Official agent skill

Azure AI Language Conversations Py

Implement Conversational Language Understanding (CLU) using the azure-ai-language-conversations Python SDK.

OfficialMITAuto-check passedAI & LLM Engineering

Install Azure AI Language Conversations Py

skills CLI

$ npx skills add microsoft/skills --skill azure-ai-language-conversations-py -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install microsoft/skills azure-ai-language-conversations-py --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/microsoft/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.github/plugins/azure-sdk-python/skills/azure-ai-language-conversations-py .claude/skills/azure-ai-language-conversations-py && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "azure-ai-language-conversations-py" agent skill from https://github.com/microsoft/skills/tree/main/.github/plugins/azure-sdk-python/skills/azure-ai-language-conversations-py into .claude/skills/azure-ai-language-conversations-py/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "azure-ai-language-conversations-py", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/microsoft/skills/tree/main/.github/plugins/azure-sdk-python/skills/azure-ai-language-conversations-py

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add microsoft/skills --skill azure-ai-language-conversations-py -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install microsoft/skills azure-ai-language-conversations-py --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/microsoft/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.github/plugins/azure-sdk-python/skills/azure-ai-language-conversations-py .agents/skills/azure-ai-language-conversations-py && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "azure-ai-language-conversations-py" agent skill from https://github.com/microsoft/skills/tree/main/.github/plugins/azure-sdk-python/skills/azure-ai-language-conversations-py into .agents/skills/azure-ai-language-conversations-py/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "azure-ai-language-conversations-py", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add microsoft/skills --skill azure-ai-language-conversations-py -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install microsoft/skills azure-ai-language-conversations-py --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/microsoft/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.github/plugins/azure-sdk-python/skills/azure-ai-language-conversations-py .cursor/skills/azure-ai-language-conversations-py && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "azure-ai-language-conversations-py" agent skill from https://github.com/microsoft/skills/tree/main/.github/plugins/azure-sdk-python/skills/azure-ai-language-conversations-py into .cursor/skills/azure-ai-language-conversations-py/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "azure-ai-language-conversations-py", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/microsoft/skills.git --path .github/plugins/azure-sdk-python/skills/azure-ai-language-conversations-py

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add microsoft/skills --skill azure-ai-language-conversations-py -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install microsoft/skills azure-ai-language-conversations-py --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/microsoft/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.github/plugins/azure-sdk-python/skills/azure-ai-language-conversations-py .gemini/skills/azure-ai-language-conversations-py && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "azure-ai-language-conversations-py" agent skill from https://github.com/microsoft/skills/tree/main/.github/plugins/azure-sdk-python/skills/azure-ai-language-conversations-py into .gemini/skills/azure-ai-language-conversations-py/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "azure-ai-language-conversations-py", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install microsoft/skills azure-ai-language-conversations-py

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add microsoft/skills --skill azure-ai-language-conversations-py -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/microsoft/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/.github/plugins/azure-sdk-python/skills/azure-ai-language-conversations-py .github/skills/azure-ai-language-conversations-py && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "azure-ai-language-conversations-py" agent skill from https://github.com/microsoft/skills/tree/main/.github/plugins/azure-sdk-python/skills/azure-ai-language-conversations-py into .github/skills/azure-ai-language-conversations-py/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "azure-ai-language-conversations-py", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add microsoft/skills --skill azure-ai-language-conversations-py -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install microsoft/skills azure-ai-language-conversations-py --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/microsoft/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.github/plugins/azure-sdk-python/skills/azure-ai-language-conversations-py .opencode/skills/azure-ai-language-conversations-py && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "azure-ai-language-conversations-py" agent skill from https://github.com/microsoft/skills/tree/main/.github/plugins/azure-sdk-python/skills/azure-ai-language-conversations-py into .opencode/skills/azure-ai-language-conversations-py/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "azure-ai-language-conversations-py", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: azure-ai-language-conversations-py
GitHub stars: 3.1k
Used in: 2 other repos
Token cost: ~1.4k tokens
SKILL.md length: 397 words
Files: 3 (incl. references)
Skills in repo: 150
Repo updated: Oct 9, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Implement Conversational Language Understanding (CLU) using the azure-ai-language-conversations Python SDK.

Works in 4 steps: Always use the latest version of the… → Emphasize the use of… → Provide clear code examples… → …
Working with ConversationAnalysisClient to analyze conversation intent and entities
SKILL.md covers System Prompt, Authentication & Lifecycle, Best Practices and Examples, plus 1 more section
Needs AZURE_TOKEN_CREDENTIALS and AZURE_CONVERSATIONS_KEY

What it does

Azure AI Language Conversations Py is an agent skill from microsoft/skills, published by the product's own GitHub organization. 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.

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `references/capabilities.md` and `references/non-hero-scenarios.md`).

