shopify-admin-average-order-value-trends

Name: shopify-admin-average-order-value-trends
Author: 40RTY-ai

168stars17forks

How to install shopify-admin-average-order-value-trends?

40rty-ai/shopify-admin-skills/shopify-admin-average-order-value-trends

$npx -y skills add 40RTY-ai/shopify-admin-skills --skill shopify-admin-average-order-value-trends

Installs into the current project.

Summary

Read-only: tracks AOV over time buckets and segments by new vs. returning customers.

Files · 1

View on GitHub

SKILL.md

1	## Purpose
2	Calculates Average Order Value (AOV) over configurable time buckets (daily, weekly, monthly) and segments results by new vs. returning customers. Tracks AOV trends to measure the impact of upsell programs, bundle offers, or free shipping thresholds. Read-only — no mutations.
3
4	## Prerequisites
5	- Authenticated Shopify CLI session: `shopify store auth --store <domain> --scopes read_orders,read_customers`
6	- API scopes: `read_orders`, `read_customers`
7
8	## Parameters
9
10	\| Parameter \| Type \| Required \| Default \| Description \|
11	\|-----------\|------\|----------\|---------\|-------------\|
12	\| store \| string \| yes \| — \| Store domain (e.g., mystore.myshopify.com) \|
13	\| days_back \| integer \| no \| 90 \| Total lookback window \|
14	\| bucket \| string \| no \| week \| Time bucket: `day`, `week`, or `month` \|
15	\| format \| string \| no \| human \| Output format: `human` or `json` \|
16
17	## Safety
18
19	> ℹ️ Read-only skill — no mutations are executed. Safe to run at any time.
20
21	## Workflow Steps
22
23	1. OPERATION: `orders` — query
24	Inputs: `query: "created_at:>='<NOW - days_back days>'"`, `first: 250`, select `totalPriceSet`, `customer { id, numberOfOrders }`, `createdAt`, pagination cursor
25	Expected output: All orders in window; paginate until `hasNextPage: false`
26
27	2. Classify each order: if `customer.numberOfOrders == 1` → new customer order; else → returning
28
29	3. OPERATION: `customers` — query (optional enrichment for cohort context)
30	Inputs: Recent customers for new vs. repeat segmentation validation
31
32	4. Group orders by time bucket; calculate AOV per bucket and per customer segment
33
34	## GraphQL Operations
35
36	```graphql
37	# orders:query — validated against api_version 2025-01
38	query AOVData($query: String!, $after: String) {
39	orders(first: 250, after: $after, query: $query) {
40	edges {
41	node {
42	id
43	name
44	createdAt
45	totalPriceSet {
46	shopMoney {
47	amount
48	currencyCode
49	}
50	}
51	customer {
52	id
53	numberOfOrders
54	}
55	}
56	}
57	pageInfo {
58	hasNextPage
59	endCursor
60	}
61	}
