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-categorizationgit 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-categorization)<a href="https://agentmods.dev/skills/finsilabs/awesome-ecommerce-skills/product-categorization"><img src="https://agentmods.dev/badge/skills/finsilabs/awesome-ecommerce-skills/product-categorization/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-categorization"><img src="https://agentmods.dev/badge/skills/finsilabs/awesome-ecommerce-skills/product-categorization.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 | $0.00026 | $0.02807 |
| Opus 5 | $0.00013 | $0.01404 |
| Sonnet 5 | $0.00005 | $0.00561 |
| Haiku 4.5 | $0.00003 | $0.00281 |
Grade A, and why
product-categorization 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 11d 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 — 283 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Categorization
Overview
A well-structured product taxonomy makes products findable and drives SEO through category pages. Every platform handles categorization differently: Shopify uses Collections and tags, WooCommerce uses hierarchical Categories and tags, and BigCommerce uses Categories with subcategories. Understanding the platform's native model and configuring it correctly is more impactful than building a custom taxonomy system.
When to Use This Skill
- When building a product catalog that needs a navigable category tree (clothing > women > dresses)
- When migrating a flat product list into a structured taxonomy
- When breadcrumb navigation is missing or generating wrong paths
- When category pages need SEO-optimized URLs and meta tags
- When ingesting large supplier catalogs that need automatic categorization
Core Instructions
Step 1: Understand how your platform handles categories
| Platform | Category Model | URL Structure |
|---|---|---|
| Shopify | Collections (flat or nested via themes); tags for additional filtering | /collections/womens-dresses |
| WooCommerce | Hierarchical categories (parent/child) + tags | /product-category/clothing/womens/dresses/ |
| BigCommerce | Nested categories (unlimited depth) | /clothing/womens/dresses/ |
| Custom / Headless | Build with materialized paths for efficient breadcrumb queries | /c/clothing/women/dresses |
Choose a depth strategy before building:
- 2–3 levels is optimal for most stores (e.g., Clothing → Womens → Dresses)
- 4 levels maximum — deeper hierarchies confuse shoppers and dilute SEO
- Flat + tags works well for small catalogs (under 200 products)
Step 2: Platform-specific setup
Shopify
Shopify uses Collections as the primary categorization mechanism. Collections can be manual (hand-curated) or automated (rules-based).
Creating a collection hierarchy:
- Go to Admin → Products → Collections → Create collection
- For the "Women's Dresses" example:
- Create a parent collection: "Clothing" (manual, for navigation menu)
- Create a child collection: "Women's Dresses" (automated, rule: tag contains
womens-dress)
- Set the collection's SEO fields: Title, Meta description, and URL handle
- Add a collection image and description — these improve SEO and conversion on the collection page
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/breadcrumb-query-and-category-move-trans/criteria.json 2.8 KB
- evals/breadcrumb-query-and-category-move-trans/task.md 2.9 KB
- evals/category-data-model-with-materialized-pa/criteria.json 2.7 KB
- evals/category-data-model-with-materialized-pa/task.md 1.9 KB
- evals/seo-meta-tags-and-ai-auto-categorization/criteria.json 3.0 KB
- evals/seo-meta-tags-and-ai-auto-categorization/task.md 4.0 KB
- tile.json 241 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.
- 11d ago First seen · 283 lines · 26 tokens per session scan A ba206324e4fe
product-categorization is a skill published in the GitHub repository finsilabs/awesome-ecommerce-skills (52 stars, last pushed 6mo ago), licensed MIT. It adds 26 tokens to every session and 2,807 once invoked, about $0.0001 per session on Opus 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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