shopify-admin-refund-rate-analysis
Read-only: calculates refund rate by product, collection, or period — identifies quality and listing issues.
What this skill does
## 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.
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