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    AI Output Quality & Production Readiness Auditor

    1

    A skeptical AI release gate that audits code for security gaps, correctness risks, and production readiness.

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    AI Output Quality & Production Readiness Auditor

    AI Output Quality & Production Readiness Auditor

    Example session with this skill installed

    Audit this Python Flask endpoint. It takes a user-provided URL, fetches the content, and saves it to a local file. Here is the code and the requirement for basic error handling.

    • Read your context and instructions
    • Compiled the ai output quality

    I have audited the Flask endpoint implementation against your requirements for content fetching and error handling.

    Audit Report: URL Content Fetcher

    FindingSeverityCategoryRecommendation
    SSRF VulnerabilityBLOCKERSecurityImplement an allowlist for domains and validate IP addresses to prevent internal network scanning.
    Unbounded File WriteHIGHReliabilityAdd a maximum file size limit and validate the 'Content-Length' header before saving.
    Path Traversal RiskHIGHSecuritySanitize the local filename using werkzeug.utils.secure_filename to prevent writing outside the target directory.
    Missing Request TimeoutsMEDIUMOperationsSet a specific timeout in the requests.get() call to prevent hanging worker processes.
    Basic Error HandlingPASSRequirementImplementation correctly uses try-except blocks for connection errors as requested.

    Verdict: BLOCK

    Executive Summary

    The implementation meets the basic functional requirement but contains critical security vulnerabilities. The SSRF risk and lack of path sanitization make this code unsafe for production. Immediate remediation of input validation is required.

    Prioritized Remediation Plan

    1. Fix Blocker: Add a URL validation layer to block localhost and private IP ranges.
    2. Sanitize Paths: Ensure the local filename cannot contain ../ sequences.
    3. Resource Limits: Implement a 5MB limit on downloaded content and a 10s request timeout.

    Next steps

    • Apply the SSRF mitigation using a validation library.
    • Update the file-writing logic to use absolute paths with secure filename generation.
    • Re-submit the updated diff for a follow-up audit.

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    About this skill

    AI-generated code often hides subtle bugs, security gaps, and production blockers that standard linters miss. ShipGuard acts as a skeptical release gate, auditing your agent's output for correctness risks, missing test coverage, and operational readiness before you merge. It moves beyond syntax checking to evaluate how code actually behaves under edge cases and production constraints.

    What it does

    • Correctness Audit identifies broken control flows, race conditions, and incorrect framework API assumptions.
    • Security Triage flags injection risks, unsafe command operations, and hardcoded secrets.
    • Production Readiness checks for missing health checks, timeout handling, and observability gaps.
    • Requirement Mapping ensures every acceptance criterion is matched to specific implementation artifacts.
    • Verification Gaps distinguishes between code that exists and code that has actually been tested.

    How it works

    1. Submit artifacts including the code diff, requirements, and any available test logs or build outputs.
    2. Review findings categorized by severity from LOW to BLOCKER with evidence-backed rationales.
    3. Check the verdict to see if the deliverable is marked as PASS, REVISE, or BLOCK.
    4. Follow the remediation plan to address identified risks and fill verification unknowns.

    Frameworks & tools

    This skill is language-agnostic and works with any stack, including React, Node.js, Python, Go, and Rust. It is optimized for reviewing code generated by Claude, GPT-4, and specialized coding agents.

    Why this beats prompting it yourself

    Generic "review this code" prompts often result in surface-level feedback or nitpicks about style. ShipGuard follows a structured audit workflow that forces the AI to map requirements to implementation and explicitly verify production concerns like graceful shutdowns and data validation.

    Use cases

    • Pre-PR Audit to catch AI-generated hallucinations and TODOs before a human developer reviews the code.
    • Security Check for auditing third-party code snippets or agent-generated infrastructure-as-code.
    • Release Gating to determine if a feature meets all acceptance criteria and operational standards.

    Known limitations

    Does not execute code or run actual penetration tests. Verdicts are based solely on provided artifacts and cannot guarantee code is bug-free.

    Cannot execute code or run external tools. Reviews are limited to provided snippets and artifacts. Verdicts reflect risk, not a guarantee of safety.

    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 14 days ago

    Permissions

    Read Files
    Write Files
    Listed14 days ago

    What's inside

    Frequently Asked Questions

    ShipGuard is language-agnostic and performs equally well across React, Node.js, Python, Go, and Rust. It is specifically optimized to catch the common hallucinations and 'lazy' implementations typical of models like Claude 3.5 Sonnet and GPT-4o.

    Unlike standard linters that check for syntax and style, ShipGuard evaluates operational logic. It identifies 'silent' production killers like missing retry logic, race conditions in async code, and hardcoded secrets that automated tools often overlook.

    To get the most accurate audit, you should provide the code diff, the original feature requirements or user stories, and any existing test logs. This allows the skill to verify that the implementation actually matches your acceptance criteria.

    ShipGuard outputs a structured report categorizing risks from 'Low' to 'Blocker,' along with a final verdict (Pass, Revise, or Block). You will also receive a specific remediation plan detailing exactly what needs to be fixed to meet production standards.

    ShipGuard analyzes the logic and structure of your artifacts but does not execute the code in a sandbox. It is designed to be a high-fidelity 'skeptical auditor' that flags risks for human review or automated gating, rather than a dynamic penetration testing tool.

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