skills/azure-personalizer/SKILL.md
Expert knowledge for Azure AI Personalizer development including troubleshooting, decision making, security, configuration, and integrations & coding patterns. Use when choosing single vs multi-slot, tuning exploration policies, configuring CMK encryption, debugging low rewards, or using local inference SDK, and other Azure AI Personalizer related development tasks. Not for Azure AI Metrics Advisor (use azure-metrics-advisor), Azure AI Anomaly Detector (use azure-anomaly-detector), Azure Machine Learning (use azure-machine-learning).
npx skillsauth add microsoftdocs/agent-skills azure-personalizerInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill provides expert guidance for Azure AI Personalizer. Covers troubleshooting, decision making, security, configuration, and integrations & coding patterns. 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-L37 | Diagnosing and fixing common Azure Personalizer problems: configuration and training issues, API/latency errors, low reward performance, and steps to debug and resolve service failures. | | Decision Making | L38-L42 | Guidance on when to use single-slot vs multi-slot Personalizer, comparing scenarios, behavior, and design tradeoffs for different personalization needs. | | Security | L43-L48 | Configuring encryption at rest (including customer-managed keys) and controlling data collection, storage, and privacy settings for Azure Personalizer. | | Configuration | L49-L56 | Configuring Personalizer’s learning behavior: policies, hyperparameters, exploration, apprentice mode, explainability, model export, and learning loop settings. | | Integrations & Coding Patterns | L57-L60 | Using the Personalizer local inference SDK for low-latency, offline/edge scenarios, including setup, integration patterns, and best practices for calling the model locally. |
| Topic | URL | |-------|-----| | Diagnose and resolve common Azure Personalizer issues | https://learn.microsoft.com/en-us/azure/ai-services/personalizer/frequently-asked-questions |
| Topic | URL | |-------|-----| | Choose between single-slot and multi-slot Personalizer | https://learn.microsoft.com/en-us/azure/ai-services/personalizer/concept-multi-slot-personalization |
| Topic | URL | |-------|-----| | Configure data-at-rest encryption and CMK for Personalizer | https://learn.microsoft.com/en-us/azure/ai-services/personalizer/encrypt-data-at-rest | | Manage data usage and privacy in Personalizer | https://learn.microsoft.com/en-us/azure/ai-services/personalizer/responsible-data-and-privacy |
| Topic | URL | |-------|-----| | Enable and use inference explainability in Personalizer | https://learn.microsoft.com/en-us/azure/ai-services/personalizer/how-to-inference-explainability | | Configure apprentice mode learning behavior in Personalizer | https://learn.microsoft.com/en-us/azure/ai-services/personalizer/how-to-learning-behavior | | Export and manage Personalizer model and learning settings | https://learn.microsoft.com/en-us/azure/ai-services/personalizer/how-to-manage-model | | Configure Azure Personalizer learning loop settings | https://learn.microsoft.com/en-us/azure/ai-services/personalizer/how-to-settings |
| Topic | URL | |-------|-----| | Use Personalizer local inference SDK for low latency | https://learn.microsoft.com/en-us/azure/ai-services/personalizer/how-to-thick-client |
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Expert knowledge for Microsoft Foundry Classic (aka Azure AI Foundry classic) development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring Foundry agents, routing Azure OpenAI models, integrating tools/RAG, securing endpoints, or deploying hubs, and other Microsoft Foundry Classic related development tasks. Not for Microsoft Foundry (use microsoft-foundry), Microsoft Foundry Local (use microsoft-foundry-local), Microsoft Foundry Tools (use microsoft-foundry-tools).
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