azure-ai

Use for Azure AI: Search, Speech, OpenAI, Document Intelligence. Helps with search, vector/hybrid search, speech-to-text, text-to-speech, transcription, OCR. WHEN: AI Search, query search, vector search, hybrid search, semantic search, speech-to-text, text-to-speech, transcribe, OCR, convert text to speech.

Quality

77%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./plugin/skills/azure-ai/SKILL.md

Quality

Discovery

82%

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 solid skill description that clearly identifies its Azure AI domain and provides explicit trigger terms via a 'WHEN:' clause. Its main weakness is that the capabilities listed are more like feature categories than concrete actions, and some generic terms (OCR, search) could cause overlap with non-Azure skills. The trigger term coverage is strong with good variation across the Azure AI service areas.

Suggestions

Make capabilities more concrete by specifying actions like 'configure Azure AI Search indexes', 'transcribe audio files using Azure Speech Services', or 'extract text from documents using Azure Document Intelligence'.

Add Azure-specific qualifiers to generic terms to reduce conflict risk, e.g., 'Azure OCR via Document Intelligence' instead of just 'OCR'.

Dimension	Reasoning	Score
Specificity	The description names the domain (Azure AI services) and lists several actions like search, speech-to-text, text-to-speech, transcription, and OCR, but these are more like feature areas than concrete specific actions (e.g., it doesn't say 'configure an Azure AI Search index' or 'transcribe audio files').	2 / 3
Completeness	The description clearly answers both 'what' (Azure AI services for search, speech, document intelligence) and 'when' with an explicit 'WHEN:' clause listing specific trigger scenarios. This satisfies the requirement for explicit trigger guidance.	3 / 3
Trigger Term Quality	Excellent coverage of natural trigger terms users would say: 'vector search', 'hybrid search', 'semantic search', 'speech-to-text', 'text-to-speech', 'transcribe', 'OCR', 'AI Search'. These are terms users would naturally use when needing this skill.	3 / 3
Distinctiveness Conflict Risk	While Azure-specific terms help distinguish it, generic terms like 'OCR', 'speech-to-text', and 'search' could overlap with non-Azure skills that handle similar capabilities (e.g., a general OCR skill or a generic search skill). The Azure branding helps but doesn't fully eliminate conflict risk.	2 / 3
	Total	10 / 12 Passed

Implementation

72%

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-organized reference skill that efficiently maps Azure AI services to their tools, capabilities, and SDK references. Its main strength is concise tabular organization and clear progressive disclosure to SDK guides. Its weakness is the lack of executable code examples or concrete workflow steps showing how to actually use the MCP tools or SDKs with parameters and expected outputs.

Suggestions

Add at least one concrete MCP tool invocation example with parameters (e.g., a search_query call with index name, query text, and expected response shape)

Include a brief executable code snippet for the most common use case (e.g., vector search or speech-to-text) rather than delegating all code to reference files

Dimension	Reasoning	Score
Conciseness	The content is lean and well-structured using tables for quick reference. It avoids explaining what Azure services are or how SDKs work in general, assuming Claude's competence. Every section serves a clear purpose.	3 / 3
Actionability	The skill provides specific MCP tool commands and references to SDK guides, but lacks executable code examples. The MCP commands are named but don't show parameter usage or example invocations with expected outputs. SDK references are delegated entirely to external files.	2 / 3
Workflow Clarity	There's a clear fallback path (MCP preferred, then '/azure:setup'), and the decision table for which service to use is helpful. However, there are no multi-step workflows with validation checkpoints — for example, no guidance on verifying search index creation succeeded or handling speech transcription errors.	2 / 3
Progressive Disclosure	Excellent structure with a concise overview in the main file and well-signaled one-level-deep references to SDK guides across multiple languages and services. The references section is clearly organized by service and language, making navigation easy.	3 / 3
	Total	10 / 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 9 hours ago

Table of Contents

Discovery Implementation Validation

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