Customer Note Bulk Annotator
Adds internal notes to customer records in bulk — useful for post-campaign flags, import annotations, or support context.
shopify-admin-customer-note-bulk-annotator
Purpose
Queries customers matching a filter (tag, email list, or spend threshold) and appends a note to each customer record. Internal notes are visible to staff in Shopify Admin but not to customers. Used for post-campaign annotation, import source tracking, VIP flags, or support context.
Prerequisites
shopify store auth --store --scopes read_customers,write_customers read_customers, write_customersParameters
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| store | string | yes | — | Store domain (e.g., mystore.myshopify.com) |
| filter | string | yes | — | Customer filter query (e.g., tag:vip, total_spent:>=500) |
| note | string | yes | — | Note text to append to matching customers |
| append | bool | no | true | Append to existing note (true) or replace entirely (false) |
| dry_run | bool | no | true | Preview matching customers without executing mutations |
| format | string | no | human | Output format: human or json |
Safety
> ⚠️ If append: false, this overwrites the existing customer note entirely. Existing notes will be lost. Default is append: true which safely appends with a timestamp prefix. Run with dry_run: true to confirm the customer list before committing.
Workflow Steps
customers — query Inputs: query: , first: 250, select id, displayName, note, pagination cursor
Expected output: Matching customers with existing notes; paginate until hasNextPage: false
append: true, prepend [YYYY-MM-DD] to existing note (newline-separated); if append: false, replace with customerUpdate — mutation Inputs: id: , note:
Expected output: customer { id, note }, userErrors
GraphQL Operations
# customers:query — validated against api_version 2025-01
query CustomersByFilter($query: String!, $after: String) {
customers(first: 250, after: $after, query: $query) {
edges {
node {
id
displayName
defaultEmailAddress {
emailAddress
}
note
tags
}
}
pageInfo {
hasNextPage
endCursor
}
}
}
# customerUpdate:mutation — validated against api_version 2025-01
mutation CustomerUpdateNote($input: CustomerInput!) {
customerUpdate(input: $input) {
customer {
id
displayName
note
}
userErrors {
field
message
}
}
}
Session Tracking
Claude MUST emit the following output at each stage. This is mandatory.
On start, emit:
╔══════════════════════════════════════════════╗
║ SKILL: Customer Note Bulk Annotator ║
║ 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>
If dry_run: true, prefix every mutation step with [DRY RUN] and do not execute it.
On completion, emit:
For format: human (default):
══════════════════════════════════════════════
OUTCOME SUMMARY
Customers matched: <n>
Notes updated: <n>
Errors: <n>
Output: annotation_log_<date>.csv
══════════════════════════════════════════════
For format: json, emit:
{
"skill": "customer-note-bulk-annotator",
"store": "<domain>",
"started_at": "<ISO8601>",
"dry_run": true,
"filter": "<query>",
"note": "<text>",
"append": true,
"outcome": {
"matched": 0,
"updated": 0,
"errors": 0,
"output_file": "annotation_log_<date>.csv"
}
}
Output Format
CSV file annotation_log_ with columns:
customer_id, name, email, previous_note, new_note
Error Handling
| Error | Cause | Recovery |
|---|---|---|
THROTTLED | API rate limit exceeded | Wait 2 seconds, retry up to 3 times |
userErrors on customerUpdate | Invalid input or read-only customer | Log error, skip customer, continue |
| No customers match filter | Filter too narrow | Exit with 0 matches |
Best Practices
append: true unless you explicitly intend to overwrite existing notes — staff notes may contain important history.note text itself (e.g., "2026-04-11: Campaign X participant") so notes remain interpretable months later.dry_run: true to confirm the customer count before annotating — a broad filter can match thousands of customers unexpectedly.