It sits in AI & LLM Engineering, covering Natural language processing. It works with Microsoft Azure, Python and Visual Studio Code. The repository describes itself as: Skills, MCP servers, Custom Agents, Agents.md for SDKs to ground Coding Agents. The licence is MIT.

When your agent uses it

Working with ConversationAnalysisClient to analyze conversation intent and entities
Building NLP features
Integrating language understanding into applications

Example prompts

“/azure-ai-language-conversations-py”

Requirements

Python 3
A credential in AZURE_CONVERSATIONS_KEY

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

Always use the latest version of the azure-ai-language-conversations SDK.
Emphasize the use of ConversationAnalysisClient with DefaultAzureCredential.
Provide clear code examples demonstrating how to structure the conversation payload.
Handle exceptions properly.

What it can do on your machine

Read from SKILL.md and the folder at commit 3898ec8. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are python).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
- AZURE_TOKEN_CREDENTIALS
- AZURE_CONVERSATIONS_KEY
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Azure AI Language Conversations Py loads about 1.4k tokens when it runs, and up to ~3k if it reads all its reference files. Until then it costs about 79 tokens; SKILL.md has 397 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~79
When it runs · the whole SKILL.md, loaded when a task matches: ~1.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from microsoft/skills at commit 3898ec8, republished under its MIT licence (© microsoft). 397 words, ~1,374 tokens.

Download SKILL.mdSave it as .claude/skills/azure-ai-language-conversations-py/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.

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
metadata.author: Microsoft
metadata.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-conversations SDK.
Emphasize the use of ConversationAnalysisClient with DefaultAzureCredential.
Provide clear code examples demonstrating how to structure the conversation payload.
Handle exceptions properly.

Authentication & Lifecycle

🔑 Two rules apply to every code sample below:

Prefer DefaultAzureCredential. It works locally (Azure CLI / VS Code / Developer CLI) and in Azure (managed identity, workload identity) with no code change. Avoid connection strings, account/API keys — they bypass Entra audit and rotation.

Local dev: DefaultAzureCredential works as-is.

Production: set AZURE_TOKEN_CREDENTIALS=prod (or AZURE_TOKEN_CREDENTIALS=<specific_credential>) to constrain the credential chain to production-safe credentials.

Wrap every client in a context manager so HTTP transports, sockets, and token caches are released deterministically:

Sync: with <Client>(...) as client:

Async: async with <Client>(...) as client: and async with DefaultAzureCredential() as credential: (from azure.identity.aio)

Snippets may abbreviate this setup, but production code should always follow both rules.

ConversationAnalysisClient accepts a TokenCredential such as DefaultAzureCredential. Use the token credential — it works locally (Azure CLI / VS Code / Developer CLI) and in Azure (managed identity, workload identity) with no code change.

Show full SKILL.md (173 more words)Show less

Legacy: API Key (existing keyed deployments)

New code should use DefaultAzureCredential. Use AzureKeyCredential only if you have an existing keyed deployment that hasn't been migrated to Entra ID yet — for example, regulated environments still completing their Entra rollout.

python

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"]

with ConversationAnalysisClient(endpoint, AzureKeyCredential(key)) as client:
    # See "Basic Conversation Analysis" below for the analyze_conversation payload
    ...

Best Practices

Pick sync OR async and stay consistent. Do not mix azure.ai.language.conversations sync clients with azure.ai.language.conversations.aio async clients in the same call path. Choose one mode per module.
Always use context managers for clients and async credentials. Wrap every client in with ConversationAnalysisClient(...) as client: (sync) or async with ConversationAnalysisClient(...) as client: (async). For async DefaultAzureCredential from azure.identity.aio, also use async with credential: so tokens and transports are cleaned up.
Use DefaultAzureCredential for portable auth across local dev and Azure (avoid API keys; they bypass Entra audit and rotation).
Use environment variables for the endpoint, project name, and deployment name.
Clearly map the participantId and id in the conversationItem payload.

Examples

Basic Conversation Analysis

python

import os
from azure.identity import DefaultAzureCredential
from azure.ai.language.conversations import ConversationAnalysisClient

endpoint = os.environ["AZURE_CONVERSATIONS_ENDPOINT"]
project_name = os.environ["AZURE_CONVERSATIONS_PROJECT"]
deployment_name = os.environ["AZURE_CONVERSATIONS_DEPLOYMENT"]

# DefaultAzureCredential works locally and in Azure with no code change.
credential = DefaultAzureCredential()

with ConversationAnalysisClient(endpoint, credential) as 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']}")

Reference Files

File	Contents
references/capabilities.md	Additional non-hero capabilities, operation-group coverage, and production checklists.
references/non-hero-scenarios.md	Dedicated non-hero examples for secondary/advanced scenarios.

