Skills azure-ai-language-conversations-py

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.

install

source · Clone the upstream repo

git clone https://github.com/microsoft/skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/microsoft/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/.github/plugins/azure-sdk-python/skills/azure-ai-language-conversations-py" ~/.claude/skills/microsoft-skills-azure-ai-language-conversations-py && rm -rf "$T"

manifest: .github/plugins/azure-sdk-python/skills/azure-ai-language-conversations-py/SKILL.md

source content

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-conversations
```
SDK.

Emphasize the use of

ConversationAnalysisClient

with

AzureKeyCredential

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
```
participantId
```
and
```
id
```
in the
```
conversationItem
```
payload.

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']}")