/shopify-admin-product-affinity-cross-sell

name: shopify-admin-product-affinity-cross-sell
role: order-intelligence
description: "Mine order history to find which products are most frequently bought together, then rank pairs by support, confidence, and lift to power bundles and cross-sell recommendations."
toolkit: shopify-admin, shopify-admin-execution
api_version: "2025-01"
graphql_operations:
  - orders:query
status: stable
compatibility: Claude Code, Cursor, Codex, Gemini CLI

Purpose

Applies market basket analysis to your order history to surface product pairs that customers naturally buy together. For every co-purchased pair it calculates **support** (how often the pair appears), **confidence** (given product A, how likely is B?), and **lift** (how much more likely than chance). The output is actionable input for product bundles, "frequently bought together" widgets, cross-sell email flows, and homepage recommendations. Read-only — no mutations are executed.

Prerequisites

• Authenticated Shopify CLI session: `shopify auth login --store <domain>`
• API scopes: `read_orders`, `read_products` (validator-confirmed: line item `product` field traverses the product graph)

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., `2025-03-31`) | | min_support | integer | no | 5 | Minimum number of orders a pair must co-appear in to be included | | min_confidence | float | no | 0.1 | Minimum P(B\|A) threshold (0–1) | | min_lift | float | no | 1.0 | Only include pairs where lift > this value (> 1 means non-random) | | top_n | integer | no | 20 | Number of top pairs to show in the ranked output | | sort_by | string | no | lift | Ranking metric: `lift`, `confidence`, or `support` | | exclude_tags | string | no | — | Comma-separated product tags to exclude (e.g., `gift-wrap,donation`) |

Workflow Steps

1. **OPERATION:** `orders` — query **Inputs:** `first: 250`, `query: "created_at:>='<date_range_start>' created_at:<='<date_range_end>'"`, pagination cursor; select `lineItems` with `product { id, title }` and `quantity`; skip orders with a single line item **Expected output:** All multi-item orders in range; paginate until `hasNextPage: false`; build a product frequency map (`product_id → order_count`) and a pair frequency map (`(product_a_id, product_b_id) → co_occurrence_count`)

2. **In-memory analysis:**

• For each order with ≥ 2 distinct products, enumerate every unique unordered pair and increment the pair counter
• Compute metrics for each pair that meets `min_support`:
• **Support** = `pair_count / total_orders`
• **Confidence A→B** = `pair_count / count(orders containing A)`
• **Confidence B→A** = `pair_count / count(orders containing B)`
• **Lift** = `support / (P(A) × P(B))`
• Filter by `min_confidence` and `min_lift`; sort by `sort_by`; truncate to `top_n`

GraphQL Operations

# orders:query (multi-item basket analysis) — validated against api_version 2025-01
query OrdersForAffinityAnalysis($first: Int!, $after: String, $query: String) {
  orders(first: $first, after: $after, query: $query) {
    edges {
      node {
        id
        createdAt
        lineItems(first: 50) {
          edges {
            node {
              quantity
              product {
                id
                title
                tags
              }
            }
          }
        }
      }
    }
    pageInfo {
      hasNextPage
      endCursor
    }
  }
}

Session Tracking

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

**On start**, emit:

╔══════════════════════════════════════════════╗
║  SKILL: product-affinity-cross-sell          ║
║  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
  Orders analysed:      <n>
  Unique products:      <n>
  Pairs evaluated:      <n>
  Pairs above threshold:<n>
  Date range:           <start> to <end>
  Sort by:              <lift|confidence|support>
  Errors:               0
  Output:               product_affinity_<date>.csv
══════════════════════════════════════════════

Followed by an inline ranked table of the top `top_n` pairs:

| Rank | Product A | Product B | Support | Conf A→B | Conf B→A | Lift | |------|-----------|-----------|---------|----------|----------|------| | 1 | ... | ... | ... | ...% | ...% | ... |

For `format: json`, emit:

{
  "skill": "product-affinity-cross-sell",
  "store": "<domain>",
  "started_at": "<ISO8601>",
  "completed_at": "<ISO8601>",
  "dry_run": false,
  "steps": [
    { "step": 1, "operation": "OrdersForAffinityAnalysis", "type": "query", "params_summary": "<date_range_start> to <date_range_end>", "result_summary": "<n> orders, <n> multi-item baskets", "skipped": false }
  ],
  "outcome": {
    "orders_analysed": 0,
    "unique_products": 0,
    "pairs_evaluated": 0,
    "pairs_above_threshold": 0,
    "date_range_start": "<date_range_start>",
    "date_range_end": "<date_range_end>",
    "sort_by": "lift",
    "results": [],
    "errors": 0,
    "output_file": "product_affinity_<date>.csv"
  }
}

Output Format

CSV file `product_affinity_<YYYY-MM-DD>.csv` with one row per qualifying pair:

| Column | Description | |-