62	}
63	```
64
65	```graphql
66	# customers:query — validated against api_version 2025-01
67	query NewVsReturningCustomers($query: String!, $after: String) {
68	customers(first: 250, after: $after, query: $query) {
69	edges {
70	node {
71	id
72	numberOfOrders
73	createdAt
74	}
75	}
76	pageInfo {
77	hasNextPage
78	endCursor
79	}
80	}
81	}
82	```
83
84	## Session Tracking
85
86	Claude MUST emit the following output at each stage. This is mandatory.
87
88	On start, emit:
89	```
90	╔══════════════════════════════════════════════╗
91	║ SKILL: Average Order Value Trends ║
92	║ Store: <store domain> ║
93	║ Started: <YYYY-MM-DD HH:MM UTC> ║
94	╚══════════════════════════════════════════════╝
95	```
96
97	After each step, emit:
98	```
99	[N/TOTAL] <QUERY\|MUTATION> <OperationName>
100	→ Params: <brief summary of key inputs>
101	→ Result: <count or outcome>
102	```
103
104	On completion, emit:
105
106	For `format: human` (default):
107	```
108	══════════════════════════════════════════════
109	AOV TRENDS (<days_back> days, bucket: <bucket>)
110	Orders analyzed: <n>
111	Overall AOV: $<amount>
112	New customer AOV: $<amount>
113	Returning AOV: $<amount>
114
115	Period Orders AOV New AOV Returning AOV
116	────────────────────────────────────────────────────
117	2026-W14 <n> $<n> $<n> $<n>
118	Output: aov_trends_<date>.csv
119	══════════════════════════════════════════════
120	```
121
122	For `format: json`, emit:
123	```json
124	{
125	"skill": "average-order-value-trends",
126	"store": "<domain>",
127	"period_days": 90,
128	"overall_aov": 0,
129	"new_customer_aov": 0,
130	"returning_customer_aov": 0,
131	"by_period": [],
132	"output_file": "aov_trends_<date>.csv"
133	}
134	```
135
136	## Output Format
137	CSV file `aov_trends_<YYYY-MM-DD>.csv` with columns:
138	`period`, `order_count`, `aov`, `new_customer_orders`, `new_customer_aov`, `returning_orders`, `returning_aov`, `currency`
139
140	## Error Handling
141	\| Error \| Cause \| Recovery \|
142	\|-------\|-------\|----------\|
143	\| `THROTTLED` \| API rate limit exceeded \| Wait 2 seconds, retry up to 3 times \|
144	\| Guest checkout orders \| No customer record \| Count in totals but exclude from new/returning segmentation \|
145	\| No orders in window \| New store or quiet period \| Exit with 0 AOV \|
146
147	## Best Practices
148	- A free shipping threshold increase or bundle introduction should show up as an AOV lift in the week/month it launched — use this report to measure the impact.
149	- Returning customer AOV is typically higher than new — a shrinking gap may indicate loyalty erosion.
150	- `bucket: week` is best for campaign measurem

