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commerce-data-model

Design and audit the core e-commerce domain model — products, variants, SKUs, options, collections, attributes, taxonomy, faceted search and pagination. Language- and platform-agnostic. Triggers: modeling a catalog, reviewing an existing product schema, designing filtering/search.

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SKILL.md (original)

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Commerce Data Model

Design the backbone of any storefront: a product catalog model that survives variants, bundles, localization, and faceted search — independent of language, framework, or database.

Note: This skill may create or modify files in your project (a model spec or schema doc). It will ask before writing.

Before Starting

Gather: business context (physical/digital/services, single vs multi-vendor, B2C/B2B), scale (SKU count, variant explosion, locales/currencies), and goal (model from scratch, audit an existing schema, or design taxonomy + filtering).

How This Skill Works

ModeTriggerOutput
ModelmodelEntity model spec: Product, Variant, SKU, Option, Collection, Attribute
AuditauditGap analysis of an existing schema against commerce best practices
TaxonomytaxonomyCategory tree, attribute schema, faceted-search and pagination design

Phase 1 — Core Entities

The canonical separation that prevents 90% of catalog pain:

EntityIsIs NOTKey fields
ProductThe marketing/SEO unit ("Acme Tee")A buyable thingtitle, description, slug, status, brand
VariantA buyable combination ("Tee / Red / M")A categoryoption values, price, weight, barcode
SKUThe stock-keeping identifierA variant's pricecode, variant_id
OptionAn axis of variation (Color, Size)A valuename, position
CollectionA merchandising groupingA taxonomy nodetitle, rules (manual/automatic)

Rule: Price, inventory, and barcode live on the Variant, never the Product. A product with no options still has exactly one variant.

For the full field-level spec, identifiers (GTIN/MPN), bundles/kits, and digital vs physical modeling, see references/data-model-spec.md.


Phase 2 — Attributes & Taxonomy

  • Taxonomy (category tree): hierarchical, navigational, one primary path per product. Stable URLs.
  • Collections: many-to-many merchandising overlays (manual or rule-based). A product lives in one category branch but many collections.
  • Attributes: typed key-values (material: cotton, sleeve: short). Drive facets. Define a controlled vocabulary — free-text attributes break filtering.

Attribute typing: text | enum | number | boolean | measurement. Enums must reference a value list. Measurements carry a unit.


Phase 3 — Faceted Search & Pagination

  • Facets come from enum/boolean/measurement attributes, not free text.
  • Multi-select within a facet = OR; across facets = AND.
  • Always show counts; never show zero-result facet values.
  • Pagination: prefer cursor/keyset over offset for deep catalogs (offset degrades and double-counts under writes). Cap page size.

Output Artifacts

RequestOutput
Model from scratchEntity spec + ER sketch + identifier strategy
Audit schemaVerdict per entity: COMPLIANT / CONCERNS, with fixes
Design taxonomyCategory tree + attribute schema + facet plan

A schema is COMPLIANT when price/inventory sit on variants, every product has ≥1 variant, attributes are typed against vocabularies, and URLs are stable.


Related Skills

  • storefront-architecture: consumes this model to design PLP/PDP pages. Next step after modeling.
  • merchant-feed: maps these entities to Google/Meta feed attributes.
  • inventory-stock: owns stock levels that hang off the SKU/variant.
  • order-lifecycle: references variants as order line items.

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