skills/azure-anomaly-detector/SKILL.md
Expert knowledge for Azure AI Anomaly Detector development including troubleshooting, best practices, limits & quotas, configuration, and deployment. Use when running Anomaly Detector in containers, tuning uni/multivariate APIs, handling data limits, or fixing API errors, and other Azure AI Anomaly Detector related development tasks. Not for Azure AI Metrics Advisor (use azure-metrics-advisor), Azure Monitor (use azure-monitor), Azure Machine Learning (use azure-machine-learning).
npx skillsauth add microsoftdocs/agent-skills azure-anomaly-detectorInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill provides expert guidance for Azure AI Anomaly Detector. Covers troubleshooting, best practices, limits & quotas, configuration, 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 | L33-L38 | Diagnosing and fixing Azure Anomaly Detector issues, including multivariate API error codes, configuration problems, data/format issues, and common service or performance failures. | | Best Practices | L39-L44 | Guidance on preparing data, tuning parameters, interpreting results, and designing workflows for effective use of univariate and multivariate Azure Anomaly Detector APIs. | | Limits & Quotas | L45-L49 | Service limits for Anomaly Detector: max data points, series length, request rates, model constraints, and how quotas affect API usage and scaling. | | Configuration | L50-L54 | How to configure and tune Anomaly Detector Docker containers, including environment variables, resource limits, logging, networking, and runtime behavior settings. | | Deployment | L55-L58 | How to package and run Anomaly Detector in containers: Docker setup, Azure Container Instances deployment, and IoT Edge module deployment and configuration. |
| Topic | URL | |-------|-----| | Troubleshoot Multivariate Anomaly Detector error codes | https://learn.microsoft.com/en-us/azure/ai-services/anomaly-detector/concepts/troubleshoot | | Diagnose and resolve Azure Anomaly Detector issues | https://learn.microsoft.com/en-us/azure/ai-services/anomaly-detector/faq |
| Topic | URL | |-------|-----| | Apply univariate Anomaly Detector API best practices | https://learn.microsoft.com/en-us/azure/ai-services/anomaly-detector/concepts/anomaly-detection-best-practices | | Use multivariate Anomaly Detector API effectively | https://learn.microsoft.com/en-us/azure/ai-services/anomaly-detector/concepts/best-practices-multivariate |
| Topic | URL | |-------|-----| | Review Azure Anomaly Detector service limits and quotas | https://learn.microsoft.com/en-us/azure/ai-services/anomaly-detector/service-limits |
| Topic | URL | |-------|-----| | Configure Anomaly Detector container runtime settings | https://learn.microsoft.com/en-us/azure/ai-services/anomaly-detector/anomaly-detector-container-configuration |
| Topic | URL | |-------|-----| | Deploy and run Anomaly Detector Docker containers | https://learn.microsoft.com/en-us/azure/ai-services/anomaly-detector/anomaly-detector-container-howto |
tools
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, model router patterns, MCP tools, private networking, or eval workflows, and other Microsoft Foundry related development tasks. Not for Microsoft Foundry Classic (use microsoft-foundry-classic), Microsoft Foundry Local (use microsoft-foundry-local), Microsoft Foundry Tools (use microsoft-foundry-tools).
tools
Expert knowledge for Microsoft Foundry Local (aka Azure AI Foundry Local) development including troubleshooting, decision making, configuration, and integrations & coding patterns. Use when calling Foundry Local REST/chat APIs, tools, transcription, LangChain apps, Olive HF compilation, or CLI, and other Microsoft Foundry Local related development tasks. Not for Microsoft Foundry (use microsoft-foundry), Microsoft Foundry Classic (use microsoft-foundry-classic), Microsoft Foundry Tools (use microsoft-foundry-tools), Azure Local (use azure-local).
tools
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, configuring model routing, securing VNets/Private Link, integrating tools/SDKs, or deploying hubs, and other Microsoft Foundry Classic related development tasks. Not for Microsoft Foundry (use microsoft-foundry), Microsoft Foundry Local (use microsoft-foundry-local), Microsoft Foundry Tools (use microsoft-foundry-tools).
development
Expert guidance for designing, assessing, and optimizing Azure workloads using Azure Well Architected. Covers design review checklists, recommendations, design principles, tradeoffs, service guides, workload patterns, and assessment questions. Use when designing AI, HPC, SaaS, AVD, or mission-critical workloads with WAF-aligned Azure patterns and guidance, and other Azure Well Architected related development tasks.