microsoft-foundry by microsoft/azure-skills
npx skills add https://github.com/microsoft/azure-skills --skill microsoft-foundry必读： 在调用任何 Foundry MCP 工具之前，请先阅读此技能及相关子技能。
MANDATORY: Read this skill and the relevant sub-skill BEFORE calling any Foundry MCP tool.
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| trace |
| troubleshoot | 查看容器日志，查询遥测数据，诊断故障 | troubleshoot |
| create | 创建新的托管智能体应用程序。支持 Microsoft Agent Framework、LangGraph 或 Python/C# 中的自定义框架。从 foundry-samples 仓库下载入门示例。 | create |
| eval-datasets | 将生产环境追踪数据收集为评估数据集，管理数据集版本和拆分，跟踪评估指标随时间的变化，检测回归，并维护从追踪到部署的完整谱系。适用于：从追踪创建数据集、数据集版本控制、评估趋势分析、回归检测、数据集比较、评估谱系。 | eval-datasets |
| project/create | 创建新的 Azure AI Foundry 项目，用于托管智能体和模型。在加入 Foundry 或设置新基础设施时使用。 | 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）。在需要精细控制手动配置 AI Services 资源时使用。 | resource/create/create-foundry-resource.md |
| models/deploy-model | 具有智能路由的统一模型部署。处理快速预设部署、完全自定义部署（版本/SKU/容量/RAI）以及跨区域的容量发现。路由到子技能：preset（快速部署）、customize（完全控制）、capacity（查找可用性）。 | models/deploy-model/SKILL.md |
| quota | 管理 Microsoft Foundry 资源的配额和容量。在检查配额使用情况、排查因配额不足导致的部署失败、请求增加配额或规划容量时使用。 | 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。用于访问控制、审计权限和 CI/CD 设置。 | rbac/rbac.md |
加入流程：project/create → deploy → invoke
| 意图 | 工作流 |
|---|---|
| 从头创建新智能体 | create → deploy → invoke |
| 部署现有代码 | deploy → invoke |
| 测试/与智能体对话 | invoke |
| 故障排除 | invoke → troubleshoot |
| 修复并重新部署 | troubleshoot → fix → deploy → invoke |
仅解析缺失的值。首先从用户消息中提取，然后从 azd 中提取，最后再询问。
azure.yaml；如果找到，则运行 azd env get-values| azd 变量 | 解析为 |
|---|---|
AZURE_AI_PROJECT_ENDPOINT / AZURE_AIPROJECT_ENDPOINT | 项目端点 |
AZURE_CONTAINER_REGISTRY_NAME / AZURE_CONTAINER_REGISTRY_ENDPOINT | ACR 注册表 |
AZURE_SUBSCRIPTION_ID | 订阅 |
每个工作流步骤之后，在继续之前进行验证：
| 类型 | 种类 | 描述 |
|---|---|---|
| Prompt | "prompt" | 基于 LLM，由模型部署支持 |
| Hosted | "hosted" | 基于容器，运行自定义代码 |
| 设置 | 能力宿主 | 描述 |
|---|---|---|
| Basic | 无 | 默认。所有资源由 Microsoft 管理。 |
| Standard | Azure AI Services | 自带存储和搜索（公共网络）。参见 standard-agent-setup。 |
| Standard + Private Network | Azure AI Services | 具有 VNet 隔离和私有端点的标准设置。参见 private-network-standard-agent-setup。 |
必读： 对于标准设置，在继续之前请阅读相应的参考文档：
- 公共网络： references/standard-agent-setup.md
- 私有网络（VNet 隔离）： references/private-network-standard-agent-setup.md
ask_user 或 askQuestions 工具task 或 runSubagent 工具来委托长时间运行或独立的子任务（例如，环境变量扫描、状态轮询、Dockerfile 生成）子技能中的脚本需要：Azure CLI (az) ≥2.0、jq（用于 shell 脚本）。如需使用 Python SDK，请通过 pip install azure-ai-projects azure-identity 安装。
每周安装次数
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Query traces, analyze latency/failures, correlate eval results to specific responses via App Insights customEvents |
| trace |
| troubleshoot | View container logs, query telemetry, diagnose failures | troubleshoot |
| create | Create new hosted agent applications. Supports Microsoft Agent Framework, LangGraph, or custom frameworks in Python or C#. Downloads starter samples from foundry-samples repo. | create |
| eval-datasets | Harvest production traces into evaluation datasets, manage dataset versions and splits, track evaluation metrics over time, detect regressions, and maintain full lineage from trace to deployment. Use for: create dataset from traces, dataset versioning, evaluation trending, regression detection, dataset comparison, eval lineage. | eval-datasets |
| 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 |
| 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 |
Onboarding flow: project/create → deploy → invoke
| Intent | Workflow |
|---|---|
| New agent from scratch | create → deploy → invoke |
| Deploy existing code | deploy → invoke |
| Test/chat with agent | invoke |
| Troubleshoot | invoke → troubleshoot |
| Fix + redeploy | troubleshoot → fix → deploy → invoke |
Resolve only missing values. Extract from user message first, then azd, then ask.
azure.yaml; if found, run azd env get-values| azd Variable | Resolves To |
|---|---|
AZURE_AI_PROJECT_ENDPOINT / AZURE_AIPROJECT_ENDPOINT | Project endpoint |
AZURE_CONTAINER_REGISTRY_NAME / AZURE_CONTAINER_REGISTRY_ENDPOINT | ACR registry |
AZURE_SUBSCRIPTION_ID | Subscription |
After each workflow step, validate before proceeding:
| Type | Kind | Description |
|---|---|---|
| Prompt | "prompt" | LLM-based, backed by model deployment |
| Hosted | "hosted" | Container-based, running custom code |
| Setup | Capability Host | Description |
|---|---|---|
| Basic | None | Default. All resources Microsoft-managed. |
| Standard | Azure AI Services | Bring-your-own storage and search (public network). See standard-agent-setup. |
| Standard + Private Network | Azure AI Services | Standard setup with VNet isolation and private endpoints. See private-network-standard-agent-setup. |
MANDATORY: For standard setup, read the appropriate reference before proceeding:
- Public network: references/standard-agent-setup.md
- Private network (VNet isolation): references/private-network-standard-agent-setup.md
ask_user or askQuestions tool whenever collecting information from the usertask or runSubagent tool to delegate long-running or independent sub-tasks (e.g., env var scanning, status polling, Dockerfile generation)Scripts in sub-skills require: Azure CLI (az) ≥2.0, jq (for shell scripts). Install via pip install azure-ai-projects azure-identity for Python SDK usage.
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