Shopify Admin Vip Customer Identifier

Purpose

Ranks customers by lifetime spend and order frequency, identifies the top N% (by value, frequency, or both), and outputs a CSV of VIP candidates. Optionally applies a VIP tag to qualified customers via customerUpdate. Used to build loyalty segments, prioritize white-glove support, or seed exclusive-access campaigns. The lifetime spend and order count are pulled directly from Shopify customer aggregates — no external CRM required.

Prerequisites

• Authenticated Shopify CLI session: shopify store auth --store --scopes read_customers,read_orders,write_customers
• API scopes: read_customers, read_orders, write_customers (only if tag_customers: true)

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 | | dryrun | bool | no | true | Preview VIP list without applying tags | | rankby | string | no | spend | Ranking strategy: spend (lifetime value), frequency (order count), or both (composite score) | | toppct | float | no | 5 | Top percentile to qualify as VIP (e.g., 5 = top 5%) | | minorders | integer | no | 2 | Minimum lifetime orders to be eligible | | minspend | float | no | 0 | Minimum lifetime spend (shop currency) to be eligible | | tagcustomers | bool | no | false | If true, apply VIP tag to qualified customers via customerUpdate | | tag | string | no | vip | Tag string applied when tag_customers: true |

Safety

> ⚠️ When tag_customers: true, Step 3 executes customerUpdate mutations that mutate customer tag lists. Tags persist until manually removed. Run with dry_run: true first to confirm the VIP list and qualifying thresholds. The default is dry_run: true — you must explicitly set dry_run: false and tag_customers: true to apply tags.

Workflow Steps

1. OPERATION: customers — query

Inputs: first: 250, query: "orders_count:>=", select id, displayName, defaultEmailAddress { emailAddress }, numberOfOrders, amountSpent { amount currencyCode }, tags, pagination cursor Expected output: All customers meeting min_orders threshold; paginate until hasNextPage: false

1. OPERATION: orders — query (only when ranking by frequency, for recency annotation)

Inputs: For each top candidate: query: "customer_id:", first: 1, sortKey: CREATED_AT, reverse: true Expected output: Most recent order per candidate to annotate the export

1. Filter to amountSpent.amount >= min_spend. Score each customer: spend → spend; frequency → orders; both → 0.6 × normalized spend + 0.4 × normalized frequency. Take the top top_pct%.

1. OPERATION: customerUpdate — mutation (only if tag_customers: true and dry_run: false)

Inputs: input: { id: , tags: [...existing_tags, ] } Expected output: customer.id, customer.tags, userErrors

GraphQL Operations

# customers:query — validated against api_version 2025-01
query VIPCandidateCustomers($first: Int!, $after: String, $query: String) {
  customers(first: $first, after: $after, query: $query) {
    edges {
      node {
        id
        displayName
        firstName
        lastName
        defaultEmailAddress {
          emailAddress
        }
        numberOfOrders
        amountSpent {
          amount
          currencyCode
        }
        tags
        createdAt
      }
    }
    pageInfo {
      hasNextPage
      endCursor
    }
  }
}

# orders:query — validated against api_version 2025-01
query VIPLastOrder($query: String!) {
  orders(first: 1, query: $query, sortKey: CREATED_AT, reverse: true) {
    edges {
      node {
        id
        name
        createdAt
        totalPriceSet {
          shopMoney { amount currencyCode }
        }
      }
    }
  }
}

# customerUpdate:mutation — validated against api_version 2025-01
mutation CustomerUpdateVipTag($input: CustomerInput!) {
  customerUpdate(input: $input) {
    customer {
      id
      displayName
      tags
    }
    userErrors {
      field
      message
    }
  }
}

Session Tracking

Claude MUST emit the following output at each stage. This is mandatory.

On start, emit:

╔══════════════════════════════════════════════╗
║  SKILL: VIP Customer Identifier              ║
║  Store:                        ║
║  Started:              ║
╚══════════════════════════════════════════════╝

After each step, emit:

[N/TOTAL]   
          → Params: 
          → Result: 

If dry_run: true, prefix every mutation step with [DRY RUN] and do not execute it.

On completion, emit:

For format: human (default):

══════════════════════════════════════════════
VIP CUSTOMER REPORT
  Customers scanned:    
  Eligible (≥ min):     
  VIPs (top %):    
  Threshold spend:      $
  Threshold orders:     
  Customers tagged:       (or "skipped — dry_run")

  Top 10 VIPs by :
      Spend: $  Orders:   Last: 
  Output: vip_customers_.csv
══════════════════════════════════════════════

For format: json, emit:

{
  "skill": "vip-customer-identifier",
  "store": "",
  "dry_run": true,
  "rank_by": "spend",
  "outcome": {
    "customers_scanned": 0,
    "eligible": 0,
    "vips_identified": 0,
    "threshold_spend": 0,
    "customers_tagged": 0,
    "errors": 0,
    "output_file": "vip_customers_.csv"
  }
}

Output Format

CSV file vip_customers_.csv with columns: customer_id, name, email, lifetime_spend, currency, orders_count, last_order_date, composite_score, rank, tag_applied

Error Handling

| Error | Cause | Recovery | |-------|-------|----------| | THROTTLED | API rate limit exceeded | Wait 2 seconds, retry up to 3 times | | userErrors on customerUpdate | Customer not found or tag conflict | Log, skip, continue | | Fewer eligible than top_pct% | Small customer base | Lower min_orders/min_spend | | Multi-currency stores | currencyCode varies | Convert via shop default before ranking |

Best Practices

• Use rank_by: both to balance whales with loyalists — pure spend ranking can over-index on one-time large purchases.
• Re-run quarterly with a date-stamped tag (e.g., vip-2026-Q2) so lapsed VIPs roll off rather than accumulating permanently.
• Pair with customer-win-back — VIPs who become inactive should be flagged for high-priority re-engagement.
• Run with dry_run: true first; review the threshold spend value to confirm the cutoff matches your VIP definition.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: 40RTY-ai
• Source: 40RTY-ai/shopify-admin-skills
• License: MIT
• Homepage: http://skills.40rty.ai

Install and usage instructions live in the source repository linked above.