azure-resource-health-diagnose

github/awesome-copilot · updated Apr 8, 2026

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$npx skills add https://github.com/github/awesome-copilot --skill azure-resource-health-diagnose
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summary

Diagnose Azure resource health issues and generate a prioritized remediation plan.

  • 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
skill.md

Azure Resource Health & Issue Diagnosis

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.

Prerequisites

  • Azure MCP server configured and authenticated
  • Target Azure resource identified (name and optionally resource group/subscription)
  • Resource must be deployed and running to generate logs/telemetry
  • Prefer Azure MCP tools (azmcp-*) over direct Azure CLI when available

Workflow Steps

Step 1: Get Azure Best Practices

Action: Retrieve diagnostic and troubleshooting best practices Tools: Azure MCP best practices tool Process:

  1. Load Best Practices:
    • Execute Azure best practices tool to get diagnostic guidelines
    • Focus on health monitoring, log analysis, and issue resolution patterns
    • Use these practices to inform diagnostic approach and remediation recommendations

Step 2: Resource Discovery & Identification

Action: Locate and identify the target Azure resource Tools: Azure MCP tools + Azure CLI fallback Process:

  1. Resource Lookup:

    • If only resource name provided: Search across subscriptions using azmcp-subscription-list
    • Use az resource list --name <resource-name> to find matching resources
    • If multiple matches found, prompt user to specify subscription/resource group
    • Gather detailed resource information:
      • Resource type and current status
      • Location, tags, and configuration
      • Associated services and dependencies
  2. Resource Type Detection:

    • Identify resource type to determine appropriate diagnostic approach:
      • Web Apps/Function Apps: Application logs, performance metrics, dependency tracking
      • Virtual Machines: System logs, performance counters, boot diagnostics
      • Cosmos DB: Request metrics, throttling, partition statistics
      • Storage Accounts: Access logs, performance metrics, availability
      • SQL Database: Query performance, connection logs, resource utilization
      • Application Insights: Application telemetry, exceptions, dependencies
      • Key Vault: Access logs, certificate status, secret usage
      • Service Bus: Message metrics, dead letter queues, throughput

Step 3: Health Status Assessment

Action: Evaluate current resource health and availability Tools: Azure MCP monitoring tools + Azure CLI Process:

  1. Basic Health Check:

    • Check resource provisioning state and operational status
    • Verify service availability and responsiveness
    • Review recent deployment or configuration changes
    • Assess current resource utilization (CPU, memory, storage, etc.)
  2. Service-Specific Health Indicators:

    • Web Apps: HTTP response codes, response times, uptime
    • Databases: Connection success rate, query performance, deadlocks
    • Storage: Availability percentage, request success rate, latency
    • VMs: Boot diagnostics, guest OS metrics, network connectivity
    • Functions: Execution success rate, duration, error frequency

Step 4: Log & Telemetry Analysis

Action: Analyze logs and telemetry to identify issues and patterns Tools: Azure MCP monitoring tools for Log Analytics queries Process:

  1. Find Monitoring Sources:

    • Use azmcp-monitor-workspace-list to identify Log Analytics workspaces
    • Locate Application Insights instances associated with the resource
    • Identify relevant log tables using azmcp-monitor-table-list
  2. Execute 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)
    
  3. Pattern Recognition:

    • Identify recurring error patterns or anomalies
    • Correlate errors with deployment times or configuration changes
    • Analyze performance trends and degradation patterns
    • Look for dependency failures or external service issues

Step 5: Issue Classification & Root Cause Analysis

Action: Categorize identified issues and determine root causes Process:

  1. Issue Classification:

    • Critical: Service unavailable, data loss, security breaches
    • High: Performance degradation, intermittent failures, high error rates
    • Medium: Warnings, suboptimal configuration, minor performance issues
    • Low: Informational alerts, optimization opportunities
  2. Root Cause Analysis:

