customer-returns

Name: customer-returns
Availability: InStock
Author: Canhada-Labs

Manage retail returns operations with RMA workflow design and fraud detection.

2|Updated Jun 30, 2026

Search Tags:#fraud detection #consumer rights #RMA #restocking #retail returns

Ready to run

npx skills add https://github.com/Canhada-Labs/ceo-orchestration --skill customer-returns

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: customer-returns
Source: https://github.com/Canhada-Labs/ceo-orchestration/tree/main/.claude/skills/domains/retail/skills/customer-returns
Command: npx skills add https://github.com/Canhada-Labs/ceo-orchestration --skill customer-returns

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill manages retail and e-commerce returns operations, providing governance, auditability, and analytics to optimize return processes and mitigate fraud.

Core Features & Use Cases

RMA Workflow Design: Structure return merchandise authorization (RMA) workflows for efficient returns management.
Return Reason Taxonomy: Categorize return reasons for root-cause analysis and policy improvement.
Restocking Decisions: Determine restocking dispositions based on item condition and category policy.
Fraud Detection: Identify and mitigate fraud patterns like wardrobing and serial returner behavior.
Consumer Rights Compliance: Ensure adherence to consumer rights laws across jurisdictions.
Use Case: When auditing a return program or investigating a spike in return rates, this Skill helps define RMA rules, classify return reasons, and optimize restocking strategies.

Quick Start

Load the customer-returns skill and apply it to the 'returns' directory within your repository.

Frequently Asked Questions about customer-returns

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

FAQPage Schema

How do I design an RMA workflow for retail returns management?▼

Return fraud detection identifies patterns like wardrobing and serial returner behavior by analyzing return data against governance policies. This Skill applies analytics to mitigate fraud while maintaining consumer rights compliance across jurisdictions.

What is the best way to classify return reasons for root-cause analysis?▼

Return reason taxonomy categorizes return reasons to enable accurate root-cause analysis and policy improvement. This Skill structures return reason classification to help you audit return programs and investigate spikes in return rates effectively.

How do restocking decisions work for returned retail items?▼

Restocking decisions determine item disposition based on condition and category policy. This Skill analyzes return data to apply governance policies, helping you decide whether returned items should be restocked or handled differently.

Can I ensure consumer rights compliance when managing e-commerce returns?▼

Consumer rights compliance for e-commerce returns requires adherence to jurisdictional laws throughout the RMA process. This Skill provides governance and auditability to ensure your returns operations align with consumer rights regulations across different regions.

Do I need existing return data to analyze serial returner behavior?▼

Analyzing serial returner behavior requires existing return data to identify fraud patterns and apply governance policies. This Skill processes your return data directory to detect wardrobing and serial returner behavior through comprehensive analytics.