Azure AI Anomaly Detector Skill
This skill provides expert guidance for Azure AI Anomaly Detector. Covers troubleshooting, best practices, architecture & design patterns, 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 | L34-L39 | Diagnosing and fixing Anomaly Detector issues, including multivariate API error codes, model training/detection failures, data format problems, and common service or configuration errors. |
| Best Practices | L40-L45 | Guidance on preparing data, tuning parameters, interpreting results, and designing workflows for effective use of univariate and multivariate Azure Anomaly Detector APIs. |
| Architecture & Design Patterns | L46-L50 | Designing predictive maintenance solutions using Multivariate Anomaly Detector, including data preparation, model setup, and architecture patterns for monitoring complex equipment. |
| Limits & Quotas | L51-L56 | Details on Anomaly Detector regional endpoints, usage constraints, request/throughput limits, quotas, and how these caps affect model training and inference. |
| Configuration | L57-L61 | How to configure and tune Anomaly Detector Docker containers, including environment variables, resource limits, logging, networking, and runtime behavior settings. |
| Deployment | L62-L67 | How to package and run Anomaly Detector in containers: Docker setup, Azure Container Instances deployment, and IoT Edge module deployment and configuration. |
Troubleshooting
Best Practices
Architecture & Design Patterns
Limits & Quotas
Configuration
Deployment
