retail-expert

Provide Python tools for retail POS, inventory, and customer analytics.

41|9|Updated Jan 13, 2026

Search Tags:#e-commerce #inventory management #omnichannel #retail #pos #customer analytics

Ready to run

npx skills add https://github.com/personamanagmentlayer/pcl --skill retail-expert

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: retail-expert
Source: https://github.com/personamanagmentlayer/pcl/tree/main/stdlib/domains/retail-expert
Command: npx skills add https://github.com/personamanagmentlayer/pcl --skill retail-expert

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scikit-learn, pandas, numpy, scipy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides expert guidance and tools to optimize retail operations, from point-of-sale systems and inventory management to customer analytics and e-commerce strategies.

Core Features & Use Cases

POS System Management: Handle transactions, apply discounts, and process payments.
Inventory Optimization: Calculate reorder points, EOQ, and forecast demand.
Customer Analytics: Perform RFM analysis, segment customers, and predict churn.
Use Case: A retail manager can use this Skill to analyze sales data, identify their most valuable customer segments, and optimize stock levels for key products to reduce waste and increase sales.

Quick Start

Use the retail-expert skill to calculate the reorder point for a product with average daily demand of 50 units, a lead time of 7 days, and a service level of 98%.

Frequently Asked Questions about retail-expert

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I calculate the reorder point for retail inventory management?▼

To calculate the reorder point for retail inventory management, you multiply average daily demand by lead time and add a safety stock buffer based on your target service level. This Skill computes these metrics using Python and pandas to automate stock optimization.

What is RFM analysis and how does it apply to customer analytics?▼

RFM analysis is a customer analytics technique that segments shoppers based on Recency, Frequency, and Monetary value. This Skill uses scikit-learn and pandas to process retail transaction data, identify high-value customer segments, and predict churn.

Can I use Python and pandas for point-of-sale transaction processing?▼

Yes, you can use Python and pandas for point-of-sale transaction processing. This Skill provides scripts to handle POS data, apply discounts, and process payments, facilitating operational efficiency through data-driven retail automation.

Does this retail analytics solution work with scikit-learn and numpy for demand forecasting?▼

Yes, this retail analytics solution works with scikit-learn and numpy for demand forecasting. It leverages these dependencies alongside scipy to analyze historical sales data and predict future product demand for inventory optimization.

What is the best way to calculate Customer Lifetime Value (CLV) for e-commerce?▼

The best way to calculate Customer Lifetime Value (CLV) for e-commerce is by analyzing historical transaction data to predict future revenue per customer. This Skill provides Python tools to compute CLV, supporting targeted omnichannel customer engagement strategies.