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

• Authenticated Shopify CLI session: shopify store auth --store <domain> --scopes read_customers,write_customers
• API scopes: read_customers, write_customers

Parameters

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

1. OPERATION: customers — query Inputs: query: <filter>, first: 250, select id, displayName, note, pagination cursor Expected output: Matching customers with existing notes; paginate until hasNextPage: false

2. Construct new note: if append: true, prepend [YYYY-MM-DD] <note> to existing note (newline-separated); if append: false, replace with <note>

3. OPERATION: customerUpdate — mutation Inputs: id: <customer_id>, note: <new_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_<YYYY-MM-DD>.csv with columns: customer_id, name, email, previous_note, new_note

Error Handling

Best Practices

• Always use append: true unless you explicitly intend to overwrite existing notes — staff notes may contain important history.
• Include a datestamp in the note text itself (e.g., "2026-04-11: Campaign X participant") so notes remain interpretable months later.
• Use dry_run: true to confirm the customer count before annotating — a broad filter can match thousands of customers unexpectedly.
• For import-source tracking, annotate immediately after the import run to maintain a clear audit trail.