Govern AI models, MCP tools, and agents through Azure API Management with semantic caching, token limits, and content safety.
Works with
Supports five core policy categories: semantic caching (60-80% cost savings), token rate limiting, content safety filtering, jailbreak detection, and request rate limiting for MCP tool protection
Enables backend configuration for Azure OpenAI, AI Foundry models, and custom APIs with load balancing and managed identity authentication
Includes token metrics and
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionazure-aigatewayExecute the skills CLI command in your project's root directory to begin installation:
Fetches azure-aigateway from microsoft/GitHub-Copilot-for-Azure and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate azure-aigateway. Access via /azure-aigateway in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Accelerate learning and skill development by 2x
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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.
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
microsoft/GitHub-Copilot-for-Azure
microsoft/GitHub-Copilot-for-Azure
microsoft/azure-skills
membranedev/application-skills
microsoft/azure-skills
davila7/claude-code-templates
We added azure-aigateway from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: azure-aigateway is focused, and the summary matches what you get after install.
azure-aigateway has been reliable in day-to-day use. Documentation quality is above average for community skills.
azure-aigateway is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in azure-aigateway — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Keeps context tight: azure-aigateway is the kind of skill you can hand to a new teammate without a long onboarding doc.
azure-aigateway has been reliable in day-to-day use. Documentation quality is above average for community skills.
We added azure-aigateway from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in azure-aigateway — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Solid pick for teams standardizing on skills: azure-aigateway is focused, and the summary matches what you get after install.
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