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- OpenClaw
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- Extract archive
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- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Communication & Collaboration
Whatsapp Context Manager for Agents
AI-powered WhatsApp tool for agents providing instant customer history, sentiment, priority, order details, VIP detection, and response suggestions.
skill openclawclawhub Free
AI-powered WhatsApp tool for agents providing instant customer history, sentiment, priority, order details, VIP detection, and response suggestions.
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- README.md, SKILL.md, examples_whatsapp.py, install_check_whatsapp.py, requirements_whatsapp.txt, test_whatsapp.py
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Release facts
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- 0.1.0
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- cerbug45
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Documentation
WhatsApp Intelligent Context Manager - Skill Guide
This skill provides an AI-powered context management system for WhatsApp customer service agents, enabling instant access to customer history, sentiment analysis, and smart response suggestions.
Quick Installation
# Download and extract unzip whatsapp-context-manager.zip cd whatsapp-context-manager # Verify installation (no dependencies needed!) python install_check_whatsapp.py # Run tests python test_whatsapp.py # Try examples python examples_whatsapp.py
What Problem Does This Solve?
Without This System: โ Agents have no context when customer messages arrive โ No idea if customer is VIP or first-timer โ Can't see order status without switching systems โ Don't know if message is urgent or can wait โ Guessing what to say instead of smart suggestions With This System: โ Complete customer context in 2 seconds โ Automatic sentiment analysis (angry/happy/neutral) โ Smart priority (critical/high/normal/low) โ Order status right there โ AI-powered response suggestions โ VIP customer detection
1. Initialize the System
from whatsapp_context_manager import ContextManager # Create context manager (creates local database) manager = ContextManager("production.db")
2. Process Incoming WhatsApp Message
# When a WhatsApp message arrives context = manager.process_incoming_message( phone="+1234567890", message_content="Where is my order?!", agent_id="agent_001" )
3. Display Context to Agent
# Show agent what they need to know print(f"Priority: {context.priority.value}") # "critical" print(f"Sentiment: {context.sentiment.value}") # "negative" print(f"Category: {context.category}") # "order_status" print(f"VIP Customer: {context.customer.is_vip}") # True/False # Key insights for insight in context.key_insights: print(f"๐ก {insight}") # Warnings for warning in context.warnings: print(f"โ ๏ธ {warning}") # Suggested responses for response in context.suggested_responses: print(f"๐ฌ {response}")
4. Send Reply
# Agent sends reply manager.send_message( phone="+1234567890", message_content="Your order #12345 is on the way!", agent_id="agent_001" )
What Agent Sees - Dashboard Example
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โ AGENT DASHBOARD โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ Customer: +1234567890 โ โ Name: John Doe โ โ VIP: YES โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ Priority: CRITICAL โ โ Sentiment: NEGATIVE โ โ Category: ORDER_STATUS โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ KEY INSIGHTS: โ โ โข ๐ VIP Customer - Prioritize response โ โ โข ๐ฆ Active Order: #ORD-12345 - shipped โ โ โข ๐ Tracking: TRK-ABC123 โ โ โข โก Customer expects fast replies (~2min) โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ WARNINGS: โ โ โข ๐จ CRITICAL: Requires immediate attention! โ โ โข ๐ก Customer is very upset - handle with care โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ SUGGESTED RESPONSES: โ โ 1. Let me check your order status right away. โ โ 2. Your order #ORD-12345 is shipped. โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
1. Automatic Sentiment Analysis
Detects customer mood from message: # System automatically analyzes sentiment context = manager.process_incoming_message(phone, "This is TERRIBLE!", agent_id) print(context.sentiment.value) # "very_negative" context = manager.process_incoming_message(phone, "Thanks!", agent_id) print(context.sentiment.value) # "positive" Sentiment Levels: ๐ก very_negative - Angry, furious, scam ๐ negative - Disappointed, problem ๐ neutral - Questions, info requests ๐ positive - Thanks, happy ๐คฉ very_positive - Excellent, love it
2. Message Categorization
Automatically categorizes messages: # System automatically categorizes context = manager.process_incoming_message(phone, "Where is my package?", agent_id) print(context.category) # MessageCategory.ORDER_STATUS context = manager.process_incoming_message(phone, "Refund please!", agent_id) print(context.category) # MessageCategory.PAYMENT Categories: ๐ฆ ORDER_STATUS - Delivery, tracking, shipment ๐ณ PAYMENT - Refund, billing, transaction ๐ด COMPLAINT - Problem, issue, broken ๐๏ธ PRODUCT_INQUIRY - Price, stock, features ๐ SUPPORT - Help, how-to, questions ๐ฐ SALES - Buy, purchase, interested โญ FEEDBACK - Review, opinion โ OTHER - Uncategorized
3. Priority Calculation
Smart priority based on multiple factors: # System calculates priority context = manager.process_incoming_message( phone="+1234567890", message_content="My payment failed!!!", agent_id="agent_001" ) print(context.priority.value) # "critical" Priority Levels: ๐ด CRITICAL - Angry customer, payment issue, VIP unhappy ๐ HIGH - Complaints, negative sentiment ๐ก NORMAL - General questions ๐ข LOW - Info requests, positive feedback
4. Response Suggestions
AI suggests appropriate responses: context = manager.process_incoming_message( phone="+1234567890", message_content="When will my order arrive?", agent_id="agent_001" ) # Get suggestions for response in context.suggested_responses: print(response) # Output: # "Let me check your order status right away." # "Your order #12345 is currently shipped." # "Expected delivery is tomorrow."
