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    Agent Ready Storefront Audit

    by PubsProToolkit

    1

    Audit your online store to ensure it is discoverable and purchasable by autonomous AI shopping agents.

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    See it in action

    You say

    Audit this Shopify product page content and its JSON-LD schema to see if an AI shopping agent can autonomously buy this 'Eco-Therm Flask'. [Attached HTML and Schema]

    Your agent does

    VERDICT: NEEDS WORK SCORE BY DIMENSION:

    • Product data: PASS
    • Structured data: WEAK
    • Policy clarity: MISSING
    • Trust signals: PASS
    • Transactability: WEAK

    TOP FIXES:

    1. Structured Data: Missing 'priceValidUntil' and 'availability' in JSON-LD.
    2. Policy: Return window is "flexible" - define as "30 days".

    What you get

    Identify technical blockers preventing AI agents from purchasing productsValidate JSON-LD and Schema.org markup for machine-readabilitySimplify refund and shipping policies for autonomous agent parsingAudit checkout flows for bot-unfriendly gates and interstitials

    About this skill

    What it does

    This skill is an adversarial auditor that reviews your online store through the eyes of an AI shopping agent. It reads your product pages, policies, and metadata and judges whether an autonomous agent could discover, evaluate, and complete a purchase without a human stepping in.

    The problem it tackles

    Most stores are built for human eyes, with key facts hidden in images or buried in vague prose. A person can figure it out, but an AI shopping agent comparing options for a buyer tends to skip stores with unstructured data, ambiguous return policies, or checkout paths it cannot follow. This is the sell-side counterpart to discovery: not "can an agent find you" but "can an agent confidently transact with you." The audit surfaces those agent-blockers so you can fix them.

    What it reviews

    Give it raw HTML, a JSON-LD schema.org snippet, a product description, or policy text from any platform, including Shopify, WooCommerce, or a custom build. It looks for machine-readable product identity, stable pricing and availability, parseable policies, trust signals, and a clear path to checkout.

    What you get

    A structured scorecard across five dimensions (product data, structured data, policy clarity, trust signals, and transactability), a clear verdict, and a ranked list of copy-ready fixes with the offending text quoted. The bundled reference file adds a checklist plus fill-in templates for product JSON-LD, returns, and shipping policies.

    This is an audit aid based on general agent-commerce norms. It judges only the content you provide and cannot guarantee that any specific agent will buy or that fixes will increase sales. Verify against your own platform and live store behavior.

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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    Recently published to Agensi

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    Verified clean 1 month ago

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    Creator

    PubsProToolkit builds rigor-first skills for AI agents — they write your docs and content properly, then adversarially review them to catch what's wrong before it ships. The result: cleaner output and a hard quality gate in one toolkit. Built by a CMPP-certified, PhD medical writer who brings regulated-industry standards to developer docs, content, compliance, and research integrity.

    Frequently Asked Questions

    Popular in E-commerce