From azure-agent-skills
Provides expert guidance for Azure AI Anomaly Detector: troubleshooting, best practices, limits & quotas, configuration, and deployment for Docker, ACI, and IoT Edge.
How this skill is triggered — by the user, by Claude, or both
Slash command
/azure-agent-skills:azure-anomaly-detectorThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
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.
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 error codes, common failures, configuration problems, and step-by-step troubleshooting guidance. |
| 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 |
npx claudepluginhub microsoftdocs/agent-skills --plugin azure-agent-skillsGuides Azure AI Metrics Advisor development: configuring data feeds, tuning anomaly detection, managing alerts/hooks, and calling REST/SDKs.
Investigates Azure operational health using Monitor, Log Analytics, Application Insights, KQL triage, alert rules, workbooks, and telemetry-gap analysis for incident and posture investigations.
Provides systematic diagnostic flows for Azure services: App Service, Functions, AKS, Container Apps, Event Hubs, and Service Bus using AppLens and Azure Monitor.