---

name: messaging-bots

description: Develops integration patterns for messaging bots across platforms, focusing on automated interactions.

license: MIT compatibility: opencode metadata: version: "1.0.0" domain: communications role: implementation output-format: code triggers: messaging bots, chatbot integration, automated responses, conversational agents archetypes: conversational automation, messaging anti_triggers: human customer service, manual interactions response_profile: verbosity: low directive_strength: high scope: infrastructure related-skills: communications/messaging-channels, communications/messaging-microsoft-teams

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Messaging Bots Integration

Implements patterns for developing messaging bots, focusing on automated interactions using various APIs.

When to Use

Use this skill for:

• Creating automated responses for messaging platforms.
• Integrating chatbot capabilities into existing messaging workflows.

Core Workflow

Additional Examples

Example 3: NLP Integration with External APIs

import requests

def intent_recognition_with_external_api(user_message: str, api_endpoint: str):
    headers = {'Authorization': 'Bearer YOUR_API_KEY'}
    response = requests.post(api_endpoint, json={'message': user_message}, headers=headers)
    if response.status_code == 200:
        return response.json().get('intent')
    else:
        raise Exception('Failed to connect to external API')

Example 4: Contextual Conversations

class Bot:
    def __init__(self):
        self.context = {"user_id": None, "previous_interaction": None}

    def update_context(self, user_id: str, interaction: str):
        self.context["user_id"] = user_id
        self.context["previous_interaction"] = interaction

    def respond_to_user(self, message: str):
        # Respond based on user context
        intent = recognize_intent(message)
        return f'Responding with intent: {intent} based on context: {self.context}'

Constraints

• MUST DO need to reflect all aspects of integration. Ensure proper management of context throughout user interactions.

1. Identify User Intent — Use NLP to understand user messages.
2. Generate Responses — Provide automated responses based on user input.
3. Integrate with APIs — Connect with external services for added functionality (e.g., weather, news).

Implementation Patterns

Pattern 1: Basic Message Handling

from flask import Flask, request
import json

app = Flask(__name__)

@app.route('/webhook', methods=['POST'])
def handle_message():
    data = request.get_json()
    user_message = data['message']
    response = generate_response(user_message)
    return json.dumps({'response': response}), 200

def generate_response(message):
    # Simple logic to generate a response
    return "Thanks for your message!"

Pattern 2: API Integration for Enhanced Responses

import requests

def fetch_weather_info(location):
    api_key = 'your_api_key'
    url = f'http://api.openweathermap.org/data/2.5/weather?q={location}&appid={api_key}'
    response = requests.get(url)
    response.raise_for_status()
    return response.json()

Constraints

Additional Usage Examples

Example 1: NLP Intent Recognition

def recognize_intent(message: str):
    # Use NLP to determine user intent
    # Here we would integrate with a NLP API or library to classify intents
    intents = ['greeting', 'query', 'command']  # Example intents
    # Logic to determine the intent from the message
    return intents[0]  # Placeholder return

Example 2: Integrating External APIs

# Function to fetch data from an external API and utilize in responses
def fetch_external_data(endpoint: str):
    response = requests.get(endpoint)
    response.raise_for_status()  # Handle error for bad responses
    return response.json()

MUST DO

• Keep the bot's responses engaging and contextually relevant to enhance user experience.

MUST DO

• Validate incoming messages to ensure correct structure (check for missing fields).
• Use rate limiting to avoid exceeding API call limits.

MUST NOT DO

• Do not provide responses without verifying intent.
• Avoid overwhelming users with too many automated messages during a single interaction.

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Live References

Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content.

• Facebook Bot Platform Developer Docs
• WhatsApp Business API Documentation
• Slack Events API Reference
• Microsoft Teams Bot Framework SDK
• Chatbot Design Best Practices — Interaction Design Foundation