shopify-admin-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

• Authenticated Shopify CLI session (shopify auth login --store <domain>)
• Required API scopes: read_products, read_files, read_content (themes), read_online_store_pages

Parameters

All skills accept these universal parameters:

Parameter	Type	Required	Default	Description
store	string	yes	—	Store domain (e.g., mystore.myshopify.com)
format	string	no	human	Output format: human (default) or json
dry_run	bool	no	false	No-op here — this skill never mutates

Skill-specific parameters:

Parameter	Type	Required	Default	Description
sample_size	int	no	250	How many products to sample for the catalog-data checks
min_description_chars	int	no	120	Threshold below which a description counts as "thin"

Workflow Steps

1. OPERATION: shop — query Inputs: none Expected output: Shop name, primary domain, social sameAs links, and policy presence — feeds the identity + policy checks.

2. OPERATION: themes — query 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.

3. OPERATION: metafieldDefinitions — query Inputs: ownerType: PRODUCT Expected output: Which structured attributes are defined (material, specs, features) — feeds the metafield-coverage check.

4. OPERATION: products — query (paginate to sample_size) 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.

5. OPERATION: files — query 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.

6. COMPUTE (no API): roll the findings into a 0–100 readiness score across five pillars — Discoverable (robots/llms.txt), Trusted (Organization schema, sameAs, policies), Readable (descriptions, alt text, JSON-LD fields), Structured (metafields, category, barcodes), Matchable (title/tag/metafield richness for intent) — and map each failing pillar to its fix skill.

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

Error	Cause	Recovery
THROTTLED	API rate limit	Wait 2s, retry up to 3 times
ACCESS_DENIED reading themes	Missing read_content scope	Skip the theme pillar, mark Discoverable/Trusted "unknown", continue
Empty catalog	New/empty store	Report "no products to assess"; still check theme + policies

Best Practices

• Run this FIRST and re-run it after each remediation skill — it's the scoreboard that tells you what's left and what moved.
• Sample, don't crawl: 250 products is enough to estimate fill rates; only audit the full catalog when the sample shows borderline pillars.
• Treat category-unassigned and barcode-missing as the highest-leverage gaps — they unblock both AI retrieval (Matchable) and Product JSON-LD (Readable) at once.
• This skill is read-only; it never needs dry_run. The skills it routes you to DO mutate — run each of those with dry_run: true first.