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MicrosoftDocs/azure-anomaly-detector

MicrosoftDocs

azure-anomaly-detector

Expert knowledge for Azure AI Anomaly Detector development including troubleshooting, best practices, limits & quotas, configuration, and deployment. Use when tuning Docker-based Anomaly Detector, ACI or IoT Edge deployments, univariate/multivariate APIs, or service limits, 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).

globalRequires network access. Uses mcp_microsoftdocs:microsoft_docs_fetch or fetch_webpage to retrieve documentation.
generated_at:2026-06-07
generator:docs2skills/1.0.0
New~1.0k
v1.0Saved Jun 26, 2026

Azure AI Anomaly Detector 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.

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), use read_file with the specified lines. For categories with file links (e.g., [security.md](security.md)), use read_file on the linked reference file

IMPORTANT for Agent: If metadata.generated_at is more than 3 months old, suggest the user pull the latest version from the repository. If mcp_microsoftdocs tools 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_fetch with query string from=learn-agent-skill. Returns Markdown.
  • Fallback: Use fetch_webpage with query string from=learn-agent-skill&accept=text/markdown. Returns Markdown.

Category Index

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.

Troubleshooting

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

Best Practices

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

Limits & Quotas

Topic URL
Review Azure Anomaly Detector service limits and quotas https://learn.microsoft.com/en-us/azure/ai-services/anomaly-detector/service-limits

Configuration

Topic URL
Configure Anomaly Detector container runtime settings https://learn.microsoft.com/en-us/azure/ai-services/anomaly-detector/anomaly-detector-container-configuration

Deployment

Topic URL
Deploy and run Anomaly Detector Docker containers https://learn.microsoft.com/en-us/azure/ai-services/anomaly-detector/anomaly-detector-container-howto
Files1
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Overall Score

65/100

Grade

C

Adequate

Safety

75

Quality

60

Clarity

72

Completeness

52

Summary

This skill provides expert guidance for Azure AI Anomaly Detector, covering troubleshooting, best practices, limits & quotas, configuration, and deployment scenarios. It serves as a reference index to Microsoft Learn documentation with optional MCP-based remote fetching capabilities, enabling agents to troubleshoot issues, understand service constraints, and deploy containerized Anomaly Detector instances.

Detected Capabilities

documentation fetching (via mcp_microsoftdocs or fetch_webpage)network access for remote documentation retrievalmarkdown output parsing

Trigger Keywords

Phrases that MCP clients use to match this skill to user intent.

anomaly detector troubleshootazure anomaly detectionanomaly detector dockeranomaly detector quotasmultivariate anomaly analysis

Risk Signals

INFO

Outbound network request to learn.microsoft.com for documentation fetching

SKILL.md - Lines: fetch capabilities section
INFO

Conditional suggestion to install external MCP tool if unavailable

SKILL.md - How to Use This Skill section
INFO

Metadata staleness check (3-month threshold) with version upgrade suggestion

SKILL.md - How to Use This Skill section

Referenced Domains

External domains referenced in skill content, detected by static analysis.

github.comlearn.microsoft.com

Use Cases

  • Troubleshoot Anomaly Detector error codes and diagnose multivariate API failures
  • Review service limits and quota constraints for API request rates and data processing
  • Configure Docker containers with environment variables, resource limits, and logging
  • Deploy Anomaly Detector to Azure Container Instances (ACI) or IoT Edge
  • Learn best practices for univariate and multivariate anomaly detection workflows

Quality Notes

  • Strength: Clear category index with line ranges and descriptions helps agents locate content efficiently
  • Strength: Explicit handling of missing tools with fallback instructions and installation guidance
  • Strength: Metadata staleness detection shows awareness of documentation drift
  • Limitation: Skill body content appears to be a template/stub—no actual instructional content under the category headings (lines 33–58 are not shown)
  • Limitation: Line number references (L33-L38, etc.) cannot be validated since body content is absent or truncated
  • Limitation: No edge case guidance (e.g., handling network failures, timeout behavior, API rate limits during fetch)
  • Limitation: No examples of successful troubleshooting workflows or common error patterns
  • Limitation: Scope distinction from azure-metrics-advisor and azure-monitor is mentioned but not reinforced with examples
Model: claude-haiku-4-5-20251001Analyzed: Jun 26, 2026

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MicrosoftDocs/azure-anomaly-detector | SkillRepo