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 skills add https://github.com/MicrosoftDocs/Agent-Skills --skill azure-personalizer
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), use read_file with the specified lines. For categories with file links (e.g., 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:
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-L55 | Configuring Personalizer’s learning behavior: policies, hyperparameters, exploration, apprentice mode, explainability, model export, and learning loop settings. |
| Integrations & Coding Patterns | L56-L59 | 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 |
| 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 |
This skill should be used when the user asks to "perform cloud penetration testing", "assess Azure or AWS or GCP security", "enumerate cloud resources", "exploit cloud misconfigurations", "test O365 security", "extract secrets from cloud environments", or "audit cloud infrastructure". It provides comprehensive techniques for security assessment across major cloud platforms.
Comprehensive Flow Nexus platform management - authentication, sandboxes, app deployment, payments, and challenges
Coordinate multi-layer security scanning and hardening across application, infrastructure, and compliance controls.
Implement Kubernetes security policies including NetworkPolicy, PodSecurityPolicy, and RBAC for production-grade security. Use when securing Kubernetes clusters, implementing network isolation, or enforcing pod security standards.
Implement Kubernetes security policies including NetworkPolicy, PodSecurityPolicy, and RBAC for production-grade security. Use when securing Kubernetes clusters, implementing network isolation, or enforcing pod security standards.
Run Azure compliance and security audits with azqr plus Key Vault expiration checks. Covers best-practice assessment, resource review, policy/compliance validation, and security posture checks. WHEN: compliance scan, security audit, BEFORE running azqr (compliance cli tool), Azure best practices, Key Vault expiration check, expired certificates, expiring secrets, orphaned resources, compliance assessment.
Run Azure compliance and security audits with azqr plus Key Vault expiration checks. Covers best-practice assessment, resource review, policy/compliance validation, and security posture checks. WHEN: compliance scan, security audit, BEFORE running azqr (compliance cli tool), Azure best practices, Key Vault expiration check, expired certificates, expiring secrets, orphaned resources, compliance assessment.
Comprehensive AWS security posture assessment using AWS CLI and security best practices
Take microsoftdocs/azure-personalizer from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.