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

Pending

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 domain (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 a distinct niche with minimal conflict risk. The trigger terms are comprehensive and reflect natural language practitioners would use.

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 serves as a gateway overview with strong actionability through concrete CLI commands and curl examples. The progressive disclosure is exemplary, with clear references to detailed sub-documents. The main weakness is the lack of explicit validation/verification steps in multi-step workflows like adding backends or applying policies.

Suggestions

Add a validation step after 'Add AI Backend' (e.g., `az apim backend show ...` to confirm creation, then a test curl to verify connectivity) to close the feedback loop.

Add a verification step after applying governance policies (e.g., 'Test with a request that should be blocked/cached to confirm 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, copy-paste-ready Azure CLI commands and curl examples for key tasks (getting gateway details, testing endpoints, adding backends). The troubleshooting table gives specific solutions rather than vague advice.	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.	2 / 3
Progressive Disclosure	Excellent progressive disclosure structure: the SKILL.md serves as a concise overview with well-signaled, one-level-deep references to policies.md, patterns.md, troubleshooting.md, and SDK references. Navigation is clear and organized by topic.	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: 771a666

Reviewed: 5 days ago

Table of Contents

Discovery Implementation Validation

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