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 price-optimization-toolgit 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/price-optimization-tool)<a href="https://agentmods.dev/skills/nexscope-ai/ecommerce-skills/price-optimization-tool"><img src="https://agentmods.dev/badge/skills/nexscope-ai/ecommerce-skills/price-optimization-tool/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/nexscope-ai/ecommerce-skills/price-optimization-tool"><img src="https://agentmods.dev/badge/skills/nexscope-ai/ecommerce-skills/price-optimization-tool.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, 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 MCP Rug Pull · line 13 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 | $0.00105 | $0.02619 |
| Opus 5 | $0.00053 | $0.01309 |
| Sonnet 5 | $0.00021 | $0.00524 |
| Haiku 4.5 | $0.00011 | $0.00262 |
Grade A, and why
price-optimization-tool 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 8d 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 — 246 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Price Optimization Tool
Build an evidence-bounded price decision from seller economics and observed behavior, then recommend a reversible test or rollout with explicit uncertainty.
Installation
npx skills add nexscope-ai/eCommerce-Skills --skill price-optimization-tool -g
Capabilities
- Audit price, demand, traffic, promotion, cost, and inventory data for comparability.
- Calculate contribution economics and hard candidate-price constraints.
- Estimate directional or numeric elasticity only when the evidence supports it.
- Compare price candidates under base, downside, and upside demand scenarios.
- Optimize regular, promotional, bundle, quantity, and good-better-best price candidates.
- Design controlled price tests with hypotheses, guardrails, confounder controls, and decision rules.
- Produce a recommendation with uncertainty, approval requirements, and a reversible rollout.
Usage Examples
Evaluate these five price candidates using my cost and sales history.
Can this dataset support a price-elasticity estimate, and what should I test next?
Build a price experiment for my top five Shopify SKUs without misleading customers.
Compare separate-item, bundle, and quantity-tier pricing for these products.
Inputs and Collection
Use seller-supplied and inspected evidence first. Collect:
- SKU, variant, channel, market, currency, tax treatment, fulfillment method, lifecycle stage, and business objective;
- timestamped regular price, realized selling price, list or compare-at price, coupons, promotions, and seller-funded discounts;
- timestamped sessions or impressions, orders, units, net revenue, cancellations, returns, and inventory availability;
- COGS, inbound freight, duties, packaging, fulfillment, payment, referral, affiliate, ad, return, and other variable costs;
- traffic source, ad spend, content or listing changes, stock status, seasonality, events, and promotion windows;
- comparable competitor offers with source, capture time, variant, pack size, availability, shipping, seller, and fulfillment;
- bundle components, attach rates, cannibalization risks, tier thresholds, and operational constraints;
- target metric, approved floor and ceiling, test duration constraints, platform rules, approver, and risk tolerance.
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.
- 8d ago First seen · 246 lines · 105 tokens per session scan A ad2d4d74100c
price-optimization-tool is a skill published in the GitHub repository nexscope-ai/eCommerce-Skills (908 stars, last pushed 16d ago), licensed MIT. It adds 105 tokens to every session and 2,619 once invoked, about $0.0005 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.
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