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-applicabilitygit 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-applicability)<a href="https://agentmods.dev/skills/kangise/ecommerce-ai-skills/ecom-applicability"><img src="https://agentmods.dev/badge/skills/kangise/ecommerce-ai-skills/ecom-applicability/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-applicability"><img src="https://agentmods.dev/badge/skills/kangise/ecommerce-ai-skills/ecom-applicability.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 | $0.00053 | $0.01068 |
| Opus 5 | $0.00026 | $0.00534 |
| Sonnet 5 | $0.00011 | $0.00214 |
| Haiku 4.5 | $0.00005 | $0.00107 |
Grade A, and why
ecom-applicability 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 — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Applicability Skill
Determine whether AI is appropriate for a specific e-commerce task — answers "should I use AI for X?" with boundary-aware reasoning instead of a generic yes/no.
When to Use
- The user asks "should I use AI for X?", "can AI do X?", or "is it worth automating X?".
- The user is deciding between AI, manual work, a script, a SaaS tool, or a workflow engine.
- The user is about to invest in an AI tool, agent, or pipeline and needs a feasibility/ROI sanity check.
Routing Method
Step 1 — Identify the Domain Chapter
Map the question to the matching chapter in references/boundaries.md (57 chapters, grouped by path):
| If the question is about… | Look up |
|---|---|
| Foundations: prompt quality, RAG, agents, RPA, tool choice | ## Path 0 · Foundations |
| Listing, ads, customer service, inventory, pricing, SEO, visual content, compliance, research, brand, finance, growth, operations agent | ## Path A · Operators |
| Data pipeline, prediction, RAG systems, agents, local models, review NLP, dashboards, image pipelines | ## Path B · Developers |
| AI assessment, team upskilling, ROI, risk governance, competitive intel | ## Path C · Managers |
| A specific marketplace (Amazon, Walmart, Temu, Shopify, TikTok Shop, eBay, etc.) or cross-platform strategy | ## Path D · Platforms |
| A social channel (Meta, YouTube, 小红书, Pinterest, WhatsApp, Reddit) or cross-channel strategy | ## Path E · Social Media |
Not sure? Grep boundaries.md for the domain keyword (e.g. pricing, RAG, Temu) — each chapter's Source: line gives the exact book file.
Step 2 — Check Boundary Conditions
Read that chapter's entry. Every entry is a list of "this doesn't work when…" bullets. Evaluate the user's situation against each bullet using the three-part decision rule:
- Data sufficiency — is there enough real data? (e.g. ≥1 year of sales history for inventory models, ≥hundreds of reviews for review NLP, real search-term data instead of guessed keywords). If the data is missing, estimated, or polluted, the AI cannot produce a trustworthy answer.
- Tool availability — does the tool/API exist and fit the constraints? (e.g. platform API instead of fragile RPA, local model when data cannot leave the network, official API instead of scraping). If the tool doesn't exist or the constraint blocks it, the whole approach is off the table.
- Risk / reward — what is the cost of being wrong, and can it be reversed? (e.g. irreversible actions like auto-pricing, auto-orders, or legal filings must have human confirmation; low-frequency tasks may not repay automation cost).
What ships with it
4 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.
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 · 60 lines · 53 tokens per session scan A a6816a625d5c
ecom-applicability is a skill published in the GitHub repository kangise/ecommerce-ai-skills (67 stars, last pushed 4d ago), licensed CC0-1.0. It adds 53 tokens to every session and 1,068 once invoked, about $0.0003 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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