Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add finsilabs/awesome-ecommerce-skills --skill product-data-modelinggit clone --depth 1 https://github.com/finsilabs/awesome-ecommerce-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/finsilabs/awesome-ecommerce-skills/product-data-modeling)<a href="https://agentmods.dev/skills/finsilabs/awesome-ecommerce-skills/product-data-modeling"><img src="https://agentmods.dev/badge/skills/finsilabs/awesome-ecommerce-skills/product-data-modeling/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/finsilabs/awesome-ecommerce-skills/product-data-modeling"><img src="https://agentmods.dev/badge/skills/finsilabs/awesome-ecommerce-skills/product-data-modeling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00027 | $0.03020 |
| Opus 5.5 | $0.00011 | $0.01208 |
| Sonnet 5.5 | $0.00005 | $0.00604 |
| Haiku 4.5 | $0.00003 | $0.00302 |
Grade A, and why
product-data-modeling scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 274 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Data Modeling
Overview
Every platform has its own product data model — Shopify uses products with variants and metafields, WooCommerce uses products with attributes and custom fields, and BigCommerce uses products with options and custom fields. Understanding your platform's model and fitting your catalog into it correctly prevents data quality problems and import failures. Only build a custom data model if you're building a headless storefront from scratch.
When to Use This Skill
- When designing a product catalog structure for a new store on an existing platform
- When adding variant support (size, color, material) to existing products
- When implementing custom attributes for faceted filtering
- When modeling product relationships (bundles, cross-sells, accessories)
- When importing products from a PIM or ERP into the platform's model
Core Instructions
Step 1: Understand your platform's core data model
| Platform | Product | Variants | Custom Attributes | Relationships |
|---|---|---|---|---|
| Shopify | Product + up to 3 Options, up to 100 Variants | Per-variant: price, SKU, inventory, weight, image | Metafields (standard or custom namespaces) | Collections, cross-sell via apps |
| WooCommerce | Product (Simple, Variable, Grouped, External) | Per-variation: price, SKU, stock, attributes | Custom product attributes + WooCommerce custom fields | Upsells, cross-sells (built-in), grouped products |
| BigCommerce | Product with Options and Option Sets | Per-variant (modifier/option combination): price, SKU, stock | Custom fields per product | Related products, bundled products |
| Custom / Headless | Design from scratch with PostgreSQL/MongoDB | Full control over schema | EAV or JSONB for flexible attributes | Junction tables for relationships |
Step 2: Platform-specific modeling
Shopify
Core structure:
- Product: title, description, vendor, product_type, tags, images
- Options: up to 3 (e.g., Size, Color, Material) — defines the axes of variation
- Variants: one per combination of option values — each has its own price, SKU, inventory, weight
- Metafields: custom data per product or variant (e.g., care instructions, sizing guide URL, technical specs)
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- evals/core-product-variant-schema-design/criteria.json 3.1 KB
- evals/core-product-variant-schema-design/task.md 1.7 KB
- evals/eav-pattern-and-faceted-filtering/criteria.json 3.3 KB
- evals/eav-pattern-and-faceted-filtering/task.md 2.1 KB
- evals/relationships-collections-multi-tenant-p/criteria.json 3.5 KB
- evals/relationships-collections-multi-tenant-p/task.md 1.8 KB
- tile.json 244 B
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 274 lines · 27 tokens per session scan A c88a6016e10b
product-data-modeling is a skill published in the GitHub repository finsilabs/awesome-ecommerce-skills (60 stars, last pushed 8d ago), licensed MIT. It adds 27 tokens to every session and 3,020 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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