ecommerce-support
Original:🇨🇳 Chinese
Translated
AI e-commerce customer service assistant that supports order query, product recommendation, and ticket processing. It can use Playwright MCP to automatically operate the e-commerce backend to query real order status.
5installs
Added on
NPX Install
npx skill4agent add yangliu2060/smith--skills ecommerce-supportTags
Translated version includes tags in frontmatterSKILL.md Content (Chinese)
View Translation Comparison →E-commerce Customer Service Assistant
AI-powered e-commerce customer service bot that automatically identifies customer intent and handles scenarios such as order query, product recommendation, and complaint tickets.
Trigger Conditions
This skill is activated when the user says the following content:
- "Customer service reply"
- "Handle customer issues"
- "Order query"
- "ecommerce support"
- "Help me reply to customers"
- "Generate customer service scripts"
Dependent MCP Services
| MCP | Purpose | Required |
|---|---|---|
| playwright | Automatically log in to e-commerce backend to query orders | Optional |
| supabase | Store order/ticket data | Optional |
| memory | Maintain conversation context | Optional |
Workflow
┌─────────────────────┐
│ Customer Message Input │
└──────────┬──────────┘
│
▼
┌─────────────────────┐
│ AI Intent Recognition │
│ Analyze Customer Needs │
└──────────┬──────────┘
│
┌─────┼─────┬──────────┐
│ │ │ │
▼ ▼ ▼ ▼
┌───────┐┌───────┐┌───────┐┌───────┐
│Order ││Product││Complaint││FAQ │
│Query ││Recommendation││Ticket││ │
└───┬───┘└───┬───┘└───┬───┘└───┬───┘
│ │ │ │
▼ ▼ ▼ ▼
┌───────┐┌───────┐┌───────┐┌───────┐
│Playwright│ AI ││Create││Knowledge│
│Query Backend│Analysis││Ticket││Base Matching│
└───┬───┘└───┬───┘└───┬───┘└───┬───┘
│ │ │ │
└────────┴────────┴────────┘
│
▼
┌─────────────────┐
│ Generate Customer Service Reply │
│ Save Conversation History │
└─────────────────┘Execution Steps
Step 1: Receive Customer Message
Input Format:
User: Help me reply to this customer:
"Hello, why hasn't my order 123456 been shipped yet? It's been 3 days!"Or batch processing:
User: Process these customer service messages [message list/file]Step 2: AI Intent Recognition
Intent Classification:
| Intent | Keywords | Handling Method |
|---|---|---|
| Order query | Order, shipping, logistics, where is it | Query order status |
| Refund and return | Refund, return, exchange, don't want it | Create return ticket |
| Product inquiry | In stock, size, color, recommendation | Product recommendation |
| Complaint and suggestion | Complaint, bad review, unsatisfied, garbage | Create complaint ticket |
| Promotion activity | Coupon, full reduction, activity, cheap | Provide activity information |
| After-sales issue | Broken, quality problem, repair | Create after-sales ticket |
| Casual chat | Hello, thank you, are you there | Regular reply |
Intent Recognition Prompt:
Analyze the intent of the following customer message:
【Customer Message】
{message}
【Output Format】
{
"intent": "order_query|refund|product_inquiry|complaint|promotion|after_sale|chat",
"confidence": 0.95,
"entities": {
"order_id": "123456",
"product_name": "",
"emotion": "negative|neutral|positive"
},
"urgency": "high|medium|low"
}Step 3: Order Query Processing
Method A: Use Playwright MCP to query real orders
Applicable Scenario: Need to query real order status from e-commerce backend
javascript
// Playwright MCP operation steps
1. Open e-commerce backend
browser_navigate({ url: "https://seller.taobao.com" })
// Or other e-commerce platform backends
2. Check login status
- If not logged in, prompt the user to log in first
- Save login status for subsequent use
3. Enter order management
browser_click({ element: "Order Management" })
4. Search for order number
browser_type({
element: "Order search box",
text: "{order_id}"
})
browser_click({ element: "Search" })
5. Get order status
browser_snapshot() // Screenshot to get order information
6. Parse order status
- Order status (pending shipping/shipped/delivered)
- Logistics information
- Estimated arrival timeSupported E-commerce Platforms:
- Taobao/Tmall Seller Center
- JD Merchant Backend
- Pinduoduo Merchant Edition
- Shopify Admin
- Youzan Merchant Backend
Method B: Simulate order data
Applicable Scenario: Demo or no backend access permission
json
{
"order_id": "123456",
"status": "shipped",
"status_text": "Shipped, in transit",
"logistics": {
"company": "SF Express",
"tracking_no": "SF1234567890",
"last_update": "2025-12-28 15:30",
"location": "Shenzhen Transit Center"
},
"estimated_delivery": "2025-12-30"
}Step 4: Product Recommendation Processing
Recommendation Logic:
1. Parse customer needs (category, price range, preference)
2. Match product library/search for products
3. Generate recommendation script
【Recommendation Script Template】
Dear customer, according to your needs, we recommend the following products for you:
1️⃣ **{Product Name 1}** - ¥{Price}
{Product Highlights}
2️⃣ **{Product Name 2}** - ¥{Price}
{Product Highlights}
Which one do you think is more suitable? Feel free to ask me any questions~Use Playwright to get product information
javascript
// Get product details from e-commerce platform
1. Open product page
browser_navigate({ url: "{product_url}" })
2. Get product information
browser_snapshot()
3. Parse information
- Product name
- Price
- Stock status
- Specification parametersStep 5: Ticket Processing
Create Ticket:
json
{
"ticket_id": "TK20251229001",
"type": "complaint",
"customer_id": "C12345",
"order_id": "123456",
"description": "Customer complains about slow logistics",
"priority": "high",
"status": "open",
"created_at": "2025-12-29T10:30:00Z"
