/shopify-admin-checkout-abandonment-report
Aggregate abandoned checkout data for a time range, broken down by cart value bucket and hour of day (UTC).
$ npx -y skills add 40rty-ai/shopify-admin-skills --skill shopify-admin-checkout-abandonment-report --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/shopify-admin-checkout-abandonment-report
Context preview
The summary Claude sees to decide when to auto-load this skill.
Aggregate abandoned checkout data for a time range, broken down by cart value bucket and hour of day (UTC).
SKILL.md
shopify-admin-checkout-abandonment-report.SKILL.mdname: shopify-admin-checkout-abandonment-report
role: conversion-optimization
description: "Aggregate abandoned checkout data for a time range, broken down by cart value bucket and hour of day (UTC)."
toolkit: shopify-admin, shopify-admin-execution
api_version: "2025-01"
graphql_operations:
- abandonedCheckouts:query
status: stable
compatibility: Claude Code, Cursor, Codex, Gemini CLI
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-
Read more
name: shopify-admin-checkout-abandonment-report role: conversion-optimization description: "Aggregate abandoned checkout data for a time range, broken down by cart value bucket and hour of day (UTC)." toolkit: shopify-admin, shopify-admin-execution api_version: "2025-01" graphql_operations: - abandonedCheckouts:query status: stable compatibility: Claude Code, Cursor, Codex, Gemini CLI
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-
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