azure-diagnostics▌
microsoft/GitHub-Copilot-for-Azure · updated Apr 8, 2026
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Systematic diagnosis and remediation for Azure production issues using AppLens, Azure Monitor, and resource health checks.
- ›Covers Container Apps, Function Apps, and AKS clusters with service-specific troubleshooting guides for image pulls, cold starts, health probes, invocation failures, and node/pod issues
- ›Includes AppLens MCP integration for AI-powered root cause analysis and Azure Monitor MCP for KQL-based log and metric queries
- ›Provides a five-step diagnostic flow: identify sympt
Azure Diagnostics
AUTHORITATIVE GUIDANCE — MANDATORY COMPLIANCE
This document is the official source for debugging and troubleshooting Azure production issues. Follow these instructions to diagnose and resolve common Azure service problems systematically.
Triggers
Activate this skill when user wants to:
- Debug or troubleshoot production issues
- Diagnose errors in Azure services
- Analyze application logs or metrics
- Fix image pull, cold start, or health probe issues
- Investigate why Azure resources are failing
- Find root cause of application errors
- Troubleshoot Azure Function Apps (invocation failures, timeouts, binding errors)
- Find the App Insights or Log Analytics workspace linked to a Function App
- Troubleshoot AKS clusters, nodes, pods, ingress, or Kubernetes networking issues
Rules
- Start with systematic diagnosis flow
- Use AppLens (MCP) for AI-powered diagnostics when available
- Check resource health before deep-diving into logs
- Select appropriate troubleshooting guide based on service type
- Document findings and attempted remediation steps
- Route AKS incidents to the dedicated AKS troubleshooting document
Quick Diagnosis Flow
- Identify symptoms - What's failing?
- Check resource health - Is Azure healthy?
- Review logs - What do logs show?
- Analyze metrics - Performance patterns?
- Investigate recent changes - What changed?
Troubleshooting Guides by Service
| Service | Common Issues | Reference |
|---|---|---|
| Container Apps | Image pull failures, cold starts, health probes, port mismatches | container-apps/ |
| Function Apps | App details, invocation failures, timeouts, binding errors, cold starts, missing app settings | functions/ |
| AKS | Cluster access, nodes, kube-system, scheduling, crash loops, ingress, DNS, upgrades |
AKS Troubleshooting |
Routing
- Keep Container Apps and Function Apps diagnostics in this parent skill.
- Route active AKS incidents, AKS-specific intake, evidence gathering, and remediation guidance to AKS Troubleshooting.
Quick Reference
Common Diagnostic Commands
# Check resource health
az resource show --ids RESOURCE_ID
# View activity log
az monitor activity-log list -g RG --max-events 20
# Container Apps logs
az containerapp logs show --name APP -g RG --follow
# Function App logs (query App Insights traces)
az monitor app-insights query --apps APP-INSIGHTS -g RG \
--analytics-query "traces | where timestamp > ago(1h) | order by timestamp desc | take 50"
AppLens (MCP Tools)
For AI-powered diagnostics, use:
mcp_azure_mcp_applens
intent: "diagnose issues with <resource-name>"
command: "diagnose"
parameters:
resourceId: "<resource-id>"
Provides:
- Automated issue detection
- Root cause analysis
- Remediation recommendations
Azure Monitor (MCP Tools)
For querying logs and metrics:
mcp_azure_mcp_monitor
intent: "query logs for <resource-name>"
command: "logs_query"
parameters:
workspaceId: "<workspace-id>"
query: "<KQL-query>"
See kql-queries.md for common diagnostic queries.
Check Azure Resource Health
Using MCP
mcp_azure_mcp_resourcehealth
intent: "check health status of <resource-name>"
command: "get"
parameters:
resourceId: "<resource-id>"
Using CLI
# Check specific resource health
az resource show --ids RESOURCE_ID
# Check recent activity
az monitor activity-log list -g RG --max-events 20
References
How to use azure-diagnostics on Cursor
AI-first code editor with Composer
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-diagnostics
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches azure-diagnostics from GitHub repository microsoft/GitHub-Copilot-for-Azure and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate azure-diagnostics. Access the skill through slash commands (e.g., /azure-diagnostics) 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.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
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.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 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▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.8★★★★★37 reviews- ★★★★★Liam Agarwal· Dec 20, 2024
azure-diagnostics fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Sakura Kapoor· Dec 4, 2024
azure-diagnostics is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Soo Ramirez· Nov 23, 2024
Solid pick for teams standardizing on skills: azure-diagnostics is focused, and the summary matches what you get after install.
- ★★★★★Soo Abbas· Oct 14, 2024
azure-diagnostics has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Rahul Santra· Sep 17, 2024
Registry listing for azure-diagnostics matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Soo Malhotra· Sep 17, 2024
Registry listing for azure-diagnostics matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Neel Torres· Sep 17, 2024
Solid pick for teams standardizing on skills: azure-diagnostics is focused, and the summary matches what you get after install.
- ★★★★★Pratham Ware· Aug 8, 2024
azure-diagnostics reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Liam Gill· Aug 8, 2024
azure-diagnostics reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Yash Thakker· Jul 27, 2024
I recommend azure-diagnostics for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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