Frequently Bought Together

Purpose

Analyzes order history to discover which products are frequently purchased together. Calculates co-occurrence frequency, lift scores, and confidence metrics to generate data-driven cross-sell recommendations and bundle candidates. Read-only — no mutations.

Prerequisites

Parameters

Safety

> ℹ️ Read-only skill — no mutations are executed. Safe to run at any time.

Workflow Steps

Inputs: query: "created_at:>=''", first: 250, select lineItems { product { id, title } }, pagination cursor

Expected output: All orders with product-level line items

Inputs: Product IDs from top pairs for titles, images, prices

Expected output: Product details for display

GraphQL Operations

# orders:query — validated against api_version 2025-01
query OrderLineItems($query: String!, $after: String) {
  orders(first: 250, after: $after, query: $query) {
    edges {
      node {
        id
        lineItems(first: 50) {
          edges {
            node {
              product { id title }
              quantity
            }
          }
        }
      }
    }
    pageInfo { hasNextPage endCursor }
  }
}

# products:query — validated against api_version 2025-01
query ProductDetails($ids: [ID!]!) {
  nodes(ids: $ids) {
    ... on Product {
      id
      title
      vendor
      productType
      priceRangeV2 {
        minVariantPrice { amount currencyCode }
        maxVariantPrice { amount currencyCode }
      }
      totalInventory
      status
    }
  }
}

Session Tracking

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

On start, emit:

╔══════════════════════════════════════════════╗
║  SKILL: Frequently Bought Together           ║
║  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):

══════════════════════════════════════════════
FREQUENTLY BOUGHT TOGETHER  (<days_back> days, <n> orders analyzed)
  Unique product pairs found: <n>
  Pairs meeting min_support:  <n>

  TOP PAIRS BY LIFT:
  #1  "<product A>" + "<product B>"
      Support: <n> orders  Lift: <n>x  Confidence: <pct>%

  #2  "<product A>" + "<product B>"
      Support: <n> orders  Lift: <n>x  Confidence: <pct>%

  BUNDLE CANDIDATES (high support + high lift):
    "<product A>" + "<product B>"  →  Suggested bundle price: $<n>

  Output: fbt_pairs_<date>.csv
══════════════════════════════════════════════

Output Format

CSV file fbt_pairs_.csv with columns:

product_a_id, product_a_title, product_b_id, product_b_title, support, confidence_a_to_b, confidence_b_to_a, lift, combined_avg_price

Error Handling

Best Practices