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-analyticsgit 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-analytics)<a href="https://agentmods.dev/skills/finsilabs/awesome-ecommerce-skills/product-analytics"><img src="https://agentmods.dev/badge/skills/finsilabs/awesome-ecommerce-skills/product-analytics/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-analytics"><img src="https://agentmods.dev/badge/skills/finsilabs/awesome-ecommerce-skills/product-analytics.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 | $0.02806 |
| Opus 5 | $0.00013 | $0.01403 |
| Sonnet 5 | $0.00005 | $0.00561 |
| Haiku 4.5 | $0.00003 | $0.00281 |
Grade A, and why
product-analytics 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 — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Analytics
Overview
Product analytics reveals which products drive revenue, which are overstocked, and which product pages are losing shoppers before they add to cart. The core analyses — sell-through rate, dead stock identification, PDP conversion funnel, and category performance — give your buying and merchandising team the data they need to make confident reorder, markdown, and catalog decisions.
This skill guides you through running these analyses using your platform's built-in tools and dedicated apps, without building custom data pipelines.
When to Use This Skill
- When the buying team needs a weekly sell-through report to decide on reorders and markdowns
- When building a product performance dashboard for merchandisers
- When identifying dead stock that ties up capital
- When measuring which products have high views but low add-to-cart rates
- When ranking products for collection page sorting based on performance data
- When generating a catalog health report before a seasonal reset
Core Instructions
Step 1: Choose your product analytics tool by platform
| Platform | Tool | What It Provides |
|---|---|---|
| Shopify | Shopify Analytics (built-in) | Product-level revenue, units sold, sell-through (if cost entered); free |
| Shopify | Inventory Planner (App Store) | Sell-through rates, days of supply, reorder recommendations, dead stock alerts |
| Shopify | Google Analytics 4 (via Shopify's GA4 integration) | PDP views, add-to-cart rate, checkout funnel by product |
| WooCommerce | WooCommerce Analytics (built-in) | Product revenue, units sold, orders by product; free |
| WooCommerce | Metorik | Advanced product analytics including sell-through, cohort analysis by product, and dead stock reports |
| BigCommerce | BigCommerce Analytics → Merchandising (built-in) | Product revenue, units sold, and conversion rate by product |
| BigCommerce | Glew.io (App Marketplace) | Advanced sell-through, dead stock, and product lifecycle analytics |
| All platforms | Google Analytics 4 + enhanced ecommerce | Views-to-cart-to-purchase funnel by product; requires GA4 setup with ecommerce tracking |
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/dead-stock-detection-and-markdown-recomm/criteria.json 2.8 KB
- evals/dead-stock-detection-and-markdown-recomm/task.md 4.1 KB
- evals/merchandising-health-score-calculation/criteria.json 2.6 KB
- evals/merchandising-health-score-calculation/task.md 3.4 KB
- evals/pdp-funnel-conversion-analysis/criteria.json 2.5 KB
- evals/pdp-funnel-conversion-analysis/task.md 2.2 KB
- tile.json 234 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 · 216 lines · 26 tokens per session scan A 8c67bb9991e6
product-analytics 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 2,806 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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