ecommerce-support_skill

Overview

This skill is an AI-driven e-commerce customer support assistant that handles order queries, product recommendations, complaint tickets, and canned replies. It can optionally use Playwright to automate backend checks for real order status and supports saving conversations and ticket records. The skill focuses on fast intent recognition and producing empathetic, accurate customer responses. It is designed for integration with optional storage and automation MCPs for end-to-end workflows.

How this skill works

The assistant first classifies customer intent and extracts entities like order IDs, product names, emotion, and urgency. For order checks it can call Playwright to log into seller dashboards and fetch real-time order or product data, or fall back to simulated records for demos. It creates tickets for refunds or complaints, generates empathetic replies using templates, and saves conversation and ticket data to storage when configured.

When to use it

• Responding to customer messages about orders, shipping, refunds, or product info
• Generating polite, professional reply drafts for customer service agents
• Automating bulk replies for a batch of similar customer inquiries
• Creating and tracking complaint or refund tickets
• Integrating backend order checks into chat workflows using Playwright

Best practices

• Start replies with empathy, then state facts and next steps
• Validate extracted entities (order ID, product) before querying backend
• Use Playwright only when you have secure, consented access to seller accounts
• Keep a knowledge base of common templates and update them from real interactions
• Prioritize urgent or high-emotion messages for manual review

Example use cases

• Single message: generate a friendly order-status reply after checking backend and include tracking info
• Batch processing: analyze 10 incoming messages, classify intents, and output 10 tailored replies for copy-paste
• Ticket creation: convert a refund or complaint intent into a tracked support ticket with priority and notes
• Product recommendation: parse user preferences and return 2–3 matched products with short selling points
• Demo mode: simulate order data when backend access is unavailable to demonstrate reply formatting