Dynamic Pricing Ecommerce

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/dynamic-pricing-ecommerceThe 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: Design a controlled dynamic-pricing or repricing system for ecommerce products. Use when a seller asks for demand-based, inventory-based, competitor-responsive, or time-based price rules; SKU eligibility; price floors…

---
name: dynamic-pricing-ecommerce
description: Design a controlled dynamic-pricing or repricing system for ecommerce products. Use when a seller asks for demand-based, inventory-based, competitor-responsive, or time-based price rules; SKU eligibility; price floors and ceilings; automation approvals; simulations; monitoring; or rollback plans across Amazon, Shopify, TikTok Shop, Walmart, eBay, or other channels. Do not use for a one-time optimal-price calculation or to change live prices without explicit authorization.
---

# Dynamic Pricing for Ecommerce

Turn seller-approved economics and trusted signals into a bounded repricing system with explicit rules, approvals, monitoring, and a kill switch.

## Installation

```bash
npx skills add nexscope-ai/eCommerce-Skills --skill dynamic-pricing-ecommerce -g
```

## Capabilities

- Define SKU eligibility for automatic, approval-required, or manual repricing.
- Calculate contribution-safe floors and commercially justified ceilings.
- Select demand, inventory, competitor, season, and promotion signals without treating noisy observations as facts.
- Create deterministic rule matrices with bounded price steps, cooldowns, and conflict precedence.
- Simulate normal, downside, promotion-stack, stockout, and price-war scenarios.
- Design approval, audit-log, rollback, anomaly-breaker, and emergency-stop controls.
- Produce a staged platform implementation and measurement plan without enabling live changes.

## Usage Examples

```text
Design safe Amazon repricing rules for these 200 SKUs without starting a price war.
```

```text
Create an inventory-aware dynamic pricing plan for my Shopify store.
```

```text
Which products can be auto-repriced, and which should always require approval?
```

```text
Audit these existing repricing rules for margin, promotion, and rollback risks.
```

## Inputs and Collection

Use seller-supplied and inspected evidence first. Collect:

- SKU, variant, channel, marketplace, currency, tax treatment, fulfillment method, and lifecycle stage;
- current price, realized selling price, list or compare-at price, coupons, promotions, bundles, and discount-combination rules;
- COGS, inbound freight, duties, packaging, fulfillment, payment, referral, affiliate, ad, return, and other variable costs;
- target contribution dollars or margin, approved floor, approved ceiling, and brand or MAP constraints;
- inventory on hand, inbound stock, sell-through, age, weeks of cover, replenishment lead time, and stockout risk;
- timestamped traffic, orders, units, realized price, conversion where available, cancellations, and returns;
- comparable competitor offers with variant, pack size, seller, fulfillment, availability, delivered price, source, and capture time;
- current repricing tool, platform capabilities, rule cadence, account permissions, approvers, and business objective.

If required economics or authorization details are missing, ask one consolidated follow-up. If they remain unavailable, design a provisional system but mark affected floors, rules, and automation decisions as blocked.

## Workflow

### 1. Establish the Evidence Boundary

List the exports, pages, cost sheets, platform settings, and seller facts actually inspected. Label each material input:

- **Confirmed:** supported by inspected evidence.
- **Assumption:** an explicit scenario placeholder, not an observed fact.
- **Unknown:** missing information that blocks reliable automation.

Do not invent demand, competitor history, costs, fees, elasticity, conversion, or platform capability. A visible competitor price is a point-in-time observation, not a durable market signal.

### 2. Calculate Economic Guardrails

Use realized seller-funded economics:

```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)
```

Model base, high-return, high-ad-cost, promotion-stack, and fee-change cases. Keep a contractual or legal minimum separate from the calculated economic floor. Define a ceiling from value, reference-price, policy, and customer-trust constraints; do not create artificial scarcity or an inflated reference price.

### 3. Classify SKU Automation Eligibility

Assign each SKU to one control tier:

| Tier | Appropriate when | Required control |
|---|---|---|
| Auto-eligible | reliable economics, stable identifier, trusted signals, reversible changes | bounded rules, logs, alerts, kill switch |
| Approval-required | launch, high margin risk, large price step, strategic product, sparse data | human review before publish |
| Manual-only | missing costs, MAP/legal ambiguity, bundles, custom products, unstable feed, sensitive category | analysis only |

Default uncertain SKUs to the more restrictive tier. Automation convenience is not evidence that a SKU is safe to automate.

### 4. Select and Validate Signals

For every signal, record source, freshness, coverage, failure mode, and fallback:

- **Competitor:** only normalized, comparable, available offers; reject mismatched packs, used items, suspicious sellers, and stale captures.
- **Demand:** use observed seller traffic and orders with timestamps; separate price effects from ads, content, seasonality, and stock.
- **Inventory:** use on-hand, age, sell-through, lead time, and replenishment risk; do not treat a feed error as surplus or scarcity.
- **Time or event:** use scheduled windows with explicit start, end, timezone, and promotion interaction.
- **Own promotion:** distinguish seller-funded from platform-funded incentives and confirm whether discounts stack.

Never use protected personal characteristics or opaque customer vulnerability to set individualized prices. Avoid price-gouging, collusion, and discriminatory outcomes.

### 5. Bu

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 dynamic-pricing-ecommerce -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 Dynamic Pricing Ecommerce

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

Spotted something wrong? Report a correction · Methodology