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Expert knowledge for Azure AI Anomaly Detector development including troubleshooting, best practices, limits & quotas, configuration, and deployment.
Passed 13 of 18 test cases with the skill, and 4 of 18 without it.
and 10 more in the full report
11 fixed5 passed either way7 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 11 test cases the skill fixed. Same model, same prompt, with and without the skill.
When fetching Azure Anomaly Detector documentation via fetch_webpage as a fallback, what format parameter must be supplied?
| Checked | Without skill | With skill |
|---|---|---|
| The response specifies accept=text/markdown for the fallback webpage fetch. | Missed | Passed |
The instructions your model reads when the skill loads.
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 |
test cases passed, vs 4 / 18 without
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