AZ

azure-ai-projects-ts

Enables JavaScript developers to manage AI project resources and agent orchestration in Azure.

Install

mkdir -p .claude/skills/azure-ai-projects-ts && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/8655" && unzip -o skill.zip -d .claude/skills/azure-ai-projects-ts && rm skill.zip

Installs to .claude/skills/azure-ai-projects-ts

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Build AI applications using Azure AI Projects SDK for JavaScript (@azure/ai-projects). Use when working with Foundry project clients, agents, connections, deployments, datasets, indexes, evaluations, or getting OpenAI clients.
226 chars✓ has a “when” trigger
Intermediate

Key capabilities

  • Manage AI agents
  • List model deployments
  • Upload and manage datasets
  • Create search indexes
  • Initialize OpenAI clients

How it works

The SDK provides a client interface to interact with Azure AI Foundry services, including agents, deployments, and indexes.

Inputs & outputs

You give it
AI project configuration
You get back
Agent response or project resource status

When to use azure-ai-projects-ts

  • Orchestrate AI agents in JS
  • Query Azure AI indexes
  • Configure AI project deployments

About this skill

Azure AI Projects SDK for TypeScript

High-level SDK for Azure AI Foundry projects with agents, connections, deployments, and evaluations.

Installation

npm install @azure/ai-projects @azure/identity

For tracing:

npm install @azure/monitor-opentelemetry @opentelemetry/api

Environment Variables

AZURE_AI_PROJECT_ENDPOINT=https://<resource>.services.ai.azure.com/api/projects/<project>
MODEL_DEPLOYMENT_NAME=gpt-4o
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production

Authentication

import { AIProjectClient } from "@azure/ai-projects";
import { DefaultAzureCredential, ManagedIdentityCredential } from "@azure/identity";

// Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
const credential = new DefaultAzureCredential({requiredEnvVars: ["AZURE_TOKEN_CREDENTIALS"]});
// Or use a specific credential directly in production:
// See https://learn.microsoft.com/javascript/api/overview/azure/identity-readme?view=azure-node-latest#credential-classes
// const credential = new ManagedIdentityCredential();

const client = new AIProjectClient(
  process.env.AZURE_AI_PROJECT_ENDPOINT!,
  credential
);

Operation Groups

GroupPurpose
client.agentsCreate and manage AI agents
client.connectionsList connected Azure resources
client.deploymentsList model deployments
client.datasetsUpload and manage datasets
client.indexesCreate and manage search indexes
client.evaluatorsManage evaluation metrics
client.memoryStoresManage agent memory

Getting OpenAI Client

const openAIClient = await client.getOpenAIClient();

// Use for responses
const response = await openAIClient.responses.create({
  model: "gpt-4o",
  input: "What is the capital of France?"
});

// Use for conversations
const conversation = await openAIClient.conversations.create({
  items: [{ type: "message", role: "user", content: "Hello!" }]
});

Agents

Create Agent

const agent = await client.agents.createVersion("my-agent", {
  kind: "prompt",
  model: "gpt-4o",
  instructions: "You are a helpful assistant."
});

Agent with Tools

// Code Interpreter
const agent = await client.agents.createVersion("code-agent", {
  kind: "prompt",
  model: "gpt-4o",
  instructions: "You can execute code.",
  tools: [{ type: "code_interpreter", container: { type: "auto" } }]
});

// File Search
const agent = await client.agents.createVersion("search-agent", {
  kind: "prompt",
  model: "gpt-4o",
  tools: [{ type: "file_search", vector_store_ids: [vectorStoreId] }]
});

// Web Search
const agent = await client.agents.createVersion("web-agent", {
  kind: "prompt",
  model: "gpt-4o",
  tools: [{
    type: "web_search_preview",
    user_location: { type: "approximate", country: "US", city: "Seattle" }
  }]
});

// Azure AI Search
const agent = await client.agents.createVersion("aisearch-agent", {
  kind: "prompt",
  model: "gpt-4o",
  tools: [{
    type: "azure_ai_search",
    azure_ai_search: {
      indexes: [{
        project_connection_id: connectionId,
        index_name: "my-index",
        query_type: "simple"
      }]
    }
  }]
});

// Function Tool
const agent = await client.agents.createVersion("func-agent", {
  kind: "prompt",
  model: "gpt-4o",
  tools: [{
    type: "function",
    function: {
      name: "get_weather",
      description: "Get weather for a location",
      strict: true,
      parameters: {
        type: "object",
        properties: { location: { type: "string" } },
        required: ["location"]
      }
    }
  }]
});

// MCP Tool
const agent = await client.agents.createVersion("mcp-agent", {
  kind: "prompt",
  model: "gpt-4o",
  tools: [{
    type: "mcp",
    server_label: "my-mcp",
    server_url: "https://mcp-server.example.com",
    require_approval: "always"
  }]
});

Run Agent

const openAIClient = await client.getOpenAIClient();

