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Azure Diagnostics

MicrosoftOfficialCreated by Microsoft

  • Skill
  • Workflow
  • Cloud and DevOps
  • MIT
  • Low risk
  • Has scripts

From the author

Debug Azure production issues using AppLens, Azure Monitor, resource health, and safe triage, including evidence-only diagnosis without live access. WHEN: debug production issues, troubleshoot app service, app service high CPU, app service deployment failure, troubleshoot container apps, container app 503 or target port mismatch, troubleshoot functions, function telemetry missing or stopped after a config/connection-string change, troubleshoot AKS, VM RDP, Linux SSH, VM black screen, can't connect to VM, reset VM password, NSG or firewall blocking, kubectl cannot connect, kube-system/CoreDNS failures, pod pending, crashloop, node not ready, upgrade failures, analyze logs, KQL, insights, image pull failures, cold start issues, health probe failures, resource health, root cause of errors, troubleshoot event hubs, troubleshoot service bus, messaging SDK error, AMQP connection failure, message lock lost, lock renewal during long-running processing, service bus dead letter.
— the description by Microsoft

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

Azure Diagnostics provides a structured, authoritative approach to investigating and resolving production issues across various Azure services. It emphasizes a systematic flow: identifying symptoms, checking resource health, reviewing logs, analyzing metrics, and investigating recent changes. The skill supports both live troubleshooting via Azure CLI and AppLens, as well as evidence-only analysis when live access is restricted. It includes specialized guidance for App Service, Container Apps, Azure Functions, AKS, VMs, and Messaging services, ensuring consistent root-cause analysis.

What it does

  • Provides a standardized diagnostic workflow for Azure production environments.
  • Integrates with AppLens and Azure Monitor for automated issue detection and log analysis.
  • Offers service-specific troubleshooting paths for App Service, AKS, VMs, and more.
  • Supports evidence-only diagnosis for secure, offline troubleshooting scenarios.
  • Includes KQL query patterns for deep-dive log and resource health analysis.
  • Facilitates the use of bundled diagnostic scripts for efficient data collection.

When to use it

  • When investigating production incidents like high CPU, deployment failures, or service crashes.
  • When troubleshooting connectivity issues such as RDP/SSH failures or network blocking.
  • When diagnosing Kubernetes-specific issues like pod crashloops or image pull failures.
  • When analyzing missing telemetry or messaging errors in Event Hubs and Service Bus.
  • When performing root-cause analysis on unhealthy or degraded Azure resources.

Advantages

  • Reduces mean time to resolution (MTTR) with a clear, step-by-step diagnostic process.
  • Ensures consistent troubleshooting practices across different Azure service types.
  • Supports secure workflows by allowing diagnosis without requiring live environment access.
  • Leverages powerful built-in tools like AppLens and Azure Resource Graph for rapid insights.
  • Provides actionable remediation steps based on identified symptoms.

Usage examples

“Troubleshoot my App Service that is experiencing high CPU usage.”
The agent will initiate the App Service diagnostic flow, run the appservice-diagnostics script, check metrics, and guide you through profiling via Kudu.
“Why is my Azure VM unreachable via SSH?”
The agent will follow the VM connectivity troubleshooting guide, checking NSG rules, firewall status, and VM agent health.
“Analyze these logs to find why my Container App is failing to pull the image.”
The agent will perform an evidence-only analysis of the provided logs to identify image pull errors or registry authentication issues.
“Check the health of my AKS cluster and identify why pods are in a crashloop.”
The agent will check resource health, query logs for the affected pods, and route to the AKS-specific troubleshooting documentation.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/microsoft/azure-diagnostics/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip azure-diagnostics.zip -d .agents/skills

Documentation by Microsoft

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 App Service issues (high CPU, deployment failures, crashes, slow responses, TLS/custom domains)

  • Respond to prompts like "troubleshoot app service", "app service high CPU", or "app service deployment failure"

  • 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

  • Troubleshoot Azure VM connectivity issues (RDP/SSH failures, port 3389/22 timeouts, NSG or firewall blocking, credential resets)

  • Troubleshoot Azure Messaging SDK issues (Event Hubs, Service Bus connection failures, AMQP errors, message lock issues)

  • Root-cause a symptom that looks like a healthy-runtime-but-wrong-destination problem (e.g., telemetry/messages stop flowing to the expected resource after a config change) — invoke this skill for the matrix even when MCP tools could answer the surface question alone

Rules

  1. Start with systematic diagnosis flow

  2. Use supplied evidence first. If the user requests evidence-only analysis or prohibits live Azure access, do not query or modify Azure. Diagnose from the supplied logs, metrics, and timeline; identify assumptions and provide optional verification steps.

  3. Use AppLens (MCP) for AI-powered diagnostics when available

  4. Check resource health before deep-diving into logs

  5. Select appropriate troubleshooting guide based on service type

  6. Document findings and attempted remediation steps

  7. Prefer bundled diagnostic scripts over ad hoc calls for permitted live collection when identifiers are known. If supplied evidence suffices or live access is prohibited, don't run them; otherwise run the applicable script first.

