azure-aigateway

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.

Quality

93%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Advisory

Suggest reviewing before use

Quality

Discovery

100%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

This is a strong skill description that clearly defines its niche (Azure API Management as AI Gateway), lists concrete capabilities, and provides extensive trigger terms via an explicit WHEN clause. The description is well-structured, uses third person voice, and covers both the 'what' and 'when' dimensions thoroughly. The trigger terms are highly specific and natural, making it easy for Claude to select this skill appropriately.

Dimension	Reasoning	Score
Specificity	The description lists multiple specific concrete actions: configuring Azure API Management as an AI Gateway, semantic caching, token limiting, content safety, load balancing, jailbreak detection, adding Azure OpenAI backends, converting APIs to MCP, importing OpenAPI specs, and more.	3 / 3
Completeness	Clearly answers both 'what' (Configure Azure API Management as an AI Gateway for AI models, MCP tools, and agents) and 'when' with an explicit 'WHEN:' clause listing numerous specific trigger scenarios.	3 / 3
Trigger Term Quality	Excellent coverage of natural trigger terms users would say: 'semantic caching', 'token limit', 'content safety', 'load balancing', 'jailbreak detection', 'Azure OpenAI backend', 'AI Foundry model', 'MCP rate limiting', 'convert API to MCP', 'import OpenAPI to gateway'. These are terms practitioners would naturally use.	3 / 3
Distinctiveness Conflict Risk	Highly distinctive with a clear niche: Azure API Management as an AI Gateway. The combination of Azure APIM, AI gateway policies, MCP tools, and AI model governance creates a very specific domain unlikely to conflict with other skills.	3 / 3
	Total	12 / 12 Passed

Implementation

87%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

This is a well-crafted skill that efficiently covers Azure AI Gateway configuration with concrete CLI commands, clear policy references, and excellent progressive disclosure. Its main weakness is the lack of explicit validation steps after key operations (backend creation, policy application), which would strengthen the workflow for potentially destructive or misconfigurable operations. Overall, it's a strong skill that respects Claude's intelligence while providing actionable, well-organized guidance.

Suggestions

Add validation/verification steps after key operations (e.g., after creating a backend, run `az apim backend show` to confirm; after applying policies, show how to test that the policy is active).

Dimension	Reasoning	Score
Conciseness	The content is lean and well-structured. It avoids explaining what APIM is or how AI gateways work conceptually—it jumps straight to actionable tables, commands, and references. Every section earns its place.	3 / 3
Actionability	Provides concrete, executable az CLI commands and curl examples with clear placeholders. The 'Add AI Backend' section gives a complete three-step workflow with real commands. The quick reference table maps policies to purposes with links to details.	3 / 3
Workflow Clarity	The 'Apply AI Governance Policy' section provides a clear recommended ordering, and 'Add AI Backend' has sequenced steps. However, there are no explicit validation checkpoints or feedback loops—after creating a backend or applying policies, there's no 'verify it worked' step before proceeding. The troubleshooting table partially compensates but is reactive rather than preventive.	2 / 3
Progressive Disclosure	Excellent structure: concise overview with quick reference table, then well-signaled one-level-deep references to policies.md, patterns.md, troubleshooting.md, and SDK references. Content is appropriately split between the overview and detailed reference files.	3 / 3
	Total	11 / 12 Passed

Validation

100%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 11 / 11 Passed

Validation for skill structure

No warnings or errors.

Repository: microsoft/github-copilot-for-azure
Commit: 915f809

Reviewed: about 10 hours ago

Table of Contents

Discovery Implementation Validation

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