Agentic Readiness Audit

Purpose

Runs a store-side "Agentic Commerce Readiness" scan — the same questions the public agentiq.report audit asks, but answered from inside the Shopify Admin with full catalog data. It scores whether AI shopping agents (ChatGPT, Gemini, Perplexity, agentic checkout) can FIND, READ, and RECOMMEND the store's products, then prints a prioritized gap list where every gap names the sibling agentic skill that fixes it. Read-only — it changes nothing. Use it first (and on a schedule) to decide which remediation skills to run.

Prerequisites

Parameters

All skills accept these universal parameters:

Skill-specific parameters:

Workflow Steps

Inputs: none

Expected output: Shop name, primary domain, social sameAs links, and policy presence — feeds the identity + policy checks.

Inputs: roles: [MAIN], then theme.files(filenames: ["templates/robots.txt.liquid", "layout/theme.liquid", "assets/llms.txt", "templates/llms.txt.liquid"])

Expected output: Whether the published theme allows AI crawlers (robots), ships an Organization JSON-LD block, and serves an llms.txt — feeds discovery + identity checks.

Inputs: ownerType: PRODUCT

Expected output: Which structured attributes are defined (material, specs, features) — feeds the metafield-coverage check.

Inputs: first: 250, fields: descriptionHtml, category, media, metafields, variants{ barcode, sku, price }

Expected output: Per-product completeness — description length, image alt-text coverage, barcode/GTIN presence, category assigned, metafield population.

Inputs: first: 50, query: "media_type:IMAGE" (sample) — corroborate alt-text coverage at the file level.

Expected output: Alt-text fill rate across product media.

GraphQL Operations

# shop:query — validated against api_version 2025-01
query AgenticReadinessShop {
  shop {
    name
    myshopifyDomain
    primaryDomain { url }
    contactEmail
    shopPolicies { type body url }
  }
}

# themes:query — validated against api_version 2025-01
query AgenticReadinessTheme {
  themes(first: 1, roles: [MAIN]) {
    nodes {
      id
      name
      files(filenames: [
        "templates/robots.txt.liquid",
        "layout/theme.liquid",
        "assets/llms.txt",
        "templates/llms.txt.liquid"
      ]) {
        nodes {
          filename
          body {
            ... on OnlineStoreThemeFileBodyText { content }
          }
        }
      }
    }
  }
}

# metafieldDefinitions:query — validated against api_version 2025-01
query AgenticReadinessMetafieldDefs {
  metafieldDefinitions(first: 100, ownerType: PRODUCT) {
    edges { node { namespace key name type { name } } }
  }
}

# products:query — validated against api_version 2025-01
query AgenticReadinessProducts($first: Int!, $after: String) {
  products(first: $first, after: $after) {
    edges {
      node {
        id
        title
        descriptionHtml
        category { id fullName }
        tags
        media(first: 10) {
          edges { node { ... on MediaImage { id image { altText url } } } }
        }
        metafields(first: 20) { edges { node { namespace key value } } }
        variants(first: 100) {
          edges { node { id sku barcode price } }
        }
      }
    }
    pageInfo { hasNextPage endCursor }
  }
}

# files:query — validated against api_version 2025-01
query AgenticReadinessFiles($first: Int!, $after: String) {
  files(first: $first, after: $after, query: "media_type:IMAGE") {
    edges { node { ... on MediaImage { id alt } } }
    pageInfo { hasNextPage endCursor }
  }
}

Session Tracking

Claude MUST emit the following output at each stage. This is mandatory.

On start, emit:

╔══════════════════════════════════════════════╗
║  SKILL: <skill name>                         ║
║  Store: <store domain>                       ║
║  Started: <YYYY-MM-DD HH:MM UTC>             ║
╚══════════════════════════════════════════════╝

After each step, emit:

[N/TOTAL] <QUERY|MUTATION>  <OperationName>
          → Params: <brief summary of key inputs>
          → Result: <count or outcome>

If dry_run: true, prefix every mutation step with [DRY RUN] and do not execute it.

On completion, emit:

For format: human (default):

══════════════════════════════════════════════
OUTCOME SUMMARY
  <Metric label>:   <value>
  Errors:           0
  Output:           <filename or "none">
══════════════════════════════════════════════

For format: json, emit:

{
  "skill": "<skill-slug>",
  "store": "<domain>",
  "started_at": "<ISO8601>",
  "completed_at": "<ISO8601>",
  "dry_run": false,
  "steps": [
    {
      "step": 1,
      "operation": "<OperationName>",
      "type": "query",
      "params_summary": "<string>",
      "result_summary": "<string>",
      "skipped": false
    }
  ],
  "outcome": {
    "metric_key": 0,
    "errors": 0,
    "output_file": null
  }
}

Output Format

A readiness scorecard. human: an overall 0–100 score + per-pillar bars (Discoverable / Trusted / Readable / Structured / Matchable) + a prioritized gap table where each row is gap → impact → the agentic skill to run. json: { score, grade, pillars{...}, gaps:[{ pillar, audit_signal, finding, fix_skill }], sampled_products }. Every fix_skill value is a sibling skill name (e.g. shopify-admin-agentic-image-alt-text) so the operator can chain straight into remediation.

Error Handling

Best Practices