  8. Route AKS incidents to the dedicated AKS troubleshooting document

  9. Keep baseline AKS diagnostics available even when the optional aks-skills add-on is absent, declined, or unsupported by the host


Quick Diagnosis Flow

  1. Identify symptoms - What's failing?

  2. Check resource health - Is Azure healthy?

  3. Review logs - What do logs show?

  4. Analyze metrics - Performance patterns?

  5. Investigate recent changes - What changed?


Troubleshooting Guides by Service

ServiceCommon IssuesReference
Container AppsImage pull failures, cold starts, health probes, port mismatchescontainer-apps/
App ServiceHigh CPU, deployment failures, crashes, slow responses, TLS/custom domainsapp-service/
Function AppsApp details, invocation failures, timeouts, binding errors, cold starts, missing app settingsfunctions/
AKSCluster access, nodes, kube-system, scheduling, crash loops, ingress, DNS, upgradesAKS Troubleshooting
ComputeVM RDP/SSH connectivity, NSG/firewall blocks, credential resets, VM agent/tooling issuesVM Connectivity Troubleshooting
MessagingEvent Hubs & Service Bus SDK errors, AMQP failures, message lock, connectivityMessaging 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.

  • Route Azure VM RDP/SSH connectivity, NSG/firewall, credential reset, and VM agent troubleshooting to VM Connectivity Troubleshooting.

  • Route Azure Messaging SDK troubleshooting (Event Hubs, Service Bus) to Messaging 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

Files

troubleshooting/aks/aks-troubleshooting.md

AKS Troubleshooting Guide

Primary AKS troubleshooting guide for incidents routed from ../../SKILL.md.

When to Use This Guide

  • lifecycle, access, node, kube-system, workload, ingress, DNS, or scaling issues

  • kubectl cannot connect, nodes are NotReady, or pods are unhealthy

Scenario Playbooks

ScenarioReference
broad cluster investigationgeneral-diagnostics.md
workload, crash, image pull, readiness, or pending pod issuespod-failures.md
node health, scaling, pressure, upgrade, or zone issuesnode-issues.md
service, ingress, DNS, or network policy issuesnetworking.md

Tool Selection For Diagnostics

When gathering AKS diagnostic evidence, use the host-assigned Azure MCP AKS area for cluster and node-pool metadata, then separate Azure MCP areas such as AppLens, Monitor, or Resource Health when their advertised schemas fit the read. Use az aks and kubectl for every check those surfaces do not provide, including all Kubernetes-side inspection; the AKS area does not run kubectl.

When standard diagnostics do not reveal root cause, use Inspektor Gadget for real-time, low-level node and pod observability (DNS traces, TCP traces, process snapshots, file access traces). See references/inspektor-gadget.md for the gadget catalog, the run-ig script, and symptom-to-gadget mapping.

See references/aks-mcp.md, references/structured-input-modes.md, references/command-flows.md

The optional, consent-gated aks-skills add-on is handled by the Workflow's focused-skill checkpoint below; this baseline guide remains usable when it is not installed or cannot execute.

Required Inputs

  • subscription or active Azure context

  • resource group and cluster name

  • symptom summary

  • first observed time or recent change window

  • impacted namespace, workload, service, or ingress when known

If cluster identity is missing and no kubectl context is active, don't stop and ask immediately. Resolve the subscription from the request or confirmed existing context. If none is known, list accessible subscriptions and ask the user to select one; don't scan AKS resources across every subscription automatically. Run az aks list --subscription <subscription-id> in the selected subscription. If exactly one cluster is found, establish context with az aks get-credentials --subscription <subscription-id> --resource-group <resource-group> --name <cluster-name>, then use the relevant script's discovery/auto-select mode (e.g. pod-evidence.sh --all-failing). If zero or multiple clusters are found, ask the user to identify the target. If kubectl already has an active context, verify that its cluster matches the incident before collecting evidence. Use the context only when the user confirms it or its cluster/subscription/resource-group matches known incident details; otherwise ask which cluster to use. After confirmation, use the script's discovery/auto-select mode and ask for remaining details only if discovery comes up empty.

Scope Buckets

  • Lifecycle: create, update, start, stop, upgrade, or provisioning failures

  • API access: kubeconfig, auth, private endpoint, DNS, or reachability problems

  • Nodes: missing nodes, NotReady, pressure, CNI, kubelet, certificate, or VMSS drift

  • kube-system: CoreDNS, metrics-server, konnectivity, ingress, CNI, CSI, or add-on failures

  • Workloads: Pending, CrashLoopBackOff, OOMKilled, PVC, quota, secret, readiness, or dependency issues

  • Connectivity and DNS: pod -> service -> endpoints -> ingress/load balancer -> DNS -> network controls

  • Scaling: node pool sizing, pending pods, autoscaler config, metrics, quota, or subnet constraints

Evidence Order

  1. The classified symptom selects the first read. For lifecycle, API-access, node, scaling, quota, upgrade, or broad and unclassified symptoms, start Azure-side: cluster state, resource health, recent operations, node pool state, detector or monitoring output.

