Shopify Admin Checkout Abandonment Report

Workflow Steps

1. OPERATION: abandonedCheckouts — query Inputs: first: 250, query: "created_at:>='<date_range_start>' created_at:<='<date_range_end>'", pagination cursor Expected output: All abandoned checkouts in range with totalPrice and createdAt; paginate until hasNextPage: false; then aggregate in-memory: (1) count by cart value bucket, (2) count by hour of day (UTC, 0–23)

GraphQL Operations

# abandonedCheckouts:query — validated against api_version 2025-04
query AbandonedCheckoutsReport($first: Int!, $after: String, $query: String) {
  abandonedCheckouts(first: $first, after: $after, query: $query) {
    edges {
      node {
        id
        createdAt
        totalPriceSet {
          shopMoney {
            amount
            currencyCode
          }
        }
        customer {
          defaultEmailAddress {
            emailAddress
          }
        }
        lineItems {
          edges {
            node {
              title
              quantity
              variant {
                price
              }
            }
          }
        }
      }
    }
    pageInfo {
      hasNextPage
      endCursor
    }
  }
}

Session Tracking

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

On start, emit:

╔══════════════════════════════════════════════╗
║  SKILL: checkout-abandonment-report          ║
║  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
  Total abandoned:   <n>
  Date range:        <start> to <end>
  Errors:            0
  Output:            none
══════════════════════════════════════════════

For format: json, emit:

{
  "skill": "checkout-abandonment-report",
  "store": "<domain>",
  "started_at": "<ISO8601>",
  "completed_at": "<ISO8601>",
  "dry_run": false,
  "steps": [
    { "step": 1, "operation": "AbandonedCheckoutsReport", "type": "query", "params_summary": "<date_range_start> to <date_range_end>", "result_summary": "<n> checkouts", "skipped": false }
  ],
  "outcome": {
    "total_abandoned": 0,
    "date_range_start": "<date_range_start>",
    "date_range_end": "<date_range_end>",
    "by_cart_value": [
      { "range": "$0 – $25", "count": 0, "pct": 0.0 }
    ],
    "by_hour_utc": [
      { "hour": "00:00", "count": 0, "pct": 0.0 }
    ],
    "errors": 0,
    "output_file": null
  }
}

Output Format

Two tables displayed inline (no CSV):

Table 1: Abandonment by Cart Value Bucket

Table 2: Abandonment by Hour of Day (UTC)

For format: json, by_cart_value is an array of {range, count, pct} objects; by_hour_utc is an array of {hour, count, pct} objects.

Note: Device type and geographic location are not available in the abandonedCheckouts API and are not reported by this skill.

Error Handling

Best Practices

1. For high-traffic stores, narrow the date range to 7–14 days for faster results; paginating 90 days of data can produce many API calls.
2. The default cart_value_buckets of [0,25,50,100,250] works for most stores — adjust thresholds to match your AOV distribution.
3. Hours are reported in UTC — convert to your store's local timezone before drawing conclusions about peak abandonment times.
4. Run this report weekly and compare the by-hour pattern to your promotional send times to find timing opportunities.
5. email is included in the query result — combine with the abandoned-cart-recovery skill to act on the customers most likely to convert based on their cart value tier.