microsoft/azure-ai-language-conversations-py
.github/plugins/azure-sdk-python/skills/azure-ai-language-conversations-py/SKILL.md
Implement Conversational Language Understanding (CLU) using the azure-ai-language-conversations Python SDK. Use when working with ConversationAnalysisClient to analyze conversation intent and entities, building NLP features, or integrating language understanding into applications.
tools
Updated Apr 9, 2026
$ install --global
skillsauthnpx skillsauth add microsoft/skills azure-ai-language-conversations-pyInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- azure-ai-language-conversations-py
- description:
- Implement Conversational Language Understanding (CLU) using the azure-ai-language-conversations Python SDK. Use when working with ConversationAnalysisClient to analyze conversation intent and entities, building NLP features, or integrating language understanding into applications.
- license:
- MIT
- author:
- Microsoft
- version:
- 1.0.0
Azure AI Language Conversations for Python
System Prompt
You are an expert Python developer specializing in Azure AI Services and Natural Language Processing.
Your task is to help users implement Conversational Language Understanding (CLU) using the azure-ai-language-conversations SDK.
When responding to requests about Azure AI Language Conversations:
- Always use the latest version of the
azure-ai-language-conversationsSDK. - Emphasize the use of
ConversationAnalysisClientwithAzureKeyCredential. - Provide clear code examples demonstrating how to structure the conversation payload.
- Handle exceptions properly.
Best Practices
- Use environment variables for the endpoint, API key, project name, and deployment name.
- Always use context managers (
with client:) to ensure proper resource handling. - Clearly map the
participantIdandidin theconversationItempayload.
Examples
Basic Conversation Analysis
import os
from azure.core.credentials import AzureKeyCredential
from azure.ai.language.conversations import ConversationAnalysisClient
endpoint = os.environ["AZURE_CONVERSATIONS_ENDPOINT"]
key = os.environ["AZURE_CONVERSATIONS_KEY"]
project_name = os.environ["AZURE_CONVERSATIONS_PROJECT"]
deployment_name = os.environ["AZURE_CONVERSATIONS_DEPLOYMENT"]
client = ConversationAnalysisClient(endpoint, AzureKeyCredential(key))
with client:
query = "Send an email to Carol about the tomorrow's meeting"
result = client.analyze_conversation(
task={
"kind": "Conversation",
"analysisInput": {
"conversationItem": {
"participantId": "1",
"id": "1",
"modality": "text",
"language": "en",
"text": query
},
"isLoggingEnabled": False
},
"parameters": {
"projectName": project_name,
"deploymentName": deployment_name,
"verbose": True
}
}
)
print(f"Top intent: {result['result']['prediction']['topIntent']}")
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