  2. For a clearly identified workload failure (a named pod in CrashLoopBackOff, ImagePullBackOff, OOMKilled, or a similar pod-local state), start with pod state, events, and current/previous logs — supplied or collected — then expand to node and Azure evidence only as the causal branch requires; no broad Azure sweep first. Otherwise Kubernetes-side state follows the Azure-side read: reachability, nodes, kube-system, events, affected namespace, pod detail, logs.

  3. Use detector, warning-event, or metrics modes when the incoming data already matches them.

  4. Deep diagnostics; when the symptom-relevant checks above do not reveal root cause, use inspektor-gadget.md for real-time tracing on the affected node.

Workflow

  1. Get cluster context.

  2. Classify the problem by scope bucket.

  3. Focused-skill checkpoint. For any incident in the Scope Buckets above, complete the handoff in optional-aks-operations.md once before gathering evidence: check the host's available-skill inventory; use the focused skill if present; if it is missing and deeper investigation would materially help the current task, make one offer and ask before installing — never install merely to interpret supplied evidence that already answers the task; if the customer declines or the host cannot install or execute it, continue with this guide and do not ask again. An application-level cause (for example a CrashLoopBackOff from bad config) does not exempt an AKS workload incident from this checkpoint.

  4. Follow the Evidence Order above.

  5. Use the matching Azure MCP area for Azure-side metadata where its schema fits, then the documented az/kubectl flows for everything else.

  6. Return evidence, failure domain, confidence, next checks, remediation, and escalation.

Error Patterns

  • No cluster context: ask for subscription, resource group, and cluster name.

  • MCP unavailable: fall back to safe az aks and kubectl reads.

  • kubectl blocked: separate auth problems from network reachability.

  • Logs or metrics missing: use events, node state, and resource descriptions.

  • Detector noise: ignore emergingIssues, prefer critical findings, rank the most actionable signal first.

Safe Fallback Checks

When the Azure MCP areas cannot perform the baseline read, run the aks-baseline script. It executes the read-only cluster + Kubernetes baseline sweep (provisioning state, node pools, activity log, node readiness, unhealthy pods, kube-system health, warning events) and returns a single labeled digest:

# bash
./scripts/aks-baseline.sh -g <resource-group> -n <cluster-name> [--namespace <namespace>]
# PowerShell
.\scripts\aks-baseline.ps1 -ResourceGroup <resource-group> -Cluster <cluster-name> [-Namespace <namespace>]

For unhealthy pods, go straight to the pod-evidence script rather than running describe/logs/top one at a time — it gathers the full read-only evidence bundle (describe, current + previous logs, resources vs usage) in a single call: ../../scripts/pod-evidence.sh / ../../scripts/pod-evidence.ps1. If the pod name isn't known yet, use --all-failing to auto-discover it instead of asking the user for it:

../../scripts/pod-evidence.sh <pod-name> -n <namespace>
../../scripts/pod-evidence.sh --all-failing
../../scripts/pod-evidence.ps1 <pod-name> -Namespace <namespace>
../../scripts/pod-evidence.ps1 -AllFailing

See pod-failures.md for how to interpret the digest.

Only fall back to the individual commands below if the bundled script is unavailable or you need a check it doesn't cover. If kubectl is missing or unconfigured, install or configure it before running Kubernetes-side checks:

az aks show -g <resource-group> -n <cluster-name>
az aks nodepool list -g <resource-group> --cluster-name <cluster-name>
kubectl cluster-info
kubectl get nodes -o wide
kubectl get pods -n kube-system
kubectl get events -A --sort-by=.lastTimestamp
kubectl describe pod <pod-name> -n <namespace>
kubectl logs <pod-name> -n <namespace> --previous

Keep these read-only unless the user explicitly asks for remediation.

Guardrails

  • default to read-only diagnostics

  • do not restart, delete, cordon, drain, scale, upgrade, or reconfigure resources unless the user explicitly asks for remediation

  • do not conclude root cause without quoting the evidence that supports it

Output Checklist

Return scope and impact, evidence, failure domain, root cause, confidence, next checks, remediation, and escalation. State once which focused optional AKS skill applied and its status: used, offered and awaiting consent, installed but not yet active, declined, or not available on this host.

Requirements and compatibility

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 a command-line tool

Security

What our scanner noticed· Low risk

  • Instructions that use the network
  • Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

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

Created by
Microsoft
Publisher
Microsoft (Official)
Source
microsoft/skills · .github/plugins/azure-skills/skills/azure-diagnostics · commit 0696600
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from .github/plugins/azure-skills/LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced
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microsoft/azure-skills

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