| name | azure-anomaly-detector |
| description | 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). |
| compatibility | Requires network access. Uses mcp_microsoftdocs:microsoft_docs_fetch or fetch_webpage to retrieve documentation. |
| metadata | {"generated_at":"2026-07-19","generator":"docs2skills/1.0.0"} |
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
Best Practices
Limits & Quotas
Configuration
Deployment
