price-optimization-tool
Evaluate ecommerce price candidates using unit economics, historical observations, elasticity analysis, scenario modeling, and controlled experiments. Use when a seller asks what price to test, how price changes could affect contribution or revenue, how to estimate elasticity, how to optimize a bundle or tier, or how to design a price experiment across Amazon, Shopify, TikTok Shop, Walmart, eBay, or other channels. Do not claim a proven optimal price without sufficient clean data, and do not change live prices without explicit authorization.
npx skills add nexscope-ai/eCommerce-Skills --skill price-optimization-tool --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 ```bash 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 ```text Evaluate these five price candidates using my cost and sales history. ``` ```text Can this dataset support a price-elasticity estimate, and what should I test next? ``` ```text Build a price experiment for my top five Shopify SKUs without misleading customers. ``
- Installation
- Capabilities
- Usage Examples
- Inputs and Collection
- Workflow
- 1. Define the Decision and Evidence Boundary
- 2. Audit and Align the Data
- 3. Calculate Unit Economics and Constraints
- 4. Assess Whether Elasticity Is Estimable
- 5. Model Candidate Prices
- 6. Select the Decision Path
- 7. Design a Controlled Price Test
- 8. Roll Out and Monitor
- Domain Rules
npx skills add nexscope-ai/eCommerce-Skills --skill price-optimization-tool -g
What does the price-optimization-tool skill do?
Evaluate ecommerce price candidates using unit economics, historical observations, elasticity analysis, scenario modeling, and controlled experiments. Use when a seller asks what price to test, how price changes could affect contribution or revenue, how to estimate elasticity, how to optimize a bundle or tier, or how to design a price experiment across Amazon, Shopify, TikTok Shop, Walmart, eBay, or other channels. Do not claim a proven optimal price without sufficient clean data, and do not change live prices without explicit authorization.
How do I install it?
Run `npx skills add nexscope-ai/eCommerce-Skills --skill price-optimization-tool --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From nexscope-ai/eCommerce-Skills, a repository with 580 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.