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 merchandising-rulesgit 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/merchandising-rules)<a href="https://agentmods.dev/skills/finsilabs/awesome-ecommerce-skills/merchandising-rules"><img src="https://agentmods.dev/badge/skills/finsilabs/awesome-ecommerce-skills/merchandising-rules/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/merchandising-rules"><img src="https://agentmods.dev/badge/skills/finsilabs/awesome-ecommerce-skills/merchandising-rules.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 | $0.02870 |
| Opus 5 | $0.00013 | $0.01435 |
| Sonnet 5 | $0.00005 | $0.00574 |
| Haiku 4.5 | $0.00003 | $0.00287 |
Grade A, and why
merchandising-rules 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 12d 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 — 230 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Merchandising Rules
Overview
Merchandising rules control which products appear first in your collections and search results. The goal is to surface products that are likely to convert — in-stock, popular, high-margin — while giving merchandisers manual control to pin hero products, hide out-of-stock items, and boost seasonal collections. Every major platform has some built-in sorting options; apps like Searchpie, Intelligems, or SearchPie add automated performance-based ranking.
When to Use This Skill
- When building product ranking logic for collection and category pages
- When creating automated (smart) collections based on product attributes, tags, or performance
- When implementing search result boosting and burying for merchandising control
- When adding pinning (manual placement) and slot-based merchandising to collection pages
- When measuring the revenue impact of different product ranking strategies
Core Instructions
Step 1: Determine your platform and choose the right merchandising tool
| Platform | Recommended Tool | Why |
|---|---|---|
| Shopify | Shopify's built-in collection sorting + Kimonix or SearchPie | Shopify has built-in sort options; Kimonix and SearchPie add performance-based automated sorting with manual override capability |
| WooCommerce | WooCommerce's default sort + YITH WooCommerce Catalog Mode or WooCommerce Product Table | WooCommerce supports basic sorting; YITH and similar plugins add advanced catalog control |
| BigCommerce | Built-in Collection Sorting + SearchPie or Boost Commerce | BigCommerce has strong built-in category sorting; Boost Commerce adds advanced search merchandising |
| Custom / Headless | Algolia or Elasticsearch with a merchandising rules layer | Algolia has a built-in "Rules" and "Pinning" feature in its dashboard; Elasticsearch needs custom scoring rules |
Step 2: Configure basic collection sorting
Shopify
Built-in sorting options (no app needed):
- Go to Products → Collections → [Collection] → Products
- From the "Sort" dropdown, choose:
- Best Selling — sorts by total units sold historically (most popular first)
- Newest — most recently added products first
- Price (Low to High / High to Low) — price-based sorting
- Manually — lets you drag products to specific positions
- The "Manual" sort lets you pin specific products by dragging them to the top — useful for hero products and new launches
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/collection-ranking-pipeline/criteria.json 2.7 KB
- evals/collection-ranking-pipeline/task.md 1.9 KB
- evals/product-scoring-engine/criteria.json 2.3 KB
- evals/product-scoring-engine/task.md 1.7 KB
- evals/search-boosting-admin-audit/criteria.json 2.4 KB
- evals/search-boosting-admin-audit/task.md 1.8 KB
- tile.json 238 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.
- 12d ago First seen · 230 lines · 26 tokens per session scan A 5cd06ecf03bf
merchandising-rules 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 2,870 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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