Diagnose Azure resource health issues and generate a prioritized remediation plan.
Works with
Analyzes resource status, logs, and telemetry across 8+ Azure service types (Web Apps, VMs, Cosmos DB, Storage, SQL Database, Application Insights, Key Vault, Service Bus)
Executes targeted KQL queries against Log Analytics and Application Insights to identify errors, performance degradation, and anomalies
Classifies issues by severity (Critical, High, Medium, Low) and performs root cause analysis acro
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionazure-resource-health-diagnoseExecute the skills CLI command in your project's root directory to begin installation:
Fetches azure-resource-health-diagnose from github/awesome-copilot and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate azure-resource-health-diagnose. Access via /azure-resource-health-diagnose in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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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:
Resource Lookup:
azmcp-subscription-listaz resource list --name <resource-name> to find matching resourcesResource Type Detection:
Action: Evaluate current resource health and availability Tools: Azure MCP monitoring tools + Azure CLI Process:
Basic Health Check:
Service-Specific Health Indicators:
Action: Analyze logs and telemetry to identify issues and patterns Tools: Azure MCP monitoring tools for Log Analytics queries Process:
Find Monitoring Sources:
azmcp-monitor-workspace-list to identify Log Analytics workspacesazmcp-monitor-table-listExecute Diagnostic Queries:
Use 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)
Pattern Recognition:
Action: Categorize identified issues and determine root causes Process:
Issue Classification:
Root Cause Analysis:
Impact Assessment:
Action: Create a comprehensive plan to address identified issues Process:
Immediate Actions (Critical issues):
Short-term Fixes (High/Medium issues):
Long-term Improvements (All issues):
Implementation Steps:
Action: Present findings and get approval for remediation actions Process:
Display Health Assessment Summary:
🏥 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)
Generate Detailed Report:
# 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)
```bash
# Critical fixes to restore service
[Azure CLI commands with explanations]
# Performance and reliability improvements
[Azure CLI commands with explanations]
# Architectural and preventive measures
[Azure CLI commands and configuration changes]
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
github/awesome-copilot
github/awesome-copilot
github/awesome-copilot
github/awesome-copilot
github/awesome-copilot
github/awesome-copilot
We added azure-resource-health-diagnose from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
azure-resource-health-diagnose reduced setup friction for our internal harness; good balance of opinion and flexibility.
azure-resource-health-diagnose has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in azure-resource-health-diagnose — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
azure-resource-health-diagnose is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
I recommend azure-resource-health-diagnose for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: azure-resource-health-diagnose is the kind of skill you can hand to a new teammate without a long onboarding doc.
azure-resource-health-diagnose is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Keeps context tight: azure-resource-health-diagnose is the kind of skill you can hand to a new teammate without a long onboarding doc.
We added azure-resource-health-diagnose from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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