SkillsLib.ai

Service Mesh Diagnostic Analyzer

Diagnose and fix service mesh issues fast

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10+ downloads
Updated Oct 2026

What You Can Do

Analyze your Kubernetes service mesh configurations (Istio, Linkerd, Consul, etc.) to identify misconfigurations, security gaps, and performance bottlenecks. The analyzer examines your mesh setup, generates a detailed diagnostic report, and provides step-by-step remediation guidance to resolve issues without downtime.

Features

Configuration validation

Detect malformed or incompatible mesh configurations automatically

mTLS and certificate analysis

Verify TLS settings, certificate chains, and authentication policies

Traffic routing review

Audit VirtualServices, DestinationRules, and traffic policies for errors

Security posture assessment

Identify overly permissive AuthorizationPolicies and RBAC misconfigurations

Performance bottleneck detection

Find inefficient retry policies, timeout settings, and load balancing issues

Dependency graph analysis

Map service-to-service communication flows and identify anomalies

Conflict detection

Catch policy overlaps, competing rules, and configuration contradictions

Automated remediation

Generate YAML patches and step-by-step fix instructions for identified issues

Example Output

Example 1: Istio VirtualService Analysis

code
Diagnosis: VirtualService 'payment-vs' has no matching DestinationRule
Severity: High
Impact: Traffic defaults to round-robin; no mTLS enforcement

Remediation:
1. Create DestinationRule 'payment-dr' for host 'payment.default.svc.cluster.local'
2. Add trafficPolicy.tls.mode: ISTIO_MUTUAL
3. Apply: kubectl apply -f payment-dr.yaml

Example 2: mTLS Misconfiguration

code
Diagnosis: PeerAuthentication 'strict' enforces mTLS but AuthorizationPolicy missing
Severity: Medium
Impact: Clients can't communicate until AuthorizationPolicy is created

Remediation:
1. Add AuthorizationPolicy rule allowing ingress from namespace 'default'
2. Use action: ALLOW with source.namespaces
3. Test with: kubectl exec <pod> -- curl http://service:8080

Example 3: Performance Optimization

code
Diagnosis: Retry policy attempts 5 times with 100ms backoff; no circuit breaker
Severity: Medium
Impact: Cascade failures; slow error detection

Remediation:
1. Reduce maxRetries to 2 for faster failure detection
2. Add outlierDetection with consecutiveErrors: 5, baseEjectionTime: 30s
3. Expected result: Circuit-breaks bad backends in <30 seconds

What's Included

  • SKILL.md: The diagnostic analyzer framework and decision logic
  • Configuration templates: Example Istio, Linkerd, and Consul YAML manifests
  • Troubleshooting checklist: Common issues organized by symptom
  • Decision tree: Step-by-step guide for isolating root causes
  • Remediation templates: Pre-built YAML patches for common fixes
  • Reference guide: Quick lookup for mesh concepts and policy syntax

Who It's For

  • Platform Engineers — Building and maintaining Kubernetes service meshes at scale
  • DevOps Engineers — Troubleshooting service mesh deployments and configurations
  • Site Reliability Engineers (SREs) — Optimizing mesh performance and reliability
  • Kubernetes cluster administrators — Managing mesh upgrades and policies
  • Cloud architects — Designing multi-cluster or multi-mesh architectures

Best For

  • Debugging service-to-service communication failures — Isolate mesh-related connectivity issues
  • Validating configurations before production — Catch misconfigurations before they cause outages
  • Troubleshooting mTLS and TLS issues — Diagnose certificate and authentication problems
  • Identifying performance bottlenecks — Find inefficient policies and routing rules
  • Ensuring security compliance — Audit AuthorizationPolicies and RBAC settings

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