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 nexscope-ai/eCommerce-Skills --skill warehouse-optimizationgit clone --depth 1 https://github.com/nexscope-ai/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/nexscope-ai/ecommerce-skills/warehouse-optimization)<a href="https://agentmods.dev/skills/nexscope-ai/ecommerce-skills/warehouse-optimization"><img src="https://agentmods.dev/badge/skills/nexscope-ai/ecommerce-skills/warehouse-optimization.svg" alt="Measured on agentmods" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
- medium MCP Rug Pull · line 14 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00132 | $0.03482 |
| Opus 5 | $0.00066 | $0.01741 |
| Sonnet 5 | $0.00026 | $0.00696 |
| Haiku 4.5 | $0.00013 | $0.00348 |
Grade A, and why
warehouse-optimization 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 4d 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 — 447 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Warehouse & Inventory Optimization 🏭
Diagnose and optimize your warehouse operations: analyze inventory health, calculate safety stock, reduce costs, and improve efficiency. No API key required.
Installation
npx skills add nexscope-ai/eCommerce-Skills --skill warehouse-optimization -g
Supported Fulfillment Models
| Model | Platform | Optimization Focus |
|---|---|---|
| Self-Fulfillment | Any | Warehouse layout, staffing, pick/pack efficiency, storage costs |
| Amazon FBA | Amazon | IPI score, storage fees, aged inventory, restock limits |
| Amazon FBM | Amazon | Shipping speed, Prime eligibility, cost vs FBA |
| Walmart WFS | Walmart | Fulfillment fees, storage limits, Pro Seller status |
| 3PL | Multi-channel | Provider costs, SLAs, contract optimization, hidden fees |
| Shopify Fulfillment Network | Shopify | Distributed inventory, delivery speed, cost analysis |
| TikTok Shop Fulfillment | TikTok | TikTok-specific requirements, shipping SLAs |
| Dropshipping | Any | Supplier reliability, lead times, stockout prevention |
| Hybrid | Multi-channel | Inventory allocation, channel balancing, split strategy |
Usage Examples
Audit my warehouse operations. I'm self-fulfilling from a 2,000 sq ft warehouse.
500 SKUs, 3,000 orders/month. Main issues: frequent stockouts on top sellers,
high storage costs on slow movers. Help me optimize.
I use FBA for my Amazon store. IPI score dropped to 350. I have excess inventory
warnings on 40 SKUs. How do I fix this before I get storage limits?
Running FBM for my oversized products and FBA for standard. 200 orders/day total.
Which SKUs should I move to FBA vs keep FBM? Help me optimize the split.
Using ShipBob as my 3PL. Monthly bill is $8,500 for 2,000 orders. Is this competitive?
What should I negotiate or consider switching?
First Interaction
When user first asks about warehouse optimization, inventory management, or fulfillment efficiency, greet them with:
What ships with it
1 file 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.
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.
- 4d ago First seen · 447 lines · 132 tokens per session scan A 18aa38e15cce
warehouse-optimization is a skill published in the GitHub repository nexscope-ai/eCommerce-Skills (875 stars, last pushed 12d ago), licensed MIT. It adds 132 tokens to every session and 3,482 once invoked, about $0.0007 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 2026-09-03.
Other skills, from other repositories
buyer-intent-writer
Write or rewrite ecommerce product descriptions, bullets, buying-guide sections, and landing-page copy around verified buyer needs, objections, comparisons, and purchase intent. Use when the user asks for buyer-intent copy, product copy, conversion copy, or a clearer product description. Do not use for technical…
competitor-seo-analyzer
Compare observable ecommerce competitor SEO, content, structured-data, trust, and buyer-intent signals. Use when the user asks for competitor SEO analysis, an SEO comparison, content gaps, or a comparison of supplied pages, listings, exports, or search evidence. Do not infer live rankings, traffic, backlinks, or AI…
ecommerce-geo-auditor
Diagnose an ecommerce page or listing for GEO, AI-search, AI-citation, and AI-shopping readiness using inspected page evidence, supplied content, or crawl data. Use when the user asks for a GEO audit, AI visibility readiness check, AI search audit, citation-readiness review, or why a product is hard for AI systems to…
ecommerce-geo-optimizer
Turn verified ecommerce GEO, AI-search, or AI-citation readiness gaps into a prioritized implementation plan covering content, product facts, technical access, structured data, trust, and measurement. Use when the user asks to improve GEO, optimize for AI search, fix a GEO audit, or create an AI visibility readiness…
ecommerce-seo-auditor
Audit ecommerce websites, product pages, collection pages, and marketplace listings for technical SEO, crawlability, indexability, on-page relevance, content, internal linking, product data, images, and Core Web Vitals. Use when the user asks for an ecommerce SEO audit, store SEO checkup, product-page review…
keyword-gap-analyzer
Find ecommerce keyword, topic, and buyer-question gaps from supplied pages, competitor content, Search Console data, Keyword Planner exports, or connected research tools. Use when the user asks for keyword gaps, missing keywords, competitor keywords, content opportunities, or keyword-to-page planning. Do not invent…