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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.

76

Quality

93%

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Advisory

Suggest reviewing before use

SKILL.md
Quality
Evals
Security

Quality

Content

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-structured skill that efficiently covers Azure AI Gateway configuration with concrete CLI commands, clear tables, and excellent progressive disclosure to reference files. The main weakness is the lack of explicit validation checkpoints after destructive or configuration operations like backend creation and policy application. The 'When to Use' trigger table and quick reference policy table are particularly effective for discoverability.

Suggestions

Add validation steps after backend creation (e.g., verify connectivity with a test call) and after policy application (e.g., check policy status or run a test request to confirm the policy is active).

Include a brief feedback loop in the 'Add AI Backend' section, such as 'Verify: az apim backend show ... to confirm creation, then test with curl to validate connectivity.'

DimensionReasoningScore

Conciseness

The content is lean and efficient. It avoids explaining what APIM is or how AI models work, assumes Claude's competence, and uses tables and code blocks to convey information densely. Every section serves a clear purpose without padding.

3 / 3

Actionability

Provides concrete, executable Azure CLI commands for discovering resources, creating backends, granting roles, and testing endpoints. The curl example for testing is copy-paste ready with clear placeholder conventions.

3 / 3

Workflow Clarity

The 'Apply AI Governance Policy' section provides a clear recommended ordering but lacks explicit validation/verification steps after applying policies or adding backends. For operations involving backend configuration and policy application, there are no feedback loops (e.g., validate policy XML, verify backend connectivity after creation).

2 / 3

Progressive Disclosure

Excellent structure with a concise overview in SKILL.md and well-signaled one-level-deep references to policies.md, patterns.md, troubleshooting.md, and SDK references. The quick reference table links directly to specific sections in reference files, making navigation easy.

3 / 3

Total

11

/

12

Passed

Description

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 concise yet comprehensive, covering both the 'what' and 'when' effectively with natural keywords that practitioners would use. It occupies a distinct niche that minimizes conflict risk with other skills.

DimensionReasoningScore

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

Validation

100%

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

Validation11 / 11 Passed

Validation for skill structure

No warnings or errors.

Repository
microsoft/github-copilot-for-azure
Reviewed

Table of Contents

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