Azure Resource Health Diagnose
GitHubOfficialOriginally contributed by @ewega as prompts/azure-resource-health-diagnose.prompt.md; moved into a skill by pull request #761Distributed by GitHub
- Skill
- Workflow
- Cloud and DevOps
- MIT
- No issues found
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a systematic approach to troubleshooting Azure resources. It guides the user through identifying the target resource, assessing its current health status, analyzing logs and telemetry data using KQL queries, and performing root cause analysis. The workflow culminates in the generation of a detailed, prioritized remediation report that includes immediate fixes, short-term improvements, and long-term architectural recommendations, along with validation and prevention strategies.
What it does
- Identifies and locates specific Azure resources across subscriptions.
- Assesses resource health based on service-specific indicators and metrics.
- Executes diagnostic KQL queries against Log Analytics and Application Insights.
- Classifies issues by severity and performs root cause analysis.
- Generates a structured, actionable remediation plan with CLI commands.
- Provides recommendations for ongoing monitoring and future prevention.
When to use it
- When an Azure resource is experiencing performance degradation or downtime.
- When investigating recurring errors or unexpected behavior in an Azure service.
- When performing a routine health check to identify potential configuration issues.
- When you need a structured plan to resolve identified infrastructure or application problems.
Advantages
- Standardizes the diagnostic process for consistent troubleshooting results.
- Reduces time-to-resolution by providing targeted KQL queries and remediation steps.
- Ensures comprehensive coverage by including validation and rollback procedures.
- Helps prioritize efforts based on business impact and issue severity.
Usage examples
- “Diagnose the health of my web app named 'production-api-01'.”
- The agent will locate the resource, check its status, run log queries to identify recent errors, and provide a report with a remediation plan.
- “My SQL database is showing high latency, can you help?”
- The agent will analyze SQL performance metrics and connection logs to identify bottlenecks and suggest specific optimization steps.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/github/azure-resource-health-diagnose/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip azure-resource-health-diagnose.zip -d .agents/skills
Documentation by ewega
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:
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:
Resource Lookup:
If only resource name provided: Search across subscriptions using
azmcp-subscription-listUse
az resource list --name <resource-name>to find matching resourcesIf 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
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:
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.)
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:
Find Monitoring Sources:
Use
azmcp-monitor-workspace-listto identify Log Analytics workspacesLocate Application Insights instances associated with the resource
Identify relevant log tables using
azmcp-monitor-table-list
Execute Diagnostic Queries: Use
azmcp-monitor-log-querywith 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 descPerformance 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 > 80Application-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:
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:
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
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
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:
Immediate Actions (Critical issues):
Emergency fixes to restore service availability
Temporary workarounds to mitigate impact
Escalation procedures for complex issues
Short-term Fixes (High/Medium issues):
Configuration adjustments and resource scaling
Application updates and patches
Monitoring and alerting improvements
Long-term Improvements (All issues):
Architectural changes for better resilience
Preventive measures and monitoring enhancements
Documentation and process improvements
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:
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]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
Files
- ATTRIBUTION.mdAdded by AutoGineer1.2 KiB
- LICENSELicence file added by AutoGineer1.0 KiB
- SKILL.md11.2 KiB
Requirements and compatibility
Written for: GitHub Copilot
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs an MCP server
Security
No issues found by our static scan· No issues found
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Originally contributed by
@ewegaas prompts/azure-resource-health-diagnose.prompt.mdmoved into a skill by pull request #761
- Publisher
- GitHub (Official)
- Distributed by
- GitHub
- Source
- github/awesome-copilot · skills/azure-resource-health-diagnose · commit 797337f
- Licence
- MIT License (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
- Contributors
- ewega
AutoGineer is not affiliated with or endorsed by ewega or GitHub.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download azure-resource-health-diagnose.zip