Order Integration
Add and track customer orders: from whatsapp_context_manager import Order from datetime import datetime, timedelta # Add order to system order = Order( order_id="ORD-12345", customer_id=context.customer.customer_id, status="shipped", amount=99.99, items=[ {"name": "Wireless Headphones", "quantity": 1, "price": 99.99} ], created_at=datetime.now().isoformat(), updated_at=datetime.now().isoformat(), tracking_number="TRK-ABC123", estimated_delivery=(datetime.now() + timedelta(days=2)).strftime("%Y-%m-%d") ) manager.add_order(order) # Now when customer asks about order, agent sees all details context = manager.process_incoming_message(phone, "Order status?", agent_id) print(context.active_orders[0].tracking_number) # "TRK-ABC123"
VIP Customer Management
Mark and manage VIP customers: # Update customer to VIP manager.update_customer_info( phone="+1234567890", name="John Doe", email="john@example.com", is_vip=True, tags=["premium", "loyal", "high-value"], notes="Always responds best to quick, direct answers" ) # Future messages automatically show VIP status context = manager.process_incoming_message(phone, "Hello", agent_id) print(context.customer.is_vip) # True print(context.customer.tags) # ["premium", "loyal", "high-value"]
Conversation History
Access complete conversation history: # Get context (includes recent messages) context = manager.process_incoming_message(phone, "Need help", agent_id) # View recent messages for msg in context.recent_messages: direction = "Customer" if msg.direction == "inbound" else "Agent" print(f"{direction}: {msg.content}")
Customer Profile
Access complete customer profile: context = manager.process_incoming_message(phone, "Hello", agent_id) customer = context.customer print(f"Phone: {customer.phone}") print(f"Name: {customer.name}") print(f"Total Messages: {customer.total_messages}") print(f"VIP: {customer.is_vip}") print(f"Tags: {customer.tags}") print(f"Notes: {customer.notes}") print(f"Last Contact: {customer.last_contact}") print(f"Sentiment History: {customer.sentiment_history}")
Use Case 1: Order Status Inquiry
# Customer: "Where is my order?" context = manager.process_incoming_message( phone="+1234567890", message_content="Where is my order?", agent_id="agent_001" ) # Agent sees: if context.active_orders: order = context.active_orders[0] print(f"Order ID: {order.order_id}") print(f"Status: {order.status}") print(f"Tracking: {order.tracking_number}") print(f"Est. Delivery: {order.estimated_delivery}") # Suggested response print(context.suggested_responses[0]) # "Your order #ORD-12345 is shipped. Tracking: TRK-ABC123"
Use Case 2: Angry Customer
# Customer: "This is TERRIBLE! I want a refund NOW!!!" context = manager.process_incoming_message( phone="+1234567890", message_content="This is TERRIBLE! I want a refund NOW!!!", agent_id="agent_001" ) # System detects: print(context.priority.value) # "critical" print(context.sentiment.value) # "very_negative" # Agent sees warnings: for warning in context.warnings: print(warning) # "๐จ CRITICAL: Requires immediate attention!" # "๐ก Customer is very upset - handle with care" # Suggested response print(context.suggested_responses[0]) # "I sincerely apologize for the inconvenience. Let me help resolve this."
