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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.

72

Quality

91%

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

93%Weight 40%Scale 1-5

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

An exemplary SKILL.md body: token-efficient, fully actionable commands, verified one-level-deep references, and good error-recovery guidance. The only meaningful gap is that validation/testing steps are not explicitly sequenced into the backend-creation workflow.

DimensionReasoningScore

Conciseness

The body is lean: trigger table, policy quick-reference table, copy-paste az/curl commands, and short pointers to reference files. No concept explanations or padding; every section earns its place. Not below 5 since nothing explains concepts Claude already knows and nothing could be cut without losing substance.

5 / 5

Actionability

Commands are copy-paste ready once placeholders are filled: "az apim show --name <apim-name> --query \"gatewayUrl\" -o tsv", a complete curl call with pinned api-version, and a backend-creation flow with RBAC grant. The common cases (test endpoint, add backend, apply policy) are covered, with policy XML appropriately deferred to references/policies.md.

5 / 5

Workflow Clarity

"Add AI Backend" gives a sequenced discover -> create -> grant flow and the policy section gives a numbered order, with a troubleshooting table for error recovery. Validation checkpoints are implicit rather than sequenced (e.g. no explicit 'test the endpoint after creating the backend' step tying the Test AI Endpoint section into the workflow), which fits anchor 4 rather than 5. Not 3: sequences and recovery guidance are clearly present.

4 / 5

Progressive Disclosure

Clear overview with one-level-deep, well-signaled references (references/policies.md, references/patterns.md, references/troubleshooting.md, four SDK quick references), all verified to exist with matching heading anchors (e.g. policies.md#semantic-caching, patterns.md#pattern-1-add-ai-model-backend). Bulk detail is correctly split out of SKILL.md, making navigation easy.

5 / 5

Total

19

/

20

Passed

Description

87%Weight 40%Scale 1-5

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

A strong description: third-person 'what' statement, explicit WHEN trigger guidance, and a dense list of natural trigger phrases. Minor improvement is possible by moving capability breadth into the descriptive sentence and adding a few common synonyms such as "APIM".

DimensionReasoningScore

Specificity

"Configure Azure API Management as an AI Gateway for AI models, MCP tools, and agents" names the domain plus several concrete capabilities ("semantic caching", "content safety", "load balancing", "jailbreak detection"), with minor gaps since the breadth sits in the WHEN list rather than the capability statement. It is above anchor 3 (which expects only 1-2 concrete actions) but below anchor 5 because the 'what' sentence itself describes one configuring action rather than a comprehensive list of capabilities.

4 / 5

Completeness

Explicitly answers both 'what' ("Configure Azure API Management as an AI Gateway for AI models, MCP tools, and agents") and 'when' via an explicit "WHEN:" clause with concrete trigger phrases, matching the anchor-5 example structure. A missing 'Use when...' clause would cap this at 3, but the WHEN clause is present and specific.

5 / 5

Trigger Term Quality

The WHEN clause covers natural phrases users would actually say: "semantic caching", "token limit", "content safety", "add Azure OpenAI backend", "test AI gateway", "convert API to MCP", "import OpenAPI to gateway". Good keyword coverage with a few natural synonyms missing (e.g. "APIM", "rate limit my API"), which keeps it below anchor 5's comprehensive synonym/extension coverage.

4 / 5

Distinctiveness Conflict Risk

A clear niche (Azure API Management as AI Gateway) with distinct triggers like "jailbreak detection", "MCP rate limiting", and "add AI Foundry model"; minimal conflict risk with other skills since triggers are Azure-gateway-specific. Generic terms like "load balancing" are scoped by the Azure APIM framing.

5 / 5

Total

18

/

20

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.

Validation — 14 / 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/GitHub-Copilot-for-Azure
Reviewed

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