Shopify Admin Top Product Performance

Workflow Steps

1. OPERATION: orders — query Inputs: first: 250, query: "created_at:>='<date_range_start>' created_at:<='<date_range_end>'", pagination cursor Expected output: All orders in range with line items (title, quantity, originalTotalSet, refundableQuantity) and refund line items; paginate until hasNextPage: false; aggregate in-memory per product: sum originalTotalSet for gross revenue, sum refund amounts for net revenue, sum quantities for units sold, compute refund rate

GraphQL Operations

# orders:query (for product revenue) — validated against api_version 2025-01
query OrdersForProductPerformance($first: Int!, $after: String, $query: String) {
  orders(first: $first, after: $after, query: $query) {
    edges {
      node {
        id
        createdAt
        lineItems(first: 50) {
          edges {
            node {
              title
              quantity
              variant {
                id
                sku
                product {
                  id
                  title
                }
              }
              originalTotalSet {
                shopMoney { amount currencyCode }
              }
              refundableQuantity
            }
          }
        }
        refunds {
          refundLineItems(first: 50) {
            edges {
              node {
                quantity
                lineItem {
                  variant {
                    id
                    product { id title }
                  }
                }
                subtotalSet {
                  shopMoney { amount currencyCode }
                }
              }
            }
          }
        }
      }
    }
    pageInfo {
      hasNextPage
      endCursor
    }
  }
}

Session Tracking

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

On start, emit:

╔══════════════════════════════════════════════╗
║  SKILL: top-product-performance              ║
║  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>

On completion, emit:

For format: human (default):

══════════════════════════════════════════════
OUTCOME SUMMARY
  Orders processed:   <n>
  Products ranked:    <n>
  Date range:         <start> to <end>
  Sort by:            <revenue|units|refund_rate>
  Errors:             0
  Output:             none
══════════════════════════════════════════════

For format: json, emit:

{
  "skill": "top-product-performance",
  "store": "<domain>",
  "started_at": "<ISO8601>",
  "completed_at": "<ISO8601>",
  "dry_run": false,
  "steps": [
    { "step": 1, "operation": "OrdersForProductPerformance", "type": "query", "params_summary": "<date_range_start> to <date_range_end>", "result_summary": "<n> orders processed", "skipped": false }
  ],
  "outcome": {
    "orders_processed": 0,
    "products_ranked": 0,
    "date_range_start": "<date_range_start>",
    "date_range_end": "<date_range_end>",
    "sort_by": "revenue",
    "results": [],
    "errors": 0,
    "output_file": null
  }
}

Output Format

Ranked table displayed inline (no CSV), truncated to top_n entries:

For format: json, results is an array of objects with keys: rank, product_id, product_title, units_sold, gross_revenue, refunded_amount, net_revenue, refund_rate_pct.

Error Handling

Best Practices

1. For stores with many orders, use a 30-day window first. Wider windows paginate more aggressively and take longer.
2. sort_by: refund_rate highlights products with quality or expectation issues — a refund rate above 10% is worth investigating.
3. Revenue figures are gross (before refunds) and net (after refunds) — use net revenue for accurate profitability ranking.
4. Products that have been deleted will still appear if they were purchased in the date range — they show with product_id: null and their title from the order line item.
5. Combine with discount-ab-analysis to see which discount codes drove the most revenue for your top products.