From azure
Expert knowledge for Azure AI services development including troubleshooting, best practices, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building, debugging, or optimizing Azure AI services applications. Not for Azure Machine Learning (use azure-machine-learning), Azure AI Search (use azure-cognitive-search), Azure AI Speech (use azure-speech), Azure AI Custom Vision (use azure-custom-vision).
npx claudepluginhub atc-net/atc-agentic-toolkit --plugin azureThis skill uses the workspace's default tool permissions.
This skill provides expert guidance for Azure AI Services. Covers troubleshooting, best practices, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. It combines local quick-reference content with remote documentation fetching capabilities.
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.
Deploys, evaluates, and manages Microsoft Foundry AI agents: Docker builds to ACR, hosted agent creation, batch evals, prompt optimization, tracing, troubleshooting.
Guides Payload CMS config (payload.config.ts), collections, fields, hooks, access control, APIs. Debugs validation errors, security, relationships, queries, transactions, hook behavior.
Share bugs, ideas, or general feedback.
This skill provides expert guidance for Azure AI Services. Covers troubleshooting, best practices, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. It combines local quick-reference content with remote documentation fetching capabilities.
IMPORTANT for Agent: This file may be large. Use the Category Index below to locate relevant sections, then use
read_filewith specific line ranges (e.g.,L136-L144) to read the sections needed for the user's question This skill requires network access to fetch documentation content. Usemcp_microsoftdocs:microsoft_docs_fetchto retrieve full articles.
WebFetch tool if the Microsoft Learn MCP server is not available.| Category | Lines | Description |
|---|---|---|
| Troubleshooting | L36-L40 | Diagnosing and fixing common Content Understanding issues, including model errors, data ingestion problems, configuration mistakes, and troubleshooting steps for failed analyses. |
| Best Practices | L41-L46 | Best practices for Azure AI Content Understanding: designing extraction workflows, tuning models, improving document parsing accuracy, and handling complex or low‑quality documents. |
| Decision Making | L47-L56 | Guidance on choosing pricing tiers, comparing Content Understanding vs Document Intelligence vs LLMs, selecting standard vs pro modes, Foundry vs Studio, migration steps, and cost estimation. |
| Limits & Quotas | L57-L64 | Rate limits, quotas, and list-size limits for Foundry autoscale and Content Moderator (image/term lists), plus service quotas for Content Understanding. |
| Security | L65-L80 | Securing Azure AI/Foundry: auth (Entra, keys, Key Vault), encryption (CMK, data-at-rest), DLP for outbound calls, VNet rules, policy-based governance, and secure analyzer access. |
| Configuration | L81-L99 | Configuring Foundry endpoints, credentials, containers, logging, and Content Understanding analyzers (classification, layout, audiovisual), routing, outputs, and resource recovery/purge. |
| Integrations & Coding Patterns | L100-L110 | Using Azure Content Moderator and Content Understanding via REST/.NET: text/image/video moderation, custom term lists, and building custom multimodal analyzers and workflows. |
| Deployment | L111-L118 | How to package and run Foundry tools/containers on Azure (ACI, Docker Compose, disconnected), and deploy Foundry resources using Azure AI containers and ARM templates |
| Topic | URL |
|---|---|
| Resolve common issues with Content Understanding | https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/faq |
| Topic | URL |
|---|---|
| Apply best practices for Content Understanding workloads | https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/concepts/best-practices |
| Improve Content Understanding document extraction quality | https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/document/analyzer-improvement |
| Topic | URL |
|---|---|
| Choose and use Foundry commitment tier pricing | https://learn.microsoft.com/en-us/azure/ai-services/commitment-tier |
| Choose between Content Understanding, Document Intelligence, and LLMs | https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/choosing-right-ai-tool |
| Choose between standard and pro modes in Content Understanding | https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/concepts/standard-pro-modes |
| Compare Content Understanding in Foundry vs Studio | https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/foundry-vs-content-understanding-studio |
| Migrate Content Understanding analyzers from preview to GA | https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/how-to/migration-preview-to-ga |
| Estimate and plan costs for Content Understanding | https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/pricing-explainer |
| Topic | URL |
|---|---|
| Use autoscale to increase Foundry rate limits | https://learn.microsoft.com/en-us/azure/ai-services/autoscale |
| Use Content Moderator image lists within quota limits | https://learn.microsoft.com/en-us/azure/ai-services/content-moderator/image-lists-quickstart-dotnet |
| Understand Content Moderator image and term list limits | https://learn.microsoft.com/en-us/azure/ai-services/content-moderator/samples-dotnet |
| Service quotas and limits for Content Understanding | https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/service-limits |
| Topic | URL |
|---|---|
| Run Foundry Tools using Azure AI containers | https://learn.microsoft.com/en-us/azure/ai-services/cognitive-services-container-support |
| Deploy Foundry containers to Azure Container Instances | https://learn.microsoft.com/en-us/azure/ai-services/containers/azure-container-instance-recipe |
| Run Foundry containers in disconnected environments | https://learn.microsoft.com/en-us/azure/ai-services/containers/disconnected-containers |
| Deploy multiple Azure AI containers with Docker Compose | https://learn.microsoft.com/en-us/azure/ai-services/containers/docker-compose-recipe |
| Deploy Foundry resources using ARM templates | https://learn.microsoft.com/en-us/azure/ai-services/create-account-resource-manager-template |