Shopify Admin Partial Refund Pattern Detector

Purpose

Scans recent orders, extracts every refund, and flags orders that have either (a) two or more partial refunds, or (b) a partial-refund-to-order-total ratio above a configurable threshold. These patterns frequently indicate friendly fraud (incremental claims), an unhappy repeat customer pattern, or a staff workflow gap (refunding piecemeal instead of issuing one full credit). Read-only — no mutations.

Prerequisites

• Authenticated Shopify CLI session: shopify store auth --store <domain> --scopes read_orders
• API scopes: read_orders

Parameters

Safety

ℹ️ Read-only skill — no mutations are executed. Safe to run at any time. Flagged orders are advisory — confirm with refund notes and customer history before taking action against a customer account.

Workflow Steps

1. OPERATION: orders — query Inputs: query: "created_at:>='<NOW - days_back days>' financial_status:partially_refunded", first: 250, select refunds { id, createdAt, totalRefundedSet, note }, totalPriceSet, customer, pagination cursor Expected output: All partially refunded orders with full refund history; paginate until hasNextPage: false

2. For each order, count refunds and sum totalRefundedSet.shopMoney.amount. Compute ratio = total_refunded / order_total.

3. Flag orders meeting either condition: refund_count >= min_partials OR ratio >= ratio_threshold (and ratio < 1.0 so fully refunded orders are excluded).

4. Group flagged orders by customer.id to surface repeat-offender customers (more than one flagged order in the window).

GraphQL Operations

# orders:query — validated against api_version 2025-01
query PartialRefundPatterns($query: String!, $after: String) {
  orders(first: 250, after: $after, query: $query) {
    edges {
      node {
        id
        name
        createdAt
        displayFinancialStatus
        totalPriceSet {
          shopMoney {
            amount
            currencyCode
          }
        }
        totalRefundedSet {
          shopMoney {
            amount
            currencyCode
          }
        }
        refunds {
          id
          createdAt
          note
          totalRefundedSet {
            shopMoney {
              amount
              currencyCode
            }
          }
          refundLineItems(first: 50) {
            edges {
              node {
                quantity
                lineItem {
                  id
                  title
                  sku
                }
              }
            }
          }
        }
        customer {
          id
          displayName
          defaultEmailAddress {
            emailAddress
          }
          numberOfOrders
        }
      }
    }
    pageInfo {
      hasNextPage
      endCursor
    }
  }
}

Session Tracking

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

On start, emit:

╔══════════════════════════════════════════════╗
║  SKILL: Partial Refund Pattern Detector      ║
║  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):

══════════════════════════════════════════════
PARTIAL REFUND PATTERN REPORT  (<days_back> days)
  Partially refunded orders:   <n>
  Flagged (multi-refund):      <n>
  Flagged (high ratio):        <n>
  Repeat-flagged customers:    <n>

  Top flagged customers by amount:
    <customer>  Orders: <n>  Refunded: $<n>  Ratio: <pct>%
  Output: partial_refund_patterns_<date>.csv
══════════════════════════════════════════════

For format: json, emit:

{
  "skill": "partial-refund-pattern-detector",
  "store": "<domain>",
  "period_days": 90,
  "partially_refunded_orders": 0,
  "flagged_multi_refund": 0,
  "flagged_high_ratio": 0,
  "repeat_flagged_customers": 0,
  "output_file": "partial_refund_patterns_<date>.csv"
}

Output Format

CSV file partial_refund_patterns_<YYYY-MM-DD>.csv with columns: order_name, order_id, customer_email, customer_lifetime_orders, order_total, total_refunded, refund_ratio, refund_count, flag_reason, first_refund_at, last_refund_at

Error Handling

Best Practices

• Combine min_partials: 2 and ratio_threshold: 0.5 for the most useful signal — single small partial refunds are usually legitimate.
• Sort by refund_ratio descending: high ratios on high-value orders are the strongest fraud signal.
• A repeat-flagged customer with numberOfOrders > 5 is often a chronic complainer, not a fraudster — review the refund notes before action.
• Use this skill quarterly alongside order-risk-report to detect post-purchase fraud that fraud filters miss at checkout.
• Refund note content frequently reveals the pattern (e.g., "item missing" repeated three times) — read the notes before flagging a customer.