Agentic Product Jsonld Backfill

Backfill the structured product/variant fields that power Product JSON-LD — barcode (GTIN), SKU, vendor, product type, weight — so AI agents can quote exact, in-stock items instead of guessing.

shopify-admin-agentic-product-jsonld-backfill


Purpose

AI shopping agents read a product's structured data (the fields Shopify themes emit as schema.org/Product JSON-LD) to confirm price, availability, and identity. Missing barcodes (GTIN), SKUs, vendor, or product type leave the listing ambiguous — so the agent skips it or recommends a competitor whose data is complete. This skill finds products/variants with those gaps and backfills them: vendor and product type at the product level, barcode/SKU at the variant level. Fixes the agentiq.report findings product-schema-jsonld, gtin-sku-pdp, and variant-metadata.


Prerequisites

  • Authenticated Shopify CLI session (shopify auth login --store )
  • Required API scopes: read_products, write_products

  • Parameters

    All skills accept these universal parameters:


    ParameterTypeRequiredDefaultDescription
    storestringyesStore domain (e.g., mystore.myshopify.com)
    formatstringnohumanOutput format: human (default) or json
    dry_runboolnofalsePreview mutations without executing

    Skill-specific parameters:


    ParameterTypeRequiredDefaultDescription
    collection_idstringnoLimit to a collection GID (else whole catalog)
    tagstringnoLimit to a product tag
    set_vendorstringnoVendor to apply where missing (else only reports)
    set_product_typestringnoProduct type to apply where missing
    barcodes_csvstringnoPath to a CSV of sku,barcode to map GTINs onto matching variants
    fieldsstringnoallComma list of fields to backfill: vendor,product_type,barcode,sku

    Safety


    > ⚠️ Step 3 (productUpdate) and Step 4 (productVariantsBulkUpdate) write live product/variant data. Barcodes and SKUs are matched from your barcodes_csv; a wrong mapping mislabels a product's identity to every agent. Always run dry_run: true first and verify the change set CSV. This skill never overwrites a field that already has a value — it only fills blanks.


    Workflow Steps


  • OPERATION: products — query
  • Inputs: first: 250, optional query: "tag:''" or collection filter; fields vendor, productType, variants{ id sku barcode }; paginate until hasNextPage: false.

    Expected output: Products/variants with missing target fields.


  • COMPUTE (no API): build the change set — only blank fields, joined to barcodes_csv by SKU for barcodes. Emit the preview CSV.

  • OPERATION: productUpdate — mutation
  • Inputs: per product { id, vendor?, productType? } (only where blank and a value is supplied).

    Expected output: Updated product; collect userErrors.


  • OPERATION: productVariantsBulkUpdate — mutation
  • Inputs: per product productId + variants: [{ id, barcode?, inventoryItem: { sku? } }] for blank variant fields.

    Expected output: Updated variants; collect userErrors across batches.


    GraphQL Operations


    # products:query — validated against api_version 2025-01
    query BackfillProducts($first: Int!, $after: String, $query: String) {
      products(first: $first, after: $after, query: $query) {
        edges {
          node {
            id
            title
            vendor
            productType
            variants(first: 100) {
              edges { node { id sku barcode } }
            }
          }
        }
        pageInfo { hasNextPage endCursor }
      }
    }
    

    # productUpdate:mutation — validated against api_version 2025-01
    mutation BackfillProductFields($input: ProductInput!) {
      productUpdate(input: $input) {
        product { id vendor productType }
        userErrors { field message }
      }
    }
    

    # productVariantsBulkUpdate:mutation — validated against api_version 2025-01
    mutation BackfillVariantFields($productId: ID!, $variants: [ProductVariantsBulkInput!]!) {
      productVariantsBulkUpdate(productId: $productId, variants: $variants) {
        productVariants { id sku barcode }
        userErrors { field message }
      }
    }
    

    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

    human: counts of products/variants updated per field + a CSV of every change (product, variant, field, old, new). json: { products_updated, variants_updated, by_field{...}, errors, output_file }.


    Error Handling

    ErrorCauseRecovery
    THROTTLEDAPI rate limitWait 2s, retry up to 3 times
    userErrors non-emptyInvalid barcode/SKU format or duplicateLog message, skip that variant, continue
    SKU not in CSVNo mapping supplied for that variantLeave barcode blank, report it as still-missing

    Best Practices

  • Run shopify-admin-agentic-readiness-audit first to size the gap, then dry_run: true here to review the exact change set.
  • Barcodes are GTIN/UPC/EAN — get them from your supplier, never invent them. A wrong GTIN is worse than a blank one.
  • This skill only fills blanks; to correct existing-but-wrong values use shopify-admin-bulk-price-adjustment-style targeted edits instead.
  • Pair with shopify-admin-agentic-metafields-setup — barcodes power JSON-LD identity, metafields power agent filtering; you usually want both.