Use Case 3: Multiple Customers Priority Queue
# Process messages from multiple customers customers = [ ("+1111111111", "Can I get some info?"), ("+2222222222", "My payment failed!!!"), ("+3333333333", "I have a complaint"), ("+4444444444", "Thanks for the help!"), ] contexts = [] for phone, message in customers: context = manager.process_incoming_message(phone, message, "agent_001") contexts.append((phone, context)) # Sort by priority priority_order = { MessagePriority.CRITICAL: 0, MessagePriority.HIGH: 1, MessagePriority.NORMAL: 2, MessagePriority.LOW: 3 } contexts.sort(key=lambda x: priority_order[x[1].priority]) # Agent dashboard shows: # 1. ๐ด +2222222222 - CRITICAL - Payment failed # 2. ๐ +3333333333 - HIGH - Complaint # 3. ๐ก +1111111111 - NORMAL - Info request # 4. ๐ข +4444444444 - LOW - Thank you message
Use Case 4: First-time vs Returning Customer
# System automatically tracks context = manager.process_incoming_message( phone="+9999999999", # New number message_content="Hello", agent_id="agent_001" ) # Check if first time if context.customer.total_messages == 1: print("๐ First time customer!") # Show introduction, onboarding info else: print(f"๐ Returning customer ({context.customer.total_messages} messages)") # Show history, previous orders
With WhatsApp Business API
from whatsapp_business_api import WhatsAppClient from whatsapp_context_manager import ContextManager # Initialize wa_client = WhatsAppClient(api_key="your_key") manager = ContextManager("production.db") # Handle incoming messages @wa_client.on_message def handle_message(phone, message): # Get context context = manager.process_incoming_message( phone=phone, message_content=message, agent_id="auto_agent" ) # Display to agent dashboard display_to_agent(context) # If critical, alert supervisor if context.priority == MessagePriority.CRITICAL: notify_supervisor(context)
With Web Dashboard
from flask import Flask, jsonify from whatsapp_context_manager import ContextManager app = Flask(__name__) manager = ContextManager() @app.route('/api/message', methods=['POST']) def process_message(): data = request.json # Process message context = manager.process_incoming_message( phone=data['phone'], message_content=data['message'], agent_id=data['agent_id'] ) # Return context as JSON return jsonify(context.to_dict())
1. Always Process Through System
# Good โ context = manager.process_incoming_message(phone, message, agent_id) # Agent has full context # Bad โ # Responding without context send_reply_directly(phone, "Hello") # Agent is blind
2. Mark VIP Customers
# Identify high-value customers early if customer_is_high_value(phone): manager.update_customer_info( phone=phone, is_vip=True, tags=["high-value", "premium"] )
3. Track Orders
# Add orders to system for automatic context when_order_placed(): manager.add_order(order) # Now agents automatically see order status when customer asks
4. Use Suggested Responses
# Get AI suggestions context = manager.process_incoming_message(phone, message, agent_id) # Show to agent for quick selection for i, response in enumerate(context.suggested_responses, 1): print(f"{i}. {response}")
5. Monitor Priority Queue
# Get all pending messages pending_contexts = get_all_pending_messages() # Sort by priority pending_contexts.sort(key=lambda x: priority_order[x.priority]) # Agents work from top (critical) to bottom (low)
1. Database Management
# Use separate databases for different purposes dev_manager = ContextManager("development.db") prod_manager = ContextManager("production.db") test_manager = ContextManager("test.db")
2. Batch Processing
# Process multiple messages efficiently for phone, message in message_queue: context = manager.process_incoming_message(phone, message, agent_id) process_context(context)
3. Regular Cleanup
# Archive old conversations (optional) # System stores everything by default # Implement custom archival if needed
Security Features
Local Storage: All data stored locally in SQLite No External Dependencies: Pure Python, no third-party libraries Data Integrity: SHA-256 checksums Secure Queries: Parameterized SQL, no injection risks Privacy: No data sent to external services
Issue: Database locked
# Use different database per process manager1 = ContextManager("agent1.db") manager2 = ContextManager("agent2.db")
Issue: Old data in tests
# Clean up test databases import os if os.path.exists("test.db"): os.remove("test.db")
Issue: No order suggestions
# Make sure orders are added to system order = Order(...) manager.add_order(order)
File Structure
whatsapp-context-manager/ โโโ whatsapp_context_manager.py # Main library โโโ examples_whatsapp.py # 8 usage examples โโโ test_whatsapp.py # Complete test suite โโโ README_WHATSAPP.md # Full documentation โโโ install_check_whatsapp.py # Installation check โโโ requirements_whatsapp.txt # Dependencies (none!) โโโ LICENSE_WHATSAPP # MIT License โโโ .gitignore_whatsapp # Git ignore rules
Requirements
Python 3.8 or higher No external dependencies!
Testing
# Run all tests python test_whatsapp.py # Should show: # โ Sentiment analysis tests passed # โ Message categorization tests passed # โ Priority calculation tests passed # โ Customer management tests passed # โ Message storage tests passed # โ Order management tests passed # โ VIP customer tests passed # โ Sentiment tracking tests passed # โ Response suggestions tests passed # โ Priority queue tests passed # โ Conversation flow tests passed # โ Context export tests passed # โ ALL TESTS PASSED
Examples
Run the examples to see the system in action: python examples_whatsapp.py Includes: Basic message processing Customer with active order Angry customer scenario VIP customer handling Conversation history Multiple customers priority queue Agent dashboard view Context export to JSON
Getting Help
๐ Read full documentation: README_WHATSAPP.md ๐ป Check examples: examples_whatsapp.py ๐งช Run tests: test_whatsapp.py ๐ Report issues on GitHub โญ Star the repo if helpful!
Next Steps
โ Install and verify: python install_check_whatsapp.py โ Run tests: python test_whatsapp.py โ Try examples: python examples_whatsapp.py โ Integrate with your WhatsApp system โ Customize for your needs
License
MIT License - see LICENSE_WHATSAPP file
Author
cerbug45 GitHub: @cerbug45 Transform your WhatsApp customer service from reactive to proactive! ๐
Category context
Messaging, meetings, inboxes, CRM, and teammate communication surfaces.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
3 Scripts2 Docs1 Files
- SKILL.md
- README.md
- examples_whatsapp.py
- install_check_whatsapp.py
- test_whatsapp.py
- requirements_whatsapp.txt