orders:query — validated against api_version 2025-01

---
name: shopify-admin-refund-rate-analysis-8409af96315a
role: finance
description: "Read-only: calculates refund rate by product, collection, or period — identifies quality and listing issues."
toolkit: shopify-admin, shopify-admin-execution
api_version: "2025-01"
graphql_operations:
  - orders:query
status: stable
compatibility: Claude Code, Cursor, Codex, Gemini CLI
---

## Purpose
Analyzes orders with refunds to calculate refund rates by product, time period, and channel. Surfaces which products or product groups generate the most refund activity. Read-only — no mutations.

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

## Parameters

| Parameter | Type | Required | Default | Description |
|-----------|------|----------|---------|-------------|
| store | string | yes | — | Store domain (e.g., mystore.myshopify.com) |
| days_back | integer | no | 30 | Lookback window |
| group_by | string | no | product | Breakdown: `product`, `vendor`, or `period` |
| min_orders | integer | no | 5 | Minimum orders per group to include in rate calculation |
| format | string | no | human | Output format: `human` or `json` |

## Safety

> ℹ️ Read-only skill — no mutations are executed. Safe to run at any time.

## Workflow Steps

1. **OPERATION:** `orders` — query
   **Inputs:** `query: "created_at:>='<NOW - days_back days>'"`, `first: 250`, select `refunds { refundLineItems }`, `lineItems`, pagination cursor
   **Expected output:** All orders with refund data; paginate until `hasNextPage: false`

2. For each refunded line item: record product, vendor, quantity refunded, refund amount

3. Aggregate by `group_by`: calculate `refund_rate = refunded_units / total_units_sold × 100`

## GraphQL Operations

```graphql
# orders:query — validated against api_version 2025-01
query OrdersWithRefunds($query: String!, $after: String) {
  orders(first: 250, after: $after, query: $query) {
    edges {
      node {
        id
        name
        createdAt
        lineItems(first: 50) {
          edges {
            node {
              id
              quantity
              product {
                id
                title
                vendor
              }
              variant {
                id
                sku
              }
            }
          }
        }
        refunds {
          id
          createdAt
          totalRefundedSet {
            shopMoney {
              amount
              currencyCode
            }
          }
          refundLineItems(first: 50) {
            edges {
              node {
                quantity
                lineItem {
                  product {
                    id
                    title
                    vendor
                  }
                  variant {
                    id
                    sku
                  }
                }
              }
            }
          }
        }
      }
    }
    pageInfo {
      hasNextPage
      endCursor
    }
  }
}
```

## Session Tracking

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

**On start**, emit:
```
╔══════════════════════════════════════════════╗
║  SKILL: Refund Rate Analysis                 ║
║  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):
```
══════════════════════════════════════════════
REFUND RATE ANALYSIS  (<days_back> days)
  Orders analyzed:       <n>
  Orders with refunds:   <n>
  Overall refund rate:   <pct>%
  Total refunded:        $<amount>

  By <group_by>:
    "<name>"   Sold: <n>  Refunded: <n>  Rate: <pct>%
  Output: refund_rate_<date>.csv
══════════════════════════════════════════════
```

For `format: json`, emit:
```json
{
  "skill": "refund-rate-analysis",
  "store": "<domain>",
  "period_days": 30,
  "orders_analyzed": 0,
  "orders_with_refunds": 0,
  "overall_refund_rate_pct": 0,
  "total_refunded": 0,
  "currency": "USD",
  "output_file": "refund_rate_<date>.csv"
}
```

## Output Format
CSV file `refund_rate_<YYYY-MM-DD>.csv` with columns:
`group`, `group_name`, `total_units_sold`, `refunded_units`, `refund_rate_pct`, `total_refund_amount`, `currency`

## Error Handling
| Error | Cause | Recovery |
|-------|-------|----------|
| `THROTTLED` | API rate limit exceeded | Wait 2 seconds, retry up to 3 times |
| No refunds in window | Clean period | Exit with 0% rate, expected |
| Deleted product on refund line | Product removed after refund | Log as "deleted product" in group |

## Best Practices
- A refund rate above 5–10% on specific products typically signals a listing, quality, or expectation mismatch issue.
- Use `group_by: vendor` to identify if quality problems are concentrated with a specific supplier.
- Cross-reference high-refund products with `return-reason-analysis` to understand whether the issue is product quality, wrong size, or customer expectation.
- Run before quarterly supplier reviews to support data-driven conversations about product quality and chargebacks.