From azure-agent-skills
Provides expert guidance for Microsoft Foundry (Azure AI Foundry) development, covering troubleshooting, best practices, architecture, decision making, security, deployment, and integrations.
How this skill is triggered — by the user, by Claude, or both
Slash command
/azure-agent-skills:microsoft-foundryThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
This skill provides expert guidance for Microsoft Foundry. Covers troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. It combines local quick-reference content with remote documentation fetching capabilities.
This skill provides expert guidance for Microsoft Foundry. Covers troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. It combines local quick-reference content with remote documentation fetching capabilities.
IMPORTANT for Agent: Use the Category Index below to locate relevant sections. For categories with line ranges (e.g.,
L35-L120), useread_filewith the specified lines. For categories with file links (e.g.,[security.md](security.md)), useread_fileon the linked reference file
IMPORTANT for Agent: If
metadata.generated_atis more than 3 months old, suggest the user pull the latest version from the repository. Ifmcp_microsoftdocstools are not available, suggest the user install it: Installation Guide
This skill requires network access to fetch documentation content:
mcp_microsoftdocs:microsoft_docs_fetch with query string from=learn-agent-skill. Returns Markdown.fetch_webpage with query string from=learn-agent-skill&accept=text/markdown. Returns Markdown.| Category | Lines | Description |
|---|---|---|
| Troubleshooting | L37-L45 | Diagnosing and recovering from Foundry model/agent failures, evaluation and observability issues, Azure OpenAI webhook errors, and known platform bugs with workarounds. |
| Best Practices | L46-L59 | Best practices for prompts, tools, safety messages, routing, evaluation, and fine-tuning so you can design, operate, and measure high-quality Azure/Foundry AI agents in production |
| Decision Making | L60-L93 | Guidance on choosing Foundry models, regions, deployment types, costs, lifecycle/retirement, and migration/upgrade paths (Azure OpenAI, GitHub Models, legacy agents, preview→GA). |
| Architecture & Design Patterns | L94-L101 | Designing Foundry agent architectures: VNet/subnet sizing, isolated resource layouts, high availability patterns, and model router patterns for routing and scaling AI workloads. |
| Limits & Quotas | L102-L122 | Quotas, rate limits, regions, and cost controls for Foundry agents and models, including Azure OpenAI, Claude, sessions, PTU sizing, batch jobs, prompt caching, and fine-tuning. |
| Security | L123-L161 | Security, privacy, RBAC, networking, keys, guardrails, and policy controls for Foundry agents, models, MCP servers, and Agent 365 integration, including data handling and compliance. |
| Configuration | L162-L219 | Configuring and operating Foundry agents and models: endpoints, tools, memory, security/guardrails, monitoring, evaluations, Azure OpenAI/Fireworks setup, networking, storage, and IDE integrations. |
| Integrations & Coding Patterns | L220-L297 | Integrating and coding with Foundry agents and models: tools, external services (MCP, LangChain, Azure AI, M365), function calling, routing, fine-tuning, tracing, and REST/SDK usage. |
| Deployment | L298-L314 | Deploying and migrating Foundry agents/models (real-time, containerized, VNet, open-source, fine-tuned), plus CI/CD, cloud evals, red teaming, and outage recovery steps |
| Topic | URL |
|---|---|
| Troubleshoot and understand Foundry partner models | https://learn.microsoft.com/en-us/azure/foundry/foundry-models/concepts/models-from-partners |
| Recover Foundry Agent Service from resource and data loss | https://learn.microsoft.com/en-us/azure/foundry/how-to/agent-service-operator-disaster-recovery |
| Troubleshoot Foundry evaluation and observability issues | https://learn.microsoft.com/en-us/azure/foundry/observability/how-to/troubleshooting |
| Set up and troubleshoot Azure OpenAI webhooks | https://learn.microsoft.com/en-us/azure/foundry/openai/how-to/webhooks |
| Known issues and workarounds for Microsoft Foundry services | https://learn.microsoft.com/en-us/azure/foundry/reference/foundry-known-issues |
| Topic | URL |
|---|---|
| Design networking and subnet sizing for Foundry agents | https://learn.microsoft.com/en-us/azure/foundry/agents/concepts/agents-networking-deep-dive |
| Plan standard agent setup with isolated resources | https://learn.microsoft.com/en-us/azure/foundry/agents/concepts/standard-agent-setup |
| Design high availability for Microsoft Foundry agents | https://learn.microsoft.com/en-us/azure/foundry/how-to/high-availability-resiliency |
| Apply model router patterns with Foundry agents | https://learn.microsoft.com/en-us/azure/foundry/openai/how-to/model-router-agents |
npx claudepluginhub microsoftdocs/agent-skills --plugin azure-agent-skillsExpert guidance for Microsoft Foundry Classic (Azure AI Foundry) covering troubleshooting, best practices, architecture, security, and deployment. Use for Foundry agents, Azure OpenAI deployments, RAG/search, model routing, and secure VNets.
Deploys, evaluates, fine-tunes, and manages Microsoft Foundry AI agents end-to-end using azd. Covers hosted agent lifecycle, prompt and Agent Optimizer, batch/continuous eval, dataset curation, and model fine-tuning (SFT/DPO/RFT).
Governs Azure AI Foundry operations: resource vs project boundaries, RBAC, quota, network isolation, logging, and safe MCP-backed read/write execution.