.github/plugins/azure-skills/skills/azure-aigateway/SKILL.md
Configure Azure API Management as an AI Gateway for AI models, MCP tools, and agents. WHEN: semantic caching, token limit, content safety, load balancing, AI model governance, MCP rate limiting, jailbreak detection, add Azure OpenAI backend, add AI Foundry model, test AI gateway, LLM policies, configure AI backend, token metrics, AI cost control, convert API to MCP, import OpenAPI to gateway.
npx skillsauth add microsoft/azure-skills azure-aigatewayInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Configure Azure API Management (APIM) as an AI Gateway for governing AI models, MCP tools, and agents.
To deploy APIM, use the azure-prepare skill. See APIM deployment guide.
| Category | Triggers | |----------|----------| | Model Governance | "semantic caching", "token limits", "load balance AI", "track token usage" | | Tool Governance | "rate limit MCP", "protect my tools", "configure my tool", "convert API to MCP" | | Agent Governance | "content safety", "jailbreak detection", "filter harmful content" | | Configuration | "add Azure OpenAI backend", "configure my model", "add AI Foundry model" | | Testing | "test AI gateway", "call OpenAI through gateway" |
| Policy | Purpose | Details |
|--------|---------|---------|
| azure-openai-token-limit | Cost control | Model Policies |
| azure-openai-semantic-cache-lookup/store | 60-80% cost savings | Model Policies |
| azure-openai-emit-token-metric | Observability | Model Policies |
| llm-content-safety | Safety & compliance | Agent Policies |
| rate-limit-by-key | MCP/tool protection | Tool Policies |
# Get gateway URL
az apim show --name <apim-name> --resource-group <rg> --query "gatewayUrl" -o tsv
# List backends (AI models)
az apim backend list --service-name <apim-name> --resource-group <rg> \
--query "[].{id:name, url:url}" -o table
# Get subscription key
az apim subscription keys list \
--service-name <apim-name> --resource-group <rg> --subscription-id <sub-id>
GATEWAY_URL=$(az apim show --name <apim-name> --resource-group <rg> --query "gatewayUrl" -o tsv)
curl -X POST "${GATEWAY_URL}/openai/deployments/<deployment>/chat/completions?api-version=2024-02-01" \
-H "Content-Type: application/json" \
-H "Ocp-Apim-Subscription-Key: <key>" \
-d '{"messages": [{"role": "user", "content": "Hello"}], "max_tokens": 100}'
See references/patterns.md for full steps.
# Discover AI resources
az cognitiveservices account list --query "[?kind=='OpenAI']" -o table
# Create backend
az apim backend create --service-name <apim> --resource-group <rg> \
--backend-id openai-backend --protocol http --url "https://<aoai>.openai.azure.com/openai"
# Grant access (managed identity)
az role assignment create --assignee <apim-principal-id> \
--role "Cognitive Services User" --scope <aoai-resource-id>
Recommended policy order in <inbound>:
See references/policies.md for complete example.
| Issue | Solution |
|-------|----------|
| Token limit 429 | Increase tokens-per-minute or add load balancing |
| No cache hits | Lower score-threshold to 0.7 |
| Content false positives | Increase category thresholds (5-6) |
| Backend auth 401 | Grant APIM "Cognitive Services User" role |
See references/troubleshooting.md for details.
development
# Azure App Onboard Scaffold — IaC Generation + Self-Review Generate deployment-ready infrastructure code from an architecture plan, verify it with adversarial self-review, and bridge to validation — all without deploying. ## Quick Reference | Property | Value | |----------|-------| | Parent | [azure-app-onboard](../SKILL.md) | | Best for | Turning `prepare-plan.json` service list into Bicep templates with secure-by-default patterns | | Inputs | `prepare-plan.json` (services, naming, quotas),
devops
# Prepare — Architecture Planning & Cost Estimation ## Quick Reference | Property | Value | |----------|-------| | Best for | Mapping app components to Azure services with cost estimation and quota validation | | Inputs | `prereq-output.json` + `context.json` from `.copilot-azure/sessions/{id}/` | | Outputs | `prepare-plan.json` written to session directory | | Parent | [azure-app-onboard](../SKILL.md) | ## When to Use This Skill Invoked by the `azure-app-onboard` orchestrator at Phase 2 whe
testing
# Deploy — IaC Execution & Health Verification ## Quick Reference | Property | Value | |----------|-------| | Best for | Executing validated IaC against Azure, health-checking deployed resources | | Inputs | `prepare-plan.json` + `scaffold-manifest.json` from `.copilot-azure/sessions/{id}/` | | Outputs | `deploy-result.json` written to session directory | | Parent | [azure-app-onboard](../SKILL.md) | ## When to Use This Skill Invoked by the `azure-app-onboard` orchestrator at Phase 4 when `s
tools
End-to-end orchestrator: from a business idea, app idea, or existing app to running Azure deployment with cost estimates and pre-deploy approval. Analyzes your app, auto-detects the right Azure services, scaffolds infrastructure code, and deploys — tailored to your app, not a template. Handles moving existing apps to Azure without rewriting or with minimal changes. WHEN: bring your app to Azure, plan my app, cost to run, is my code ready to deploy, deploy my app to the cloud, deploy all my services, what Azure services do I need, plan my Azure deployment, deploy my new app to Azure, one-click deploy, I have an app and want it on Azure, migrate my app to Azure, help me get started, build an app, no code yet, starter project. DO NOT USE FOR: running azd up (use azure-deploy), optimizing existing costs (use azure-cost), code readiness checks only (use azure-app-onboard-prereq).