Price Optimization Tool

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 3e6c611584c3 · 19 Oct 2026Report an issue or request a re-scan

• Publishernexscope-ai GitHub organizationOwner of the repository this skill is read from
• Source foldernexscope-ai/eCommerce-Skills/price-optimization-toolThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdict3e6c611584c3A new audit runs when the source changes
• Repository statusNot archivedLast push 54 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: 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…

---
name: price-optimization-tool
description: 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.
---

# 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.
```

```text
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.

If material inputs are missing, ask one consolidated follow-up. If they remain unavailable, provide a provisional candidate framework and test plan, not a fabricated optimal price.

## Workflow

### 1. Define the Decision and Evidence Boundary

State the SKU, market, channel, objective, candidate range, time horizon, and decision owner. List inspected sources and label inputs:

- **Confirmed:** supported by inspected evidence.
- **Assumption:** an explicit scenario input, not an observed fact.
- **Unknown:** missing information that blocks a calculation or conclusion.

Choose one primary objective, such as contribution dollars, contribution per visitor, cash recovery, revenue, sell-through, launch learning, or a constrained balance. Do not silently optimize revenue when the seller asked for profit, or units when inventory is limited.

### 2. Audit and Align the Data

Build a time-aligned dataset at the most reliable common granularity. Check:

- realized price rather than list price alone;
- seller-funded discount and promotion stacking;
- currency, tax, pack size, product version, channel, and market consistency;
- stockouts, suppressed listings, missing traffic, cancellations, and returns;
- changes in ads, traffic mix, content, reviews, fulfillment, competitors, and seasonality;
- sufficient observations and meaningful price variation.

Exclude or flag non-comparable periods. Do not interpret a price-demand correlation as causal when other material variables changed.

### 3. Calculate Unit Economics and Constraints

For each observed or candidate price:

```text
Net Revenue = Selling Price - Seller-Funded Discounts - Refund Allowance
Contribution $ = Net Revenue - COGS - Variable Selling Costs
Contribution % = Contribution $ / Net Revenue
```

When percentage fees apply to selling price:

```text
Price Floor = (Unit Cost + Fixed Variable Costs + Target Contribution $) / (1 - Variable Fee Rate)
```

Run base, high-return, high-ad-cost, fee-change, and promotion-stack scenarios. Keep gross margin, markup, contribution margin, and net profit distinct. Remove candidates that violate approved economics, legal or contractual constraints, platform rules, or customer-trust limits.

### 4. Assess Whether Elasticity Is Estimable

Use a numeric estimate only when there is sufficient clean price variation, comparable exposure, reliable quantity or conversion data, and manageable confounding. A simple midpoint diagnostic is:

```text
Price Elasticity = ((Q2 - Q1) / ((Q2 + Q1) / 2)) / ((P2 - P1) / ((P2 + P1) / 2))
```

Report the observation window, units, exclusions, uncertainty, and whether the result is descriptive or plausibly causal. Segment only when sample size and decision relevance justify it.

If evidence is weak:

- state that elasticity is not reliably estimable;
- use a range of explicitly labeled demand-response scenarios;
- recommend the smallest useful controlled test;
- never substitute an unverified category benchmark and call it product evidence.

### 5. Model Candidate Prices

Create a candid

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

npx skills add nexscope-ai/eCommerce-Skills --skill price-optimization-tool -g

git clone https://github.com/nexscope-ai/eCommerce-Skills
git -C eCommerce-Skills checkout 3e6c611584c3

Works in

1. 2026-10-19
   ✓SAFE B 89 / 100

   first audit · source 3e6c611584c3

Questions about Price Optimization Tool

• github.com/nexscope-ai/eCommerce-Skills/tree/main/price-optimiza — the audited source at commit 3e6c611584c3 and every finding aboveObserved · 19 Oct 2026

Spotted something wrong? Report a correction · Methodology