Files

SKILL.md and 2 other files (references) in .github/plugins/azure-sdk-python/skills/azure-ai-language-conversations-py of microsoft/skills.

SKILL.md
references/capabilities.md
references/non-hero-scenarios.md

Open the folder on GitHubat commit 3898ec8

Used in 2 other repositories

We found 6 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in microsoft/skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Azure AI Language Conversations Py next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Azure AI Language Conversations Py compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Azure AI Language Conversations Py this skillmicrosoft/skills	3.1k	2 repos	~1.4k	Automated safety check: Pass	MIT	today
Hugging Face TokenizersOrchestra-Research/AI-Research-SKILLs	13k	6 repos	~3.4k	Automated safety check: Pass	MIT	3 mo ago
Azure Openai To Responsesmicrosoft/ai-agents-for-beginners	77k	—	~6k	Automated safety check: Notes	MIT	20 days ago
Azure Openai To Responsesmicrosoft/ai-agents-for-beginners	77k	—	~6k	Automated safety check: Notes	MIT	20 days ago
Sentence Transformers EmbeddingsOrchestra-Research/AI-Research-SKILLs	13k	2 repos	~1.6k	Automated safety check: Pass	MIT	3 mo ago
Deepgram Text Intelligencedeepgram/deepgram-python-sdk	469	—	~1.4k	Automated safety check: Pass	MIT	yesterday

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Questions about Azure AI Language Conversations Py

What does Azure AI Language Conversations Py do?

Implement Conversational Language Understanding (CLU) using the azure-ai-language-conversations Python SDK. Azure AI Language Conversations Py is an agent skill from microsoft/skills, published by the product's own GitHub organization. Implement Conversational Language Understanding (CLU) using the azure-ai-language-conversations Python SDK.

When should I use Azure AI Language Conversations Py?

Azure AI Language Conversations Py fits situations like: working with ConversationAnalysisClient to analyze conversation intent and entities; building NLP features; integrating language understanding into applications.

How do I install Azure AI Language Conversations Py in Claude Code?

Run `npx skills add microsoft/skills --skill azure-ai-language-conversations-py -a claude-code`. Or copy the skill folder (.github/plugins/azure-sdk-python/skills/azure-ai-language-conversations-py in microsoft/skills) into .claude/skills/azure-ai-language-conversations-py in your project. Claude Code loads it when a task matches its description.

How do I install Azure AI Language Conversations Py in Codex?

Run `npx skills add microsoft/skills --skill azure-ai-language-conversations-py -a codex`. Or copy the skill folder (.github/plugins/azure-sdk-python/skills/azure-ai-language-conversations-py in microsoft/skills) into .agents/skills/azure-ai-language-conversations-py in your project. Codex loads it when a task matches its description.

Can I use Azure AI Language Conversations Py in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add microsoft/skills --skill azure-ai-language-conversations-py -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/azure-ai-language-conversations-py, .gemini/skills/azure-ai-language-conversations-py, .github/skills/azure-ai-language-conversations-py and .opencode/skills/azure-ai-language-conversations-py in your project.

What does Azure AI Language Conversations Py need to run?

Going by SKILL.md and its folder, Azure AI Language Conversations Py needs credentials named AZURE_TOKEN_CREDENTIALS and AZURE_CONVERSATIONS_KEY. Our summary lists: Python 3; A credential in AZURE_CONVERSATIONS_KEY.

Does Azure AI Language Conversations Py access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Azure AI Language Conversations Py safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Azure AI Language Conversations Py use?

Azure AI Language Conversations Py is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Azure AI Language Conversations Py use?

About 1.4k tokens (SKILL.md is roughly 5.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.7k tokens, read only when the agent opens those files.

What are the alternatives to Azure AI Language Conversations Py?

Skills that share tags, products or a category with Azure AI Language Conversations Py: Hugging Face Tokenizers (Orchestra-Research/AI-Research-SKILLs, 13k stars), Azure Openai To Responses (microsoft/ai-agents-for-beginners, 77k stars), Azure Openai To Responses (microsoft/ai-agents-for-beginners, 77k stars) and Sentence Transformers Embeddings (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Azure AI Language Conversations Py?

microsoft (a GitHub organization, an official publisher) maintains it in microsoft/skills, which has 3,094 GitHub stars. The repository holds 150 skills in this directory. The repository was last updated on October 9, 2026.

Source: microsoft/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Azure AI Language Conversations Py

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Azure AI Language Conversations for Python

System Prompt

Authentication & Lifecycle

Legacy: API Key (existing keyed deployments)

Best Practices

Examples

Basic Conversation Analysis

Reference Files

Files

Used in 2 other repositories

Compare with similar skills

Similar skills

More from microsoft/skills

Related

Questions about Azure AI Language Conversations Py