From azure-agent-skills
Expert guidance for Azure AI Language: CLU, custom NER/classification, CQA, sentiment/summarization, PII/key phrase pipelines. Includes troubleshooting, best practices, and architecture.
How this skill is triggered — by the user, by Claude, or both
Slash command
/azure-agent-skills:azure-language-serviceThe summary Claude sees in its skill listing — used to decide when to auto-load this 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.
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.
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:
mcp_microsoftdocs:microsoft_docs_fetch with query string from=learn-agent-skill. Returns Markdown.fetch_webpage with query string from=learn-agent-skill&accept=text/markdown. Returns Markdown.| Category | Lines | Description |
|---|---|---|
| Troubleshooting | L37-L42 | Diagnosing and fixing common issues in custom text classification and custom question answering, including model performance, configuration, and runtime/response problems. |
| Best Practices | L43-L59 | Best practices for designing, labeling, and evaluating CLU, custom NER, text classification, and CQA projects, including multilingual handling, emojis, schemas, and autolabeling. |
| Decision Making | L60-L69 | Guidance on choosing regions and resources, lifecycle policies, and migration paths from LUIS, QnA Maker, Text Analytics, and Language Studio to Azure Language and Microsoft Foundry |
| Architecture & Design Patterns | L70-L76 | Architectural guidance for CLU and custom text classification: choosing CLU vs orchestration workflows, and designing regional backup, redundancy, and failover strategies. |
| Limits & Quotas | L77-L95 | Limits, quotas, and language/region support for Azure AI Language features (CLU, NER, PII, key phrases, QnA), including data sizes, throughput, containers, and training job constraints. |
| Security | L96-L106 | Security, encryption, and access control for Azure AI Language: RBAC, managed identities, SAS, CMK/data-at-rest, network isolation, Private Link, and CQA-specific security setup. |
| Configuration | L107-L131 | Configuring Azure AI Language/CLU/NER/CQA projects and containers, including data formats, resources, Docker/on-prem setups, metrics, confidence scores, PII redaction, and sentiment/summarization. |
| Integrations & Coding Patterns | L132-L163 | Implementing Azure AI Language features via REST/SDKs: CLU, custom NER/classification, CQA, sentiment, summarization, health, entity linking, and integrating with bots/Power Automate. |
| Deployment | L164-L173 | 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. |
| Topic | URL |
|---|---|
| Resolve common issues in custom text classification | https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-text-classification/faq |
| Diagnose and resolve custom question answering issues | https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/how-to/troubleshooting |
| Topic | URL |
|---|---|
| Understand Azure Language model lifecycle policies | https://learn.microsoft.com/en-us/azure/ai-services/language-service/concepts/model-lifecycle |
| Choose Azure regions for Language service features | https://learn.microsoft.com/en-us/azure/ai-services/language-service/concepts/regional-support |
| Migrate Azure Language Studio projects to Microsoft Foundry | https://learn.microsoft.com/en-us/azure/ai-services/language-service/migration-studio-to-foundry |
| Choose and manage Azure resources for CQA | https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/concepts/azure-resources |
| Decide migration from LUIS and QnA Maker to Azure Language | 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 |
| 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 |
npx claudepluginhub microsoftdocs/agent-skills --plugin azure-agent-skillsExpert guidance for Azure AI Document Intelligence: troubleshooting, best practices, decision-making, architecture, deployment, security, and coding patterns. Use when working with AnalyzeDocument APIs, custom models, containers, or Logic Apps/Functions.
Provides guidance on Azure OpenAI Service 2025 models like GPT-5 series, GPT-4.1, o3/o4-mini reasoning, Sora video generation, image/audio models, and Azure CLI deployment.
Uses MCP tools and SDKs for Azure AI: Search (vector/hybrid queries), Speech (STT/TTS/transcription), OpenAI models, Document Intelligence (OCR).