
Shopify Admin Checkout Abandonment Report
- 6 installs
- 173 repo stars
- Updated June 26, 2026
- 40rty-ai/shopify-admin-skills
shopify-admin-checkout-abandonment-report is a Claude Code skill that aggregates a Shopify store's abandoned checkouts by cart-value bucket and hour of day over a date range.
About
This skill pulls abandoned checkout data from a Shopify store over a date range and aggregates it by cart-value bucket and by hour of day in UTC. Store owners use it to see when and at what price point customers abandon checkout. It is read-only and outputs two inline tables rather than a CSV.
- Aggregates abandoned checkouts by cart-value bucket and hour of day (UTC)
- Read-only query against the Shopify abandonedCheckouts API with paginated fetch
- Reports two inline tables; notes device and geo data are not available from the API
Shopify Admin Checkout Abandonment Report by the numbers
- 6 all-time installs (skills.sh)
- Ranked #1,691 of 2,715 Automation & Workflows skills by installs in the Skillselion catalog
- Data as of Aug 1, 2026 (Skillselion catalog sync)
shopify-admin-checkout-abandonment-report capabilities & compatibility
Free skill; requires an authenticated Shopify store session with the read_checkouts scope.
- Capabilities
- cart abandonment report · conversion analysis · checkout analytics
- Works with
- stripe
- Use cases
- data analysis
- Pricing
- Bring your own API key
What shopify-admin-checkout-abandonment-report says it does
Aggregate abandoned checkout data for a time range, broken down by cart value bucket and hour of day (UTC).
Device type and geographic location are not available in the `abandonedCheckouts` API and are not reported by this skill.
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| Installs | 6 |
|---|---|
| repo stars | ★ 173 |
| Last updated | June 26, 2026 |
| Repository | 40rty-ai/shopify-admin-skills ↗ |
What it does
Report Shopify abandoned checkouts broken down by cart value and hour of day to spot when customers drop off.
Who is it for?
Merchants who want to find the price points and times of day where checkout abandonment concentrates.
Skip if: Recovering carts or sending abandonment emails; it only reports, and it cannot break down by device or geography.
When should I use this skill?
You want a breakdown of when and at what cart value customers are abandoning checkout.
What you get
Two tables show abandoned-checkout counts and percentages by cart-value band and by UTC hour.
- Inline table of abandonment by cart-value bucket
- inline table of abandonment by hour of day (UTC)
By the numbers
- default cart_value_buckets [0,25,50,100,250]
- aggregates across 24 UTC hours (0-23)
- paginates 250 checkouts per request
Files
Purpose
Aggregates abandoned checkout data broken down by cart value bucket and hour of day (UTC). Helps identify when and at what price point customers are most likely to abandon checkout. Scoped to what the abandonedCheckouts API provides — device type and geographic location are not available in this API and are not reported.
Prerequisites
- Authenticated Shopify CLI session:
shopify auth login --store <domain> - API scopes:
read_checkouts
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) |
| cart_value_buckets | array | no | [0, 25, 50, 100, 250] | Array of thresholds defining cart value bands (e.g., [0,25,50,100,250] creates bands: $0–25, $25–50, $50–100, $100–250, $250+) |
Workflow Steps
1. OPERATION: abandonedCheckouts — query Inputs: first: 250, query: "created_at:>='<date_range_start>' created_at:<='<date_range_end>'", pagination cursor Expected output: All abandoned checkouts in range with totalPrice and createdAt; paginate until hasNextPage: false; then aggregate in-memory: (1) count by cart value bucket, (2) count by hour of day (UTC, 0–23)
GraphQL Operations
# abandonedCheckouts:query — validated against api_version 2025-04
query AbandonedCheckoutsReport($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
| Cart Value Range | Abandoned Checkouts | % of Total |
|---|---|---|
| $0 – $25 | ... | ... |
| $25 – $50 | ... | ... |
| $50 – $100 | ... | ... |
| $100 – $250 | ... | ... |
| $250+ | ... | ... |
Table 2: Abandonment by Hour of Day (UTC)
| Hour (UTC) | Abandoned Checkouts | % of Total |
|---|---|---|
| 00:00 | ... | ... |
| 01:00 | ... | ... |
| 02:00 | ... | ... |
| ... |
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
| Error | Cause | Recovery |
|---|---|---|
| No checkouts returned | No abandoned checkouts in date range | Widen date range or verify read_checkouts scope |
| Invalid date format | Date not in ISO 8601 | Use format YYYY-MM-DD |
| Rate limit (429) | Too many paginated requests | Narrow date range or reduce first to 100 |
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.
Related skills
FAQ
Can it break abandonment down by device or country?
No. The skill notes device type and geographic location are not available in the abandonedCheckouts API and are not reported.
Does it export a CSV?
No. It displays two tables inline: abandonment by cart-value bucket and by hour of day (UTC).