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帮助你端到端部署、评估、微调并持续优化 Microsoft Foundry 智能体与模型。
复制安装指令，让 AI 自动完成配置 · 推荐新手
请帮我安装 askskill 上的 "microsoft-foundry" 技能： 1. 下载 https://raw.githubusercontent.com/microsoft/GitHub-Copilot-for-Azure/main/plugin/skills/microsoft-foundry/SKILL.md 2. 保存为 ~/.claude/skills/microsoft-foundry/SKILL.md 3. 装好后重载技能，告诉我可以用了
请指导我在 Microsoft Foundry 中完成智能体部署：从 Docker 构建、推送到 ACR、创建 Foundry 项目与资源，到创建一个托管智能体，并说明所需 RBAC 权限、区域与容量检查项。
一套分步骤的部署指南，包含前置条件、命令示例、权限配置与常见故障排查建议。
我已经有一个 Foundry 智能体，请帮我设计批量评估和持续评估方案，包括数据集准备、grader 设置、关键指标、持续监控流程，以及如何根据评估结果改进提示词和指令。
一个完整的评估方案，涵盖评测数据、指标定义、自动化监控思路和优化建议。
请帮我规划在 Microsoft Foundry 中进行模型微调与智能体优化：比较 SFT、DPO、RFT 的适用场景，说明训练数据准备方式，如何从 traces 筛选数据集，以及如何使用 prompt optimizer 和 Agent Optimizer scaffold 提升效果。
一份微调与优化路线图，包含方法选择建议、数据准备步骤和可执行的优化流程。
This skill helps developers work with Microsoft Foundry resources, covering model discovery and deployment, complete dev lifecycle of AI agent, evaluation workflows, and troubleshooting.
MANDATORY: Before executing ANY workflow, you MUST first call the Azure MCP
foundrytool and inspect the available Foundry MCP tools and related parameters. Treat this initialfoundrycall as a discovery/help step. For this skill, Azure MCPfoundryis the required entry point for Foundry-related MCP operations.
MANDATORY: Before executing ANY workflow-specific steps, you MUST read the corresponding sub-skill document. Do not call workflow-specific MCP tools for a workflow without reading its skill document. This applies even if you already know the MCP tool parameters — the skill document contains required workflow steps, pre-checks, and validation logic that must be followed. This rule applies on every new user message that triggers a different workflow, even if the skill is already loaded.
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 |
|---|---|---|
| Containerize, build, push to ACR, create/update/clone agent deployments |
| deploy |
| invoke | Send messages to an agent, single or multi-turn conversations | invoke |
| invocations-ws | Build, deploy, and connect to hosted agents that speak the invocations_ws duplex WebSocket protocol — voice agents, real-time streams, and signaling for out-of-band media transports. | invocations-ws |
| observe | Evaluate agent quality, run batch evals, analyze failures, optimize prompts, improve agent instructions, compare versions, set up CI/CD monitoring, and enable continuous production evaluation | observe |
| trace | Query traces, analyze latency/failures, correlate eval results to specific responses via App Insights customEvents | trace |
| troubleshoot | View hosted agent logs, query telemetry, diagnose failures | troubleshoot |
| create | Create new hosted agent applications. Supports Microsoft Agent Framework, LangGraph, or custom frameworks in Python or C#, across responses, invocations, or invocations_ws protocols. | create |
| agent-optimizer | Make existing Python hosted-agent code optimization-ready, configure eval.yaml, run Agent Optimizer jobs, apply candidates locally, and deploy through azd after review. | agent-optimizer |
| 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 |
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