}Ticket Processing Flow:
- Create ticket record
- Generate comfort reply
- Notify relevant personnel (available via email MCP)
- Track processing progress
Step 6: Generate Customer Service Reply
Reply Generation Prompt:
You are a professional e-commerce customer service representative, please generate a reply based on the following information:
【Customer Message】
{customer_message}
【Intent Analysis】
Intent: {intent}
Emotion: {emotion}
Urgency: {urgency}
【Query Result】
{query_result}
【Reply Requirements】
1. Friendly address (Dear/Hello)
2. Empathize first, then solve the problem
3. Accurate and complete information
4. Gentle and professional tone
5. Apologize actively if there is a problem
6. Ask if there are other needs at the end
【Output Format】
Output the reply content directly, you can use emojis appropriatelyStep 7: Save Conversation History
json
{
"conversation_id": "conv_20251229_001",
"customer_id": "C12345",
"messages": [
{
"role": "customer",
"content": "Why hasn't my order been shipped yet?",
"timestamp": "2025-12-29T10:30:00Z"
},
{
"role": "assistant",
"content": "Dear customer, I'm very sorry to keep you waiting...",
"timestamp": "2025-12-29T10:30:05Z",
"intent": "order_query"
}
]
}Customer Service Script Template Library
Order Query Reply
Shipped:
Dear customer, your order has been shipped! 🚚
Logistics information:
📦 Courier company: {company}
📝 Tracking number: {tracking_no}
📍 Current location: {location}
⏰ Estimated delivery: {estimated_delivery}
You can click the order details to view real-time logistics~
Do you have any other questions?Not Shipped:
Dear customer, I'm very sorry to keep you waiting! 🙏
Your order is currently being processed urgently and is expected to be shipped before {ship_date}.
We are deeply sorry for the inconvenience caused to you, and we will arrange it for you as soon as possible~
If you are in a hurry, I can help you urge the warehouse!Refund Processing Reply
Dear customer, we have received your refund application~
I have submitted it for processing here:
📋 Refund number: {refund_id}
💰 Refund amount: ¥{amount}
⏰ Expected arrival: 1-3 working days
The refund will be returned to the original account, please pay attention to the account changes then.
Feel free to contact me if you have any questions!Complaint Comfort Reply
Dear customer, I'm really sorry for the bad experience we brought you! 🙏
I fully understand your mood, this is indeed our problem.
I have already reported your situation to the supervisor, and we will give you a satisfactory solution as soon as possible.
To express our apology, we have applied for a {coupon} coupon for you,
Hope it can make up for a little bit of this unpleasant experience.
Would you mind leaving your contact number? Our supervisor will call you back personally to handle it.Product Inquiry Reply
Dear customer, the detailed information of this product is as follows:
📦 {product_name}
💰 Price: ¥{price}
📏 Specification: {specs}
🎁 Gift: {gifts}
📦 Stock: {stock_status}
{product_highlights}
There is also a {promotion} event when you place an order now~
Do you need me to check the size for you?Usage Examples
Example 1: Single Message Reply
User: Help me reply: "Where is order 123456?"
Claude:
1. Identify intent: Order query
2. [Optional] Use Playwright to query backend order status
3. Generate reply:
"Dear customer, your order 123456 has been shipped!
Courier: SF Express SF1234567890
Current location: Shenzhen Transit Center
Expected to arrive tomorrow~ Do you have any other questions?"Example 2: Batch Processing
User: Batch reply to these 10 customer messages
Claude:
1. Analyze intent one by one
2. Batch query relevant information
3. Generate 10 replies
4. Output results for copy and useExample 3: Connect to E-commerce Backend
User: Connect to Taobao backend, query the real status of order 123456
Claude:
1. Use Playwright to open Taobao Seller Center
2. Check login status (prompt if login is required)
3. Search for order number
4. Screenshot to get order status
5. Parse and generate replyE-commerce Platform Backend Configuration
Taobao/Tmall
yaml
platform: taobao
login_url: https://login.taobao.com
seller_url: https://myseller.taobao.com
order_path: /home.htm#/order-manage
search_selector: "#keyword"JD Merchant
yaml
platform: jd
login_url: https://passport.jd.com
seller_url: https://shop.jd.com
order_path: /order/list
search_selector: ".search-input"Shopify
yaml
platform: shopify
login_url: https://{store}.myshopify.com/admin
order_path: /admin/orders
search_selector: "#search-query"Data Storage
- Conversation records:
~/.claude/cache/ecommerce-support/conversations/ - Ticket records:
~/.claude/cache/ecommerce-support/tickets/ - Reply templates:
~/.claude/cache/ecommerce-support/templates/
Dependent Tools
- Claude AI: Intent recognition + reply generation
- playwright MCP: Query e-commerce backend orders (optional)
- memory MCP: Maintain conversation context (optional)
- Write: Save conversation history
Best Practices
Improve Reply Quality
- Empathize first before solving the problem
- Information should be accurate and complete
- Avoid mechanical replies
- Use emojis appropriately to increase affinity
Handle Negative Emotions
- Apologize as soon as possible
- Provide clear solutions
- Appropriate compensation (coupons, etc.)
- Upgrade channel (supervisor call back)
Improve Efficiency
- Establish a FAQ knowledge base
- Preset reply templates
- Batch process similar problems
- Automatically classify priorities
Original Source
Adapted from n8n template:
- Template ID: 7256
- Original name: AI-Powered E-commerce Customer Support Chatbot with GPT-4 & Supabase
- Link: https://n8n.io/workflows/7256