Agent skill · Data & Analytics

competitive-pricing-strategy

Build an evidence-based competitive pricing strategy for ecommerce products. Use when a seller asks how to position a price against competitors, set regular and promotional prices, protect contribution margin, design bundles or price tiers, respond to competitor moves, or coordinate prices across Amazon, Shopify, TikTok Shop, Walmart, eBay, or other channels. Do not use for automated repricing implementation or claims of a mathematically proven optimal price without sufficient data.

nexscope-aigithub.com/nexscope-aiGitHub ↗
claude-codeMIT
Install
npx skills add nexscope-ai/eCommerce-Skills --skill competitive-pricing-strategy --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 11 KB
Bundled scripts: none
Path: competitive-pricing-strategy/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 580
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Competitive Pricing Strategy Turn comparable-offer evidence, unit economics, and brand positioning into a SKU-level price architecture, competitor-response policy, and controlled rollout plan. ## Installation ```bash npx skills add nexscope-ai/eCommerce-Skills --skill competitive-pricing-strategy -g ``` ## Capabilities - Normalize competitor offers by variant, pack size, condition, shipping, discounts, and seller type. - Calculate price floors and contribution-margin scenarios from seller-supplied costs. - Map budget, value, parity, and premium positions without assuming the cheapest offer wins. - Design regular, launch, promotional, bundle, quantity, and channel-specific price architecture. - Create response rules for competitor discounts, stockouts, new entrants, and price wars. - Separate pricing recommendations from MAP, resale-price, tax, consumer-protection, and marketplace-policy decisions. - Produce an implementation plan with owners, evidence, monitoring, and stop conditions. ## Usage Examples ```text Compare these six competitor offers and tell me where my product should be priced. ``` ```text Build a launch pricing strategy for my premium skincare product on Amazon and

What's inside
Steps it walks through
  1. Installation
  2. Capabilities
  3. Usage Examples
  4. Inputs and Collection
  5. Workflow
  6. 1. Establish the Evidence Boundary
  7. 2. Normalize Comparable Offers
  8. 3. Build the Economic Guardrails
  9. 4. Map the Price-Value Landscape
  10. 5. Design the Price Architecture
  11. 6. Create Competitor-Response Rules
  12. 7. Plan the Rollout and Measurement
  13. Domain Rules
  14. Output Format
Ships with 1 file
  • agents/openai.yaml
Commands it runs
npx skills add nexscope-ai/eCommerce-Skills --skill competitive-pricing-strategy -g
More from eCommerce-Skills
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About this skill
What does the competitive-pricing-strategy skill do?

Build an evidence-based competitive pricing strategy for ecommerce products. Use when a seller asks how to position a price against competitors, set regular and promotional prices, protect contribution margin, design bundles or price tiers, respond to competitor moves, or coordinate prices across Amazon, Shopify, TikTok Shop, Walmart, eBay, or other channels. Do not use for automated repricing implementation or claims of a mathematically proven optimal price without sufficient data.

How do I install it?

Run `npx skills add nexscope-ai/eCommerce-Skills --skill competitive-pricing-strategy --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.

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