> azure-language-service
Expert knowledge for Azure AI Language development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building CLU, custom NER, text classification, CQA, sentiment, summarization, or health workloads, and other Azure AI Language related development tasks. Not for Azure AI Search (use azure-cognitive-search), Azure AI Speech (use azure-speech), Azure Translat
curl "https://skillshub.wtf/MicrosoftDocs/Agent-Skills/azure-language-service?format=md"Azure AI Language Skill
This skill provides expert guidance for Azure AI Language. Covers troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. It combines local quick-reference content with remote documentation fetching capabilities.
How to Use This Skill
IMPORTANT for Agent: Use the Category Index below to locate relevant sections. For categories with line ranges (e.g.,
L35-L120), useread_filewith the specified lines. For categories with file links (e.g.,[security.md](security.md)), useread_fileon the linked reference file
IMPORTANT for Agent: If
metadata.generated_atis more than 3 months old, suggest the user pull the latest version from the repository. Ifmcp_microsoftdocstools are not available, suggest the user install it: Installation Guide
This skill requires network access to fetch documentation content:
- Preferred: Use
mcp_microsoftdocs:microsoft_docs_fetchwith query stringfrom=learn-agent-skill. Returns Markdown. - Fallback: Use
fetch_webpagewith query stringfrom=learn-agent-skill&accept=text/markdown. Returns Markdown.
Category Index
| Category | Lines | Description |
|---|---|---|
| Troubleshooting | L37-L42 | Diagnosing and fixing common errors, low-accuracy results, and configuration issues in custom text classification and custom question answering projects in Azure AI Language. |
| Best Practices | L43-L60 | Best practices for designing, labeling, and evaluating CLU, custom NER, text classification, and CQA projects, including multilingual handling, emojis, schemas, and autolabeling. |
| Decision Making | L61-L68 | Guidance on Azure Language lifecycle policies, choosing resources for conversational QA, and when/how to migrate from LUIS, QnA Maker, or Text Analytics to Azure Language API |
| Architecture & Design Patterns | L69-L75 | Architectural guidance for CLU and custom text classification: choosing CLU vs orchestration workflows, and designing regional backup, redundancy, and failover strategies. |
| Limits & Quotas | L76-L94 | Limits, quotas, and regional/language support for Azure AI Language features (CLU, NER, PII, CQA, containers), including data, rate, throughput, and job constraints. |
| Security | L95-L104 | Security for Azure AI Language: encryption at rest, customer-managed keys, RBAC, managed identities, SAS tokens, and network isolation/Private Link for CQA resources. |
| Configuration | L105-L131 | Configuring Azure AI Language projects and containers: CLU, NER, text classification, CQA, sentiment, summarization, and health—data formats, training, metrics, resources, and runtime options. |
| Integrations & Coding Patterns | L132-L163 | How to call Azure Language/CLU/Health/Summarization/CQA APIs and SDKs, wire them into bots, Power Automate, and Foundry, and correctly handle async, parameters, and outputs |
| Deployment | L164-L174 | How to deploy and run Azure AI Language models (custom classification, NER, QnA, key phrases, language detection) across regions, containers, AKS, and migrate projects/resources. |
Troubleshooting
| Topic | URL |
|---|---|
| Resolve common issues in custom text classification | https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-text-classification/faq |
| Troubleshoot common issues in custom question answering | https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/how-to/troubleshooting |
Best Practices
Decision Making
| Topic | URL |
|---|---|
| Understand Azure Language model lifecycle policies | https://learn.microsoft.com/en-us/azure/ai-services/language-service/concepts/model-lifecycle |
| Choose and manage Azure resources for CQA | https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/concepts/azure-resources |
| Decide when to migrate from LUIS or QnA Maker | https://learn.microsoft.com/en-us/azure/ai-services/language-service/reference/migrate |
| Migrate Text Analytics apps to Azure Language API | https://learn.microsoft.com/en-us/azure/ai-services/language-service/reference/migrate-language-service-latest |
Architecture & Design Patterns
| Topic | URL |
|---|---|
| Choose CLU vs orchestration workflow architecture | https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/concepts/app-architecture |
| Design CLU regional backup and failover | https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/how-to/fail-over |
| Design regional fail-over for custom text classification solutions | https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-text-classification/fail-over |
Limits & Quotas
Security
| Topic | URL |
|---|---|
| Understand Language service data-at-rest encryption | https://learn.microsoft.com/en-us/azure/ai-services/language-service/concepts/encryption-data-at-rest |
| Apply Azure RBAC to Azure Language resources | https://learn.microsoft.com/en-us/azure/ai-services/language-service/concepts/role-based-access-control |
| Use managed identities for Language Blob access | https://learn.microsoft.com/en-us/azure/ai-services/language-service/native-document-support/managed-identities |
| Create SAS tokens for Language Blob access | https://learn.microsoft.com/en-us/azure/ai-services/language-service/native-document-support/shared-access-signatures |
| Configure data-at-rest encryption and CMK for CQA | https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/how-to/encrypt-data-at-rest |
| Configure network isolation and Private Link for CQA | https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/how-to/network-isolation |
Configuration
Integrations & Coding Patterns
Deployment
> related_skills --same-repo
> microsoft-foundry
Expert knowledge for Microsoft Foundry (aka Azure AI Foundry) development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building Foundry agents with Azure OpenAI, vector search/RAG, Sora video, realtime audio, or MCP/LangChain APIs, and other Microsoft Foundry related development tasks. Not for Microsoft Foundry Classic (use microsoft-foundry-classic),
> microsoft-foundry-tools
Expert knowledge for Microsoft Foundry Tools (aka Azure AI services, Azure Cognitive Services) development including best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using Content Understanding analyzers, Content Moderator APIs, Foundry containers, VNet/Key Vault security, or Entra auth, and other Microsoft Foundry Tools related development tasks. Not for Microsoft Foundry (use micr
> microsoft-foundry-local
Expert knowledge for Microsoft Foundry Local (aka Azure AI Foundry Local) development including troubleshooting, best practices, decision making, configuration, and integrations & coding patterns. Use when using Foundry Local CLI, chat/transcription APIs, tools, OpenAI/LangChain clients, or upgrading legacy SDKs, and other Microsoft Foundry Local related development tasks. Not for Microsoft Foundry (use microsoft-foundry), Microsoft Foundry Classic (use microsoft-foundry-classic), Microsoft Foun
> microsoft-foundry-classic
Expert knowledge for Microsoft Foundry Classic (aka Azure AI Foundry classic) development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building Foundry agents with RAG, tools, evaluators, Azure OpenAI, VNet/Private Link, or CI/CD deployments, and other Microsoft Foundry Classic related development tasks. Not for Microsoft Foundry (use microsoft-foundry