microsoft-foundry

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Microsoft Foundry Skill

This skill helps developers work with Microsoft Foundry resources, covering model discovery and deployment, RAG (Retrieval-Augmented Generation) applications, AI agent creation, evaluation workflows, and troubleshooting.

此Skill帮助开发者操作Microsoft Foundry资源，涵盖模型发现与部署、RAG（Retrieval-Augmented Generation，检索增强生成）应用、AI Agent创建、评估工作流以及故障排查。

Sub-Skills

子Skill

This skill includes specialized sub-skills for specific workflows. Use these instead of the main skill when they match your task:

Sub-Skill	When to Use	Reference
project/create	Creating a new Azure AI Foundry project for hosting agents and models. Use when onboarding to Foundry or setting up new infrastructure.	project/create/create-foundry-project.md
resource/create	Creating Azure AI Services multi-service resource (Foundry resource) using Azure CLI. Use when manually provisioning AI Services resources with granular control.	resource/create/create-foundry-resource.md
models/deploy-model	Unified model deployment with intelligent routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI), and capacity discovery across regions. Routes to sub-skills: `preset` (quick deploy), `customize` (full control), `capacity` (find availability).	models/deploy-model/SKILL.md
agent/create/agent-framework	Creating AI agents and workflows using Microsoft Agent Framework SDK. Supports single-agent and multi-agent workflow patterns with HTTP server and F5/debug support.	agent/create/agent-framework/SKILL.md
quota	Managing quotas and capacity for Microsoft Foundry resources. Use when checking quota usage, troubleshooting deployment failures due to insufficient quota, requesting quota increases, or planning capacity.	quota/quota.md
rbac	Managing RBAC permissions, role assignments, managed identities, and service principals for Microsoft Foundry resources. Use for access control, auditing permissions, and CI/CD setup.	rbac/rbac.md

💡 Tip: For a complete onboarding flow:
project/create
→
agent/create
→
agent/deploy
. If the user wants to create AND deploy an agent, start with
agent/create
which can optionally invoke
agent/deploy
automatically.

💡 Model Deployment: Use
models/deploy-model
for all deployment scenarios — it intelligently routes between quick preset deployment, customized deployment with full control, and capacity discovery across regions.

该Skill包含针对特定工作流的专用子Skill。当任务匹配时，请使用这些子Skill而非主Skill：

子Skill	适用场景	参考文档
project/create	创建用于托管Agent和模型的新Azure AI Foundry项目。适用于Foundry入门或配置新基础设施时。	project/create/create-foundry-project.md
resource/create	使用Azure CLI创建Azure AI Services多服务资源（Foundry资源）。适用于需要精细控制手动部署AI Services资源时。	resource/create/create-foundry-resource.md
models/deploy-model	具备智能路由的统一模型部署。支持快速预设部署、完全自定义部署（版本/SKU/容量/RAI）以及跨区域容量发现。可路由至子Skill： `preset` （快速部署）、 `customize` （完全控制）、 `capacity` （查找可用容量）。	models/deploy-model/SKILL.md
agent/create/agent-framework	使用Microsoft Agent Framework SDK创建AI Agent和工作流。支持单Agent和多Agent工作流模式，具备HTTP服务器和F5/调试支持。	agent/create/agent-framework/SKILL.md
quota	管理Microsoft Foundry资源的配额和容量。适用于检查配额使用情况、排查因配额不足导致的部署失败、申请配额提升或规划容量时。	quota/quota.md
rbac	管理Microsoft Foundry资源的RBAC权限、角色分配、托管标识和服务主体。适用于访问控制、权限审计和CI/CD配置。	rbac/rbac.md

💡 提示： 完整的入门流程：
project/create
→
agent/create
→
agent/deploy
。如果用户想要创建并部署Agent，请从
agent/create
开始，它可选择自动调用
agent/deploy
。

💡 模型部署： 所有部署场景均使用
models/deploy-model
—— 它可在快速预设部署、完全自定义部署和跨区域容量发现之间智能路由。

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