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

Fix and improve this skill with Tessl

tessl review fix ./plugin/skills/azure-ai/SKILL.md

Quality

Content

72%

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

This skill is well-organized as a reference/routing document for Azure AI services, with excellent progressive disclosure and concise presentation. Its main weakness is the lack of executable code examples or concrete usage patterns — it tells Claude which tools and SDKs exist but doesn't show how to use them directly. Adding even minimal executable examples for the most common operations (e.g., a search query, a transcription call) would significantly improve actionability.

Suggestions

Add at least one executable code example per major service (AI Search query, Speech transcription) showing concrete parameter usage rather than just listing command names.

For MCP tool commands, show example parameters/arguments (e.g., `search_query` with index name, query text, and search mode) so Claude knows exactly how to invoke them.

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 they work conceptually, 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 parameters or usage patterns. There's no concrete code snippet for any of the services (e.g., how to construct a vector search query or transcribe audio).	2 / 3
Workflow Clarity	There's a clear fallback path (MCP preferred, then '/azure:setup'), and the table-based organization makes service selection straightforward. However, there are no multi-step workflows with validation checkpoints for operations like setting up search indexes, running hybrid queries, or handling transcription pipelines.	2 / 3
Progressive Disclosure	Excellent progressive disclosure structure: the SKILL.md provides a concise overview with well-organized tables, then clearly signals one-level-deep references to SDK guides across multiple languages and links to external documentation. Navigation is easy and references are clearly labeled.	3 / 3
	Total	10 / 12 Passed

Description

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 trigger terms (OCR, search) are generic enough to potentially conflict with non-Azure skills. The description is functional and would generally enable correct skill selection in most contexts.

Suggestions

Make capabilities more concrete by specifying actions like 'configure search indexes', 'transcribe audio files', 'extract text from scanned documents' rather than listing feature areas.

Add 'Azure' as a qualifier to generic terms in the WHEN clause (e.g., 'Azure OCR', 'Azure speech') to reduce conflict risk with non-Azure skills that handle similar tasks.

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, terms like 'OCR', 'speech-to-text', and 'search' are quite generic and 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

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: d4563f6

Reviewed: 3 days ago

Table of Contents

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