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 kangise/ecommerce-ai-skills --skill ecom-inventorygit clone --depth 1 https://github.com/kangise/ecommerce-ai-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/kangise/ecommerce-ai-skills/ecom-inventory)<a href="https://agentmods.dev/skills/kangise/ecommerce-ai-skills/ecom-inventory"><img src="https://agentmods.dev/badge/skills/kangise/ecommerce-ai-skills/ecom-inventory/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/kangise/ecommerce-ai-skills/ecom-inventory"><img src="https://agentmods.dev/badge/skills/kangise/ecommerce-ai-skills/ecom-inventory.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00037 | $0.00331 |
| Opus 5 | $0.00018 | $0.00166 |
| Sonnet 5 | $0.00007 | $0.00066 |
| Haiku 4.5 | $0.00004 | $0.00033 |
Grade A, and why
ecom-inventory 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.
What it actually says
Inventory Skill
When to Use
Forecast inventory, set safety stock, and manage replenishment. Use for FBA inventory planning, demand forecasting, restock decisions, or multi-warehouse optimization.
Method
Step 1: Read Platform Constraints
Read references/constraints.md for platform-specific rules (character limits, byte constraints, format requirements).
Step 2: Review Boundaries
Read references/boundaries.md to know when this skill should NOT be used.
Step 3: Pick the Prompt
Pick the appropriate prompt from references/playbook.md for your scenario.
Step 4: Execute and Verify
Execute the prompt with your data. Use the <自检>/<self_check>/<セルフチェック> self-check block in each prompt to verify output quality before delivering results.
References
- Constraints — Platform rules and limits
- Playbook — Prompt collection
- Boundaries — When not to use
Templates
Copy-ready prompt templates (in assets/templates/):
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.
- assets/templates/template-1-replenishment-decision.md 2.3 KB
- assets/templates/template-2-safety-stock-calculation.md 2.0 KB
- assets/templates/template-3-new-product-first-batch.md 1.9 KB
- manifest.yaml 2.9 KB
- references/boundaries.md 1.6 KB
- references/constraints.md 6.2 KB
- references/playbook.md 26 KB
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 · 43 lines · 37 tokens per session scan A 880b90b11a0d
ecom-inventory is a skill published in the GitHub repository kangise/ecommerce-ai-skills (67 stars, last pushed 4d ago), licensed CC0-1.0. It adds 37 tokens to every session and 331 once invoked, about $0.0002 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.
Other skills, from other repositories
zach-product-research
A Sorftime-based product research skill for finding Amazon market opportunities and deciding whether a new product is worth pursuing. Sorftime is a market-research data source for Amazon sellers.
zach-seller-skill-creator
A Chinese-language guide for Amazon sellers who want to turn repeated work processes into reusable skills for an AI agent.
zach-listing-health-checker
An Amazon listing health checker that examines a product page as a shopper would see it, including visibility, price, seller, cart, delivery, category, rank, reviews, and search visibility.
zach-search-term-analyzer
An analyzer for Amazon Brand Analytics Top Search Terms reports, which show popular searches across Amazon and how clicks and conversions are distributed among products.
zach-search-term-report-analyzer
An Amazon Ads search-term report analyzer for Sponsored Products, Sponsored Brands, and Sponsored Display campaigns. It groups related search terms, measures results over 7, 14, and 30 days, and produces reports in several file formats.
amzscout-research
Amazon-seller research using AMZScout tools — analyze a product by ASIN, evaluate a niche / category, pull keyword & PPC data, and explore a brand's Amazon catalog. Use whenever the user asks whether an Amazon product is worth selling, to analyze a niche or its competition, find/discover products, check a listing's…