ai agent readiness auditor

    1

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

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    Works with the AI tools you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIGitHub CopilotGitHub CopilotGemini CLIGemini CLI+20 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.

    ai-agent-readiness-auditor.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.

    What you get

    Verify security boundaries against prompt injection and data exfiltration.Quantify hallucination risks in policy-heavy customer support agents.Evaluate failure recovery and graceful degradation under system stress.Generate structured Go/No-Go reports for product and security stakeholders.

    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.

    How to install

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

    ~30 seconds
    1. 1

      Download the ZIP

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    2. 2

      Unzip into your skills folder

      Every agent reads skills from one folder on your machine. Drop the unzipped folder in there.

    3. 3

      Ask your agent to use it

      Restart the agent if it was already running. It picks the skill up automatically - no config needed.

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    Recently published to Agensi

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    Trust & safety

    Security scanned

    Verified clean 18 days ago

    • Passed all security checks, Safe to install

    Listed18 days ago

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