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shopify-admin-average-order-value-trends

168stars17forks

View on GitHub

How to install shopify-admin-average-order-value-trends?

40rty-ai/shopify-admin-skills/shopify-admin-average-order-value-trends

$npx -y skills add 40RTY-ai/shopify-admin-skills --skill shopify-admin-average-order-value-trends

Installs into the current project.

Summary

Read-only: tracks AOV over time buckets and segments by new vs. returning customers.

Files · 1

View on GitHub

SKILL.md

1	## Purpose
2	Calculates Average Order Value (AOV) over configurable time buckets (daily, weekly, monthly) and segments results by new vs. returning customers. Tracks AOV trends to measure the impact of upsell programs, bundle offers, or free shipping thresholds. Read-only — no mutations.
3
4	## Prerequisites
5	- Authenticated Shopify CLI session: `shopify store auth --store <domain> --scopes read_orders,read_customers`
6	- API scopes: `read_orders`, `read_customers`
7
8	## Parameters
9
10	\| Parameter \| Type \| Required \| Default \| Description \|
11	\|-----------\|------\|----------\|---------\|-------------\|
12	\| store \| string \| yes \| — \| Store domain (e.g., mystore.myshopify.com) \|
13	\| days_back \| integer \| no \| 90 \| Total lookback window \|
14	\| bucket \| string \| no \| week \| Time bucket: `day`, `week`, or `month` \|
15	\| format \| string \| no \| human \| Output format: `human` or `json` \|
16
17	## Safety
18
19	> ℹ️ Read-only skill — no mutations are executed. Safe to run at any time.
20
21	## Workflow Steps
22
23	1. OPERATION: `orders` — query
24	Inputs: `query: "created_at:>='<NOW - days_back days>'"`, `first: 250`, select `totalPriceSet`, `customer { id, numberOfOrders }`, `createdAt`, pagination cursor
25	Expected output: All orders in window; paginate until `hasNextPage: false`
26
27	2. Classify each order: if `customer.numberOfOrders == 1` → new customer order; else → returning
28
29	3. OPERATION: `customers` — query (optional enrichment for cohort context)
30	Inputs: Recent customers for new vs. repeat segmentation validation
31
32	4. Group orders by time bucket; calculate AOV per bucket and per customer segment
33
34	## GraphQL Operations
35
36	```graphql
37	# orders:query — validated against api_version 2025-01
38	query AOVData($query: String!, $after: String) {
39	orders(first: 250, after: $after, query: $query) {
40	edges {
41	node {
42	id
43	name
44	createdAt
45	totalPriceSet {
46	shopMoney {
47	amount
48	currencyCode
49	}
50	}
51	customer {
52	id
53	numberOfOrders
54	}
55	}
56	}
57	pageInfo {
58	hasNextPage
59	endCursor
60	}
61	}
62	}
63	```
64
65	```graphql
66	# customers:query — validated against api_version 2025-01
67	query NewVsReturningCustomers($query: String!, $after: String) {
68	customers(first: 250, after: $after, query: $query) {
69	edges {
70	node {
71	id
72	numberOfOrders
73	createdAt
74	}
75	}
76	pageInfo {
77	hasNextPage
78	endCursor
79	}
80	}
81	}
82	```
83
84	## Session Tracking
85
86	Claude MUST emit the following output at each stage. This is mandatory.
87
88	On start, emit:
89	```
90	╔══════════════════════════════════════════════╗
91	║ SKILL: Average Order Value Trends ║
92	║ Store: <store domain> ║
93	║ Started: <YYYY-MM-DD HH:MM UTC> ║
94	╚══════════════════════════════════════════════╝
95	```
96
97	After each step, emit:
98	```
99	[N/TOTAL] <QUERY\|MUTATION> <OperationName>
100	→ Params: <brief summary of key inputs>
101	→ Result: <count or outcome>
102	```
103
104	On completion, emit:
105
106	For `format: human` (default):
107	```
108	══════════════════════════════════════════════
109	AOV TRENDS (<days_back> days, bucket: <bucket>)
110	Orders analyzed: <n>
111	Overall AOV: $<amount>
112	New customer AOV: $<amount>
113	Returning AOV: $<amount>
114
115	Period Orders AOV New AOV Returning AOV
116	────────────────────────────────────────────────────
117	2026-W14 <n> $<n> $<n> $<n>
118	Output: aov_trends_<date>.csv
119	══════════════════════════════════════════════
120	```
121
122	For `format: json`, emit:
123	```json
124	{
125	"skill": "average-order-value-trends",
126	"store": "<domain>",
127	"period_days": 90,
128	"overall_aov": 0,
129	"new_customer_aov": 0,
130	"returning_customer_aov": 0,
131	"by_period": [],
132	"output_file": "aov_trends_<date>.csv"
133	}
134	```
135
136	## Output Format
137	CSV file `aov_trends_<YYYY-MM-DD>.csv` with columns:
138	`period`, `order_count`, `aov`, `new_customer_orders`, `new_customer_aov`, `returning_orders`, `returning_aov`, `currency`
139
140	## Error Handling
141	\| Error \| Cause \| Recovery \|
142	\|-------\|-------\|----------\|
143	\| `THROTTLED` \| API rate limit exceeded \| Wait 2 seconds, retry up to 3 times \|
144	\| Guest checkout orders \| No customer record \| Count in totals but exclude from new/returning segmentation \|
145	\| No orders in window \| New store or quiet period \| Exit with 0 AOV \|
146
147	## Best Practices
148	- A free shipping threshold increase or bundle introduction should show up as an AOV lift in the week/month it launched — use this report to measure the impact.
149	- Returning customer AOV is typically higher than new — a shrinking gap may indicate loyalty erosion.
150	- `bucket: week` is best for campaign measurem

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6 picks

Type