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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.

74

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.

The body is concise, highly actionable, and well-structured with appropriate progressive disclosure. Its main weakness is the absence of explicit validation checkpoints in the backend-provisioning workflow.

Suggestions

Add an explicit validation step after backend creation (e.g. az apim backend show / a test call) with a fix-and-retry loop, since provisioning AI backends is a batch/destructive-style operation that warrants a feedback checkpoint.

Fix the broken anchor in references/patterns.md#pattern-1-add-ai-model-backend — the actual heading is 'Pattern 1: Add AI Model Backend', so the slug does not resolve.

DimensionReasoningScore

Conciseness

Lean, assumes Claude's competence with no conceptual padding, and every section delivers executable value; the trigger table lightly echoes the frontmatter but stays within budget.

3 / 3

Actionability

Provides fully executable az CLI and curl commands with real flags and placeholders (e.g. az apim backend create, role assignment create, the curl chat completions call) that are copy-paste ready.

3 / 3

Workflow Clarity

Policy order is sequenced and the Add AI Backend / Apply Governance sections reference patterns, but the multi-step backend provisioning workflow lacks explicit validation checkpoints or a validate-fix-retry loop for risky operations.

2 / 3

Progressive Disclosure

SKILL.md is a clear overview with one-level-deep references to real files (policies.md, patterns.md, troubleshooting.md, sdk/*) that are well signaled and navigable.

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.

The description is specific, trigger-rich, and clearly signals both purpose and invocation conditions. It is concise yet comprehensive for the AI gateway governance domain.

DimensionReasoningScore

Specificity

Lists multiple concrete actions ("Configure Azure API Management as an AI Gateway", "add Azure OpenAI backend", "add AI Foundry model", "configure AI backend", "convert API to MCP", "test AI gateway") rather than vague abstractions.

3 / 3

Completeness

Clearly answers what (configure APIM as an AI Gateway for models, MCP tools, and agents) and when via an explicit WHEN: clause listing concrete triggers.

3 / 3

Trigger Term Quality

Covers many natural terms a user would say ("semantic caching", "token limit", "content safety", "load balancing", "jailbreak detection", "test AI gateway", "AI cost control") with broad, realistic phrasing.

3 / 3

Distinctiveness Conflict Risk

Has a clear niche (Azure API Management AI Gateway governance) with triggers specific to APIM/AI gateway work, unlikely to be selected for unrelated skills.

3 / 3

Total

12

/

12

Passed

Validation

87%

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

Validation14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

relative_links

Relative link issues: 4 deeper-than-1-level

Warning

referenced_paths_exist

Referenced path issues: 4 deeper-than-1-level

Warning

Total

14

/

16

Passed

Repository
microsoft/azure-skills
Reviewed

Table of Contents

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