# Shopify Admin Top Product Performance

> Rank products by revenue, units sold, and refund rate over a date range by aggregating order line items.

- Skill: `40rty-ai/shopify-admin-top-product-performance` (Agent Skill)
- Install (CLI): `npx skillmds@latest add 40rty-ai/shopify-admin-top-product-performance`
- Raw SKILL.md: https://api.skillmd.com/api/skills/40rty-ai/shopify-admin-top-product-performance/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Finance & Business
- Author: 40rty-ai (https://skillmd.com/u/40rty-ai)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/40rty-ai/shopify-admin-top-product-performance

---


## Purpose
Ranks products by revenue, units sold, and refund rate for a given date range by aggregating order line items and refund line items across all orders in the period. Useful for identifying top performers and products with high refund rates. Read-only — no mutations are executed.

## Prerequisites
- Authenticated Shopify CLI session: `shopify auth login --store <domain>`
- API scopes: `read_orders`

## Parameters

| Parameter | Type | Required | Default | Description |
|-----------|------|----------|---------|-------------|
| store | string | yes | — | Store domain (e.g., mystore.myshopify.com) |
| format | string | no | human | Output format: `human` or `json` |
| dry_run | bool | no | false | Preview operations without executing mutations |
| date_range_start | string | yes | — | Start date in ISO 8601 (e.g., `2025-01-01`) |
| date_range_end | string | yes | — | End date in ISO 8601 (e.g., `2025-01-31`) |
| top_n | integer | no | 20 | Number of top products to show in the ranked output |
| sort_by | string | no | revenue | Ranking metric: `revenue`, `units`, or `refund_rate` |

## 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

```graphql
# 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:
```json
{
  "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:

| Rank | Product | Units Sold | Gross Revenue | Refunded Amount | Net Revenue | Refund Rate % |
|------|---------|------------|---------------|-----------------|-------------|---------------|
| 1 | ... | ... | ... | ... | ... | ... |

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
| Error | Cause | Recovery |
|-------|-------|----------|
| No orders returned | No orders in date range | Widen date range |
| `variant` is null on a line item | Product or variant was deleted | Still aggregated by title; product_id will be null |
| Rate limit (429) | Too many paginated requests | Narrow date range |

## 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.