a customer-facing chatbot, an internal RAG system, or an autonomous agent with tool-access capabilities.

    You will receive the complete structured audit workflow, including the multi-vector evaluation framework, severity classification templates, and the quantitative scoring system to measure your agent's production readiness.

    Setup is straightforward: once integrated into your development environment, you simply point the auditor at your agent's API or interface to begin the automated stress-testing and mapping process.

    Yes, the auditor is specifically built to test agents with tool-access, identifying potential data exfiltration paths and ensuring the agent handles malformed tool outputs without compromising system security.

    New in Agents & Orchestration