shopify-admin-product-data-completeness-score

Purpose

Calculates a data completeness score (0–100) for each active product based on the presence of key fields: description, images, SEO title, SEO description, variant weight, barcode, cost, and specified metafields. Produces a ranked list of products needing the most data work. Read-only — no mutations. Catalog health report in a single pass.

Prerequisites

Authenticated Shopify CLI session:

shopify store auth --store <domain> --scopes read_products

API scopes:
```
read_products
```

Parameters

Parameter	Type	Required	Default	Description
store	string	yes	—	Store domain (e.g., mystore.myshopify.com)
status_filter	string	no	active	Product status to score: `active` , `draft` , or `all`
required_metafields	array	no	[]	List of `namespace.key` metafields that are required (e.g., `["custom.material"]` )
format	string	no	human	Output format: `human` or `json`

Safety

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

Scoring Rubric

Field	Points
Description present (non-empty)	15
At least 1 image	15
SEO title present	10
SEO description present	10
At least 1 variant with barcode	10
At least 1 variant with cost	10
At least 1 variant with weight	10
All required metafields present	20 (split evenly)
Total	100

Workflow Steps

OPERATION:
```
products
```
— query Inputs:
```
query: "status:<status_filter>"
```
,
```
first: 250
```
, select all completeness fields, pagination cursor Expected output: Products with all scored fields; paginate until
```
hasNextPage: false
```
Score each product per rubric; rank ascending by score

GraphQL Operations

graphql

# products:query — validated against api_version 2025-01
query ProductCompleteness($query: String!, $after: String) {
  products(first: 250, after: $after, query: $query) {
    edges {
      node {
        id
        title
        handle
        descriptionHtml
        images(first: 1) {
          edges {
            node {
              id
            }
          }
        }
        seo {
          title
          description
        }
        variants(first: 10) {
          edges {
            node {
              id
              barcode
              weight
              inventoryItem {
                unitCost {
                  amount
                }
              }
            }
          }
        }
        metafields(first: 20) {
          edges {
            node {
              namespace
              key
              value
            }
          }
        }
      }
    }
    pageInfo {
      hasNextPage
      endCursor
    }
  }
}

Session Tracking

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

On start, emit:

╔══════════════════════════════════════════════╗
║  SKILL: Product Data Completeness Score      ║
║  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):

══════════════════════════════════════════════
PRODUCT DATA COMPLETENESS REPORT
  Products scored:  <n>
  Avg score:        <pct>/100
  Score < 50:       <n> products (need urgent attention)
  Score 50–79:      <n> products
  Score ≥ 80:       <n> products

  Lowest scoring products:
    "<title>"  Score: <n>/100  Missing: description, SEO title
  Output: completeness_<date>.csv
══════════════════════════════════════════════

For

format: json

, emit:

json

{
  "skill": "product-data-completeness-score",
  "store": "<domain>",
  "products_scored": 0,
  "avg_score": 0,
  "below_50_count": 0,
  "output_file": "completeness_<date>.csv"
}

Output Format

CSV file

completeness_<YYYY-MM-DD>.csv

with columns:

product_id

title

score

has_description

image_count

has_seo_title

has_seo_description

has_barcode

has_cost

has_weight

missing_metafields

Error Handling

Error	Cause	Recovery
`THROTTLED`	API rate limit exceeded	Wait 2 seconds, retry up to 3 times
No products match filter	Empty catalog or wrong filter	Exit with 0 results

Best Practices

Use this skill as a pre-launch gate — run before activating DRAFT products to ensure all required fields are filled.
Tune
```
required_metafields
```
to your store's specific needs (e.g.,
```
custom.material
```
for apparel,
```
custom.ingredients
```
for food).
A score below 50 typically means a product is missing foundational content (description or images) and should be deprioritized from launch until fixed.
Run monthly to track catalog quality trends over time; improvements after a content sprint should be visible in the average score.