Expert knowledge for Azure Advisor development including best practices, decision making, limits & quotas, security, configuration, and integrations & coding patterns. Use when managing Advisor alerts, digests, recommendation states, Resource Graph queries, or RBAC access, and other Azure Advisor related development tasks. Not for Azure Cost Management (use azure-cost-management), Azure Monitor (use azure-monitor), Azure Policy (use azure-policy), Azure Security (use azure-security).
npx skills add https://github.com/MicrosoftDocs/Agent-Skills --skill azure-advisor
This skill provides expert guidance for Azure Advisor. Covers best practices, decision making, limits & quotas, 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 |
|----------|-------|-------------|
| Best Practices | L34-L42 | Guidance on using Azure Advisor for Well-Architected assessments and bulk fixes to optimize cost, performance, reliability, and operational excellence across VMs and other services. |
| Decision Making | L43-L51 | Using Advisor workbooks and critical risk views to assess reliability, plan migrations, and estimate cost impact of Azure Advisor recommendations across key resources |
| Limits & Quotas | L52-L56 | Advisor feature availability, limits, and differences when running in Azure sovereign clouds (e.g., Azure Government, China), including which recommendations are supported. |
| Security | L57-L61 | Managing Azure Advisor permissions, roles, and RBAC settings so users and apps have appropriate access to Advisor recommendations and data |
| Configuration | L62-L72 | Configuring Azure Advisor alerts, digests, and recommendation states via portal, ARM/Bicep, tags, and workbooks to control how and when recommendations are delivered and viewed. |
| Integrations & Coding Patterns | L73-L76 | Querying Azure Advisor data via Resource Graph, using sample Kusto queries, and integrating Advisor MCP tools with AI/LLM clients for automated insights |
| Topic | URL |
|-------|-----|
| Run Well-Architected assessments in Azure Advisor | https://learn.microsoft.com/en-us/azure/advisor/advisor-assessments |
| Optimize VM and VMSS costs using Azure Advisor | https://learn.microsoft.com/en-us/azure/advisor/advisor-cost-recommendations |
| Calculate and export Azure Advisor cost savings | https://learn.microsoft.com/en-us/azure/advisor/advisor-how-to-calculate-total-cost-savings |
| Improve high-usage VM performance with Azure Advisor | https://learn.microsoft.com/en-us/azure/advisor/advisor-how-to-performance-resize-high-usage-vm-recommendations |
| Use Quick Fix for bulk remediation of Advisor recommendations | https://learn.microsoft.com/en-us/azure/advisor/advisor-quick-fix |
| Topic | URL |
|-------|-----|
| Use Azure Advisor Critical Risks for key resources | https://learn.microsoft.com/en-us/azure/advisor/advisor-critical-risks |
| Assess cost impact of Azure Advisor recommendations | https://learn.microsoft.com/en-us/azure/advisor/advisor-how-to-evaluate-cost-implications-of-recommendations |
| Analyze and optimize Azure costs with the Advisor workbook | https://learn.microsoft.com/en-us/azure/advisor/advisor-workbook-cost-optimization |
| Evaluate application reliability using the Advisor workbook | https://learn.microsoft.com/en-us/azure/advisor/advisor-workbook-reliability |
| Use Advisor Service Retirement workbook for migration planning | https://learn.microsoft.com/en-us/azure/advisor/advisor-workbook-service-retirement |
| Topic | URL |
|-------|-----|
| Understand Azure Advisor feature limits in sovereign clouds | https://learn.microsoft.com/en-us/azure/advisor/advisor-sovereign-clouds |
| Topic | URL |
|-------|-----|
| Configure Azure Advisor roles and access control | https://learn.microsoft.com/en-us/azure/advisor/permissions |
| Topic | URL |
|-------|-----|
| Create Azure Advisor alerts with ARM templates | https://learn.microsoft.com/en-us/azure/advisor/advisor-alerts-arm |
| Define Azure Advisor alert rules using Bicep | https://learn.microsoft.com/en-us/azure/advisor/advisor-alerts-bicep |
| Configure Azure Advisor alerts in the Azure portal | https://learn.microsoft.com/en-us/azure/advisor/advisor-alerts-portal |
| Configure Azure Advisor recommendation state management | https://learn.microsoft.com/en-us/azure/advisor/advisor-azure-state-management |
| Configure periodic Azure Advisor recommendation digests | https://learn.microsoft.com/en-us/azure/advisor/advisor-recommendations-digest |
| Filter Azure Advisor recommendations by resource tags | https://learn.microsoft.com/en-us/azure/advisor/advisor-tag-filtering |
| Use Azure Advisor workbook templates for insights | https://learn.microsoft.com/en-us/azure/advisor/advisor-workbooks |
| Topic | URL |
|-------|-----|
| Integrate Azure Advisor MCP tools with AI clients | https://learn.microsoft.com/en-us/azure/advisor/advisor-mcp-tools |
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take microsoftdocs/azure-advisor 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.