azure-ai▌
microsoft/azure-skills · updated Apr 8, 2026
MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.
Access Azure AI Search, Speech, OpenAI, and Document Intelligence services through unified MCP tools.
- ›AI Search supports full-text, vector, hybrid, and semantic search with built-in AI enrichment for entity extraction and OCR
- ›Speech service handles real-time and batch speech-to-text transcription, text-to-speech synthesis with neural voices, and speaker diarization
- ›MCP tools provide direct access: azure__search for index queries and azure__speech for transcription and synthesis
- ›SD
Azure AI Services
Services
| Service | Use When | MCP Tools | CLI |
|---|---|---|---|
| AI Search | Full-text, vector, hybrid search | azure__search |
az search |
| Speech | Speech-to-text, text-to-speech | azure__speech |
- |
| OpenAI | GPT models, embeddings, DALL-E | - | az cognitiveservices |
| Document Intelligence | Form extraction, OCR | - | - |
MCP Server (Preferred)
When Azure MCP is enabled:
AI Search
azure__searchwith commandsearch_index_list- List search indexesazure__searchwith commandsearch_index_get- Get index detailsazure__searchwith commandsearch_query- Query search index
Speech
azure__speechwith commandspeech_transcribe- Speech to textazure__speechwith commandspeech_synthesize- Text to speech
If Azure MCP is not enabled: Run /azure:setup or enable via /mcp.
AI Search Capabilities
| Feature | Description |
|---|---|
| Full-text search | Linguistic analysis, stemming |
| Vector search | Semantic similarity with embeddings |
| Hybrid search | Combined keyword + vector |
| AI enrichment | Entity extraction, OCR, sentiment |
Speech Capabilities
| Feature | Description |
|---|---|
| Speech-to-text | Real-time and batch transcription |
| Text-to-speech | Neural voices, SSML support |
| Speaker diarization | Identify who spoke when |
| Custom models | Domain-specific vocabulary |
SDK Quick References
For programmatic access to these services, see the condensed SDK guides:
- AI Search: Python | TypeScript | .NET
- OpenAI: .NET
- Vision: Python | Java
- Transcription: Python
- Translation: Python | TypeScript
- Document Intelligence: .NET | TypeScript
- Content Safety: Python | TypeScript | Java
Service Details
For deep documentation on specific services:
- AI Search indexing and queries -> Azure AI Search documentation
- Speech transcription patterns -> Azure AI Speech documentation
How to use azure-ai on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add azure-ai
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches azure-ai from GitHub repository microsoft/azure-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate azure-ai. Access the skill through slash commands (e.g., /azure-ai) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★70 reviews- ★★★★★Fatima Ramirez· Dec 28, 2024
We added azure-ai from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Fatima White· Dec 20, 2024
I recommend azure-ai for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Li Sanchez· Dec 20, 2024
azure-ai is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Dhruvi Jain· Dec 16, 2024
azure-ai has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Diego Haddad· Dec 16, 2024
Solid pick for teams standardizing on skills: azure-ai is focused, and the summary matches what you get after install.
- ★★★★★Diya Farah· Dec 16, 2024
azure-ai has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Zaid Torres· Dec 12, 2024
Keeps context tight: azure-ai is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Kwame Iyer· Nov 19, 2024
azure-ai fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Diego Khan· Nov 11, 2024
Useful defaults in azure-ai — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Oshnikdeep· Nov 7, 2024
Keeps context tight: azure-ai is the kind of skill you can hand to a new teammate without a long onboarding doc.
showing 1-10 of 70