// Create conversation
const conversation = await openAIClient.conversations.create({
  items: [{ type: "message", role: "user", content: "Hello!" }]
});

// Generate response using agent
const response = await openAIClient.responses.create(
  { conversation: conversation.id },
  { body: { agent: { name: agent.name, type: "agent_reference" } } }
);

// Cleanup
await openAIClient.conversations.delete(conversation.id);
await client.agents.deleteVersion(agent.name, agent.version);

Connections

// List all connections
for await (const conn of client.connections.list()) {
  console.log(conn.name, conn.type);
}

// Get connection by name
const conn = await client.connections.get("my-connection");

// Get connection with credentials
const connWithCreds = await client.connections.getWithCredentials("my-connection");

// Get default connection by type
const defaultAzureOpenAI = await client.connections.getDefault("AzureOpenAI", true);

Deployments

// List all deployments
for await (const deployment of client.deployments.list()) {
  if (deployment.type === "ModelDeployment") {
    console.log(deployment.name, deployment.modelName);
  }
}

// Filter by publisher
for await (const d of client.deployments.list({ modelPublisher: "OpenAI" })) {
  console.log(d.name);
}

// Get specific deployment
const deployment = await client.deployments.get("gpt-4o");

Datasets

// Upload single file
const dataset = await client.datasets.uploadFile(
  "my-dataset",
  "1.0",
  "./data/training.jsonl"
);

// Upload folder
const dataset = await client.datasets.uploadFolder(
  "my-dataset",
  "2.0",
  "./data/documents/"
);

// Get dataset
const ds = await client.datasets.get("my-dataset", "1.0");

// List versions
for await (const version of client.datasets.listVersions("my-dataset")) {
  console.log(version);
}

// Delete
await client.datasets.delete("my-dataset", "1.0");

Indexes

import { AzureAISearchIndex } from "@azure/ai-projects";

const indexConfig: AzureAISearchIndex = {
  name: "my-index",
  type: "AzureSearch",
  version: "1",
  indexName: "my-index",
  connectionName: "search-connection"
};

// Create index
const index = await client.indexes.createOrUpdate("my-index", "1", indexConfig);

// List indexes
for await (const idx of client.indexes.list()) {
  console.log(idx.name);
}

// Delete
await client.indexes.delete("my-index", "1");

Key Types

import {
  AIProjectClient,
  AIProjectClientOptionalParams,
  Connection,
  ModelDeployment,
  DatasetVersionUnion,
  AzureAISearchIndex
} from "@azure/ai-projects";

Best Practices

  1. Use getOpenAIClient() - For responses, conversations, files, and vector stores
  2. Version your agents - Use createVersion for reproducible agent definitions
  3. Clean up resources - Delete agents, conversations when done
  4. Use connections - Get credentials from project connections, don't hardcode
  5. Filter deployments - Use modelPublisher filter to find specific models

When not to use it

  • Non-Foundry project management

Prerequisites

AZURE_AI_PROJECT_ENDPOINTMODEL_DEPLOYMENT_NAME

Limitations

  • Requires Azure AI Foundry project context

How it compares

It centralizes AI project management compared to using disparate service APIs.

Compared to similar skills

azure-ai-projects-ts side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
azure-ai-projects-ts (this skill)03moReviewIntermediate
open-prose12moReviewAdvanced
exa-core-workflow-a127dReviewIntermediate
perplexity-known-pitfalls027dReviewIntermediate

Try saying

Example prompts that trigger this skill in your AI assistant.

More by microsoft

View all by microsoft

You might also like

open-prose

openprose

OpenProse is a programming language for AI sessions. Activate on ANY `prose` command (prose boot, prose run, prose compile, prose update, etc.), running .prose files, mentioning OpenProse/Prose, or orchestrating multi-agent workflows. The skill intelligently interprets what the user wants.

12

exa-core-workflow-a

jeremylongshore

Execute Exa primary workflow: Core Workflow A. Use when implementing primary use case, building main features, or core integration tasks. Trigger with phrases like "exa main workflow", "primary task with exa".

10

perplexity-known-pitfalls

jeremylongshore

Identify and avoid Perplexity anti-patterns and common integration mistakes. Use when reviewing Perplexity code for issues, onboarding new developers, or auditing existing Perplexity integrations for best practices violations. Trigger with phrases like "perplexity mistakes", "perplexity anti-patterns", "perplexity pitfalls", "perplexity what not to do", "perplexity code review".

01

mcp-apps-development

jnPiyush

Build MCP Apps (ext-apps) that render interactive UI inside conversational AI clients. Use when creating visual tool outputs, interactive dashboards, form-based tools, or rich media experiences in MCP-compatible hosts like Claude Desktop, VS Code Copilot Chat, or other MCP clients that support the A

00

transformers-js

sediman-agent

Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Wor

00

langchain

zechenzhangAGI

Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.

26138

Search skills

Search the agent skills registry