Analyze Azure resource health, diagnose issues from logs and telemetry, and create a remediation plan for identified problems.
npx skills add https://github.com/github/awesome-copilot --skill azure-resource-health-diagnose
This workflow analyzes a specific Azure resource to assess its health status, diagnose potential issues using logs and telemetry data, and develop a comprehensive remediation plan for any problems discovered.
azmcp-*) over direct Azure CLI when availableAction: Retrieve diagnostic and troubleshooting best practices
Tools: Azure MCP best practices tool
Process:
Action: Locate and identify the target Azure resource
Tools: Azure MCP tools + Azure CLI fallback
Process:
azmcp-subscription-listaz resource list --name <resource-name> to find matching resourcesAction: Evaluate current resource health and availability
Tools: Azure MCP monitoring tools + Azure CLI
Process:
Action: Analyze logs and telemetry to identify issues and patterns
Tools: Azure MCP monitoring tools for Log Analytics queries
Process:
azmcp-monitor-workspace-list to identify Log Analytics workspacesazmcp-monitor-table-listUse azmcp-monitor-log-query with targeted KQL queries based on resource type:
General Error Analysis:
// Recent errors and exceptions
union isfuzzy=true
AzureDiagnostics,
AppServiceHTTPLogs,
AppServiceAppLogs,
AzureActivity
| where TimeGenerated > ago(24h)
| where Level == "Error" or ResultType != "Success"
| summarize ErrorCount=count() by Resource, ResultType, bin(TimeGenerated, 1h)
| order by TimeGenerated desc
Performance Analysis:
// Performance degradation patterns
Perf
| where TimeGenerated > ago(7d)
| where ObjectName == "Processor" and CounterName == "% Processor Time"
| summarize avg(CounterValue) by Computer, bin(TimeGenerated, 1h)
| where avg_CounterValue > 80
Application-Specific Queries:
// Application Insights - Failed requests
requests
| where timestamp > ago(24h)
| where success == false
| summarize FailureCount=count() by resultCode, bin(timestamp, 1h)
| order by timestamp desc
// Database - Connection failures
AzureDiagnostics
| where ResourceProvider == "MICROSOFT.SQL"
| where Category == "SQLSecurityAuditEvents"
| where action_name_s == "CONNECTION_FAILED"
| summarize ConnectionFailures=count() by bin(TimeGenerated, 1h)
Action: Categorize identified issues and determine root causes
Process:
Action: Create a comprehensive plan to address identified issues
Process:
Action: Present findings and get approval for remediation actions
Process:
🏥 Azure Resource Health Assessment
📊 Resource Overview:
• Resource: [Name] ([Type])
• Status: [Healthy/Warning/Critical]
• Location: [Region]
• Last Analyzed: [Timestamp]
🚨 Issues Identified:
• Critical: X issues requiring immediate attention
• High: Y issues affecting performance/reliability
• Medium: Z issues for optimization
• Low: N informational items
🔍 Top Issues:
1. [Issue Type]: [Description] - Impact: [High/Medium/Low]
2. [Issue Type]: [Description] - Impact: [High/Medium/Low]
3. [Issue Type]: [Description] - Impact: [High/Medium/Low]
🛠️ Remediation Plan:
• Immediate Actions: X items
• Short-term Fixes: Y items
• Long-term Improvements: Z items
• Estimated Resolution Time: [Timeline]
❓ Proceed with detailed remediation plan? (y/n)
# Azure Resource Health Report: [Resource Name]
**Generated**: [Timestamp]
**Resource**: [Full Resource ID]
**Overall Health**: [Status with color indicator]
## 🔍 Executive Summary
[Brief overview of health status and key findings]
## 📊 Health Metrics
- **Availability**: X% over last 24h
- **Performance**: [Average response time/throughput]
- **Error Rate**: X% over last 24h
- **Resource Utilization**: [CPU/Memory/Storage percentages]
## 🚨 Issues Identified
### Critical Issues
- **[Issue 1]**: [Description]
- **Root Cause**: [Analysis]
- **Impact**: [Business impact]
- **Immediate Action**: [Required steps]
### High Priority Issues
- **[Issue 2]**: [Description]
- **Root Cause**: [Analysis]
- **Impact**: [Performance/reliability impact]
- **Recommended Fix**: [Solution steps]
## 🛠️ Remediation Plan
### Phase 1: Immediate Actions (0-2 hours)
[Azure CLI commands with explanations]
### Phase 2: Short-term Fixes (2-24 hours)
[Azure CLI commands with explanations]
### Phase 3: Long-term Improvements (1-4 weeks)
[Azure CLI commands and configuration changes]
## 📈 Monitoring Recommendations
- **Alerts to Configure**: [List of recommended alerts]
- **Dashboards to Create**: [Monitoring dashboard suggestions]
- **Regular Health Checks**: [Recommended frequency and scope]
## ✅ Validation Steps
- [ ] Verify issue resolution through logs
- [ ] Confirm performance improvements
- [ ] Test application functionality
- [ ] Update monitoring and alerting
- [ ] Document lessons learned
## 📝 Prevention Measures
- [Recommendations to prevent similar issues]
- [Process improvements]
- [Monitoring enhancements]
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 github/azure-resource-health-diagnose 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.