    • Configuration Issues: Incorrect settings, missing dependencies
    • Resource Constraints: CPU/memory/disk limitations, throttling
    • Network Issues: Connectivity problems, DNS resolution, firewall rules
    • Application Issues: Code bugs, memory leaks, inefficient queries
    • External Dependencies: Third-party service failures, API limits
    • Security Issues: Authentication failures, certificate expiration
  3. Impact Assessment:

    • Determine business impact and affected users/systems
    • Evaluate data integrity and security implications
    • Assess recovery time objectives and priorities

Step 6: Generate Remediation Plan

Action: Create a comprehensive plan to address identified issues Process:

  1. Immediate Actions (Critical issues):

    • Emergency fixes to restore service availability
    • Temporary workarounds to mitigate impact
    • Escalation procedures for complex issues
  2. Short-term Fixes (High/Medium issues):

    • Configuration adjustments and resource scaling
    • Application updates and patches
    • Monitoring and alerting improvements
  3. Long-term Improvements (All issues):

    • Architectural changes for better resilience
    • Preventive measures and monitoring enhancements
    • Documentation and process improvements
  4. Implementation Steps:

    • Prioritized action items with specific Azure CLI commands
    • Testing and validation procedures
    • Rollback plans for each change
    • Monitoring to verify issue resolution

Step 7: User Confirmation & Report Generation

Action: Present findings and get approval for remediation actions Process:

  1. 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)
    
  2. 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]
    

    Phase 2: Short-term Fixes (2-24 hours)

    # Performance and reliability improvements
    [Azure CLI commands with explanations]
    

    Phase 3: Long-term Improvements (1-4 weeks)

    # Architectural and preventive measures
    [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]

Error Handling

  • Resource Not Found: Provide guidance on resource name/location specification
  • Authentication Issues: Guide user through Azure authentication setup
  • Insufficient Permissions: List required RBAC roles for resource access
  • No Logs Available: Suggest enabling diagnostic settings and waiting for data
  • Query Timeouts: Break down analysis into smaller time windows
  • Service-Specific Issues: Provide generic health assessment with limitations noted

Success Criteria

  • ✅ Resource health status accurately assessed
  • ✅ All significant issues identified and categorized
  • ✅ Root cause analysis completed for major problems
  • ✅ Actionable remediation plan with specific steps provided
  • ✅ Monitoring and prevention recommendations included
  • ✅ Clear prioritization of issues by business impact
  • ✅ Implementation steps include validation and rollback procedures
how to use azure-resource-health-diagnose

How to use azure-resource-health-diagnose on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add azure-resource-health-diagnose
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/github/awesome-copilot --skill azure-resource-health-diagnose

The skills CLI fetches azure-resource-health-diagnose from GitHub repository github/awesome-copilot and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/azure-resource-health-diagnose

Reload or restart Cursor to activate azure-resource-health-diagnose. Access the skill through slash commands (e.g., /azure-resource-health-diagnose) or your agent's skill management interface.

Security & Verification Notice

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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ 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.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.726 reviews
  • Shikha Mishra· Dec 20, 2024

    We added azure-resource-health-diagnose from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Sofia Singh· Sep 25, 2024

    azure-resource-health-diagnose reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Maya Robinson· Sep 13, 2024

    azure-resource-health-diagnose has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Oshnikdeep· Sep 9, 2024

    Useful defaults in azure-resource-health-diagnose — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Ganesh Mohane· Aug 28, 2024

    azure-resource-health-diagnose is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Sofia Mehta· Aug 16, 2024

    I recommend azure-resource-health-diagnose for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Maya Li· Aug 4, 2024

    Keeps context tight: azure-resource-health-diagnose is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Diego Jackson· Jul 23, 2024

    azure-resource-health-diagnose is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Sakshi Patil· Jul 19, 2024

    Keeps context tight: azure-resource-health-diagnose is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Kabir Harris· Jul 15, 2024

    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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