azure-ai-projects-ts
by microsoftBuild Azure AI Foundry apps with azure-ai-projects-ts and @azure/ai-projects in TypeScript. Use this skill for project clients, agents, connections, deployments, datasets, indexes, evaluations, and OpenAI access. It is a practical guide for API development with Azure project resources and credentials.
This skill scores 84/100 because it is a solid, install-worthy TypeScript SDK skill with clear scope, usable references, and real workflow content for Azure AI Foundry projects. Directory users should expect good operational leverage for agents working with project clients, connections, deployments, and evaluations, though the skill would benefit from more complete end-to-end guidance and tighter trigger cues.
- Clear use-case trigger in frontmatter: build AI apps with Azure AI Projects SDK for JavaScript/TypeScript, including agents, connections, deployments, datasets, indexes, and evaluations.
- Substantial operational content with 12 H2 headings, code fences, and reference docs for connections and evaluations, which helps agents act with less guesswork.
- Concrete install and auth guidance, including npm install commands, environment variables, and credential examples for local dev and production.
- No install command in the SKILL.md metadata and no scripts/rules files, so some workflows rely on reading prose and code samples rather than automated execution help.
- Only two reference files are present, so broader scenarios like datasets, indexes, or agent orchestration may have less step-by-step coverage than the frontmatter suggests.
Overview of azure-ai-projects-ts skill
What azure-ai-projects-ts is for
The azure-ai-projects-ts skill helps you build against Azure AI Foundry project APIs in TypeScript using @azure/ai-projects. It is most useful when you need to work with project-scoped agents, connections, deployments, datasets, indexes, evaluations, or OpenAI client access without guessing the SDK shape.
Who should install it
Install the azure-ai-projects-ts skill if you are shipping an Azure AI app, adding Foundry project integration to an existing Node.js codebase, or wiring API Development work that depends on Azure project resources and credentials. It is a strong fit for developers who want an implementation guide, not just a library name.
What makes it different
The value of the azure-ai-projects-ts skill is that it centers the project workflow: endpoint configuration, Azure identity, connection lookup, and evaluation loops. That makes it more practical than a generic prompt when your output must match Azure Foundry conventions and real SDK methods.
How to Use azure-ai-projects-ts skill
Install azure-ai-projects-ts
Use the standard skill install flow first, then read the packaged guidance before coding:
npx skills add microsoft/skills --skill azure-ai-projects-ts
For local work, also install the SDK dependencies the skill expects:
npm install @azure/ai-projects @azure/identity
If you plan to trace requests, add the telemetry packages mentioned in the skill files.
Feed it the right project inputs
The azure-ai-projects-ts usage pattern works best when you provide concrete Azure facts up front: your project endpoint, the target model deployment name, and the auth mode you can actually use. A weak prompt says “show me agents”; a stronger prompt says “build a TypeScript example that connects to my Foundry project, lists OpenAI-backed connections, and creates an agent using a deployed model named gpt-4o.”
Read these files first
Start with SKILL.md, then inspect references/connections.md and references/evaluations.md because they expose the most decision-making value for adoption. connections.md shows how the SDK discovers linked Azure resources, while evaluations.md shows how you verify output quality instead of stopping at a demo call.
Use this workflow
- Confirm the Azure AI Project endpoint and credential strategy.
- Map your task to one SDK area: connections, agents, deployments, datasets, indexes, or evaluations.
- Draft the prompt with your target resource names and desired output shape.
- Ask for code that matches your environment, not a generic sample.
- Test against a real project and revise based on auth, naming, or connection errors.
azure-ai-projects-ts skill FAQ
Is azure-ai-projects-ts only for Azure AI Foundry?
Yes, the azure-ai-projects-ts skill is centered on Azure AI Foundry project workflows. If your app does not use a Foundry project endpoint, project connections, or Azure identity-based access, this skill is probably the wrong fit.
Do I need this if I already know TypeScript?
Yes, if you need the Azure-specific wiring. TypeScript knowledge helps, but the hard part is often the Azure project setup, credential choice, and resource naming. The azure-ai-projects-ts guide reduces that setup guesswork.
When should I not use it?
Skip it if you only want a quick generic OpenAI example, if you are not using a project-scoped Azure resource, or if you cannot supply environment variables and credential context. In those cases, a general SDK prompt will be faster.
Is it beginner-friendly?
It is beginner-friendly if you already have an Azure project endpoint and can follow installation steps. It is less friendly if you are still deciding between local dev credentials and production identity because those choices affect the code shape.
How to Improve azure-ai-projects-ts skill
Give the skill a narrower job
The fastest way to improve azure-ai-projects-ts results is to ask for one outcome per prompt: connect, list, create, evaluate, or retrieve. Broad prompts like “build my AI app” usually produce vague samples that do not map cleanly to the SDK.
Include the Azure details that matter
State your endpoint, model deployment name, auth method, and any connection names you already know. For example: “Use DefaultAzureCredential locally, target AZURE_AI_PROJECT_ENDPOINT, and read the my-openai-connection resource.” Those details prevent the skill from inventing placeholders you cannot run.
Ask for output that matches your repo
If you need azure-ai-projects-ts for API Development, specify whether you want a route handler, service class, CLI command, or minimal integration snippet. The skill is more useful when it fits your app boundary instead of returning an isolated SDK demo.
Iterate from errors, not guesses
After the first run, correct the prompt using real failures: missing env vars, wrong connection type, unavailable evaluator, or deployment mismatch. That is the main way to turn the azure-ai-projects-ts install output into code you can actually ship.