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Skills/DevOps & CI/CD
wshobson avatar

Service Mesh Observability

wshobson/agents

A solid implementation guide for getting observability working across Istio and Linkerd deployments. Sets up the full stack with Prometheus metrics, Jaeger tracing, and Grafana dashboards, plus includes the specific PromQL queries you actually need for golden signals like P99 latency and error rates. The templates cover both quick Linkerd CLI debugging and production Kiali topology visualization. Honest take: the Istio setup is more complex but gives you finer control, while Linkerd's built-in viz extension gets you running faster. Most useful when you're past the basic mesh setup and need to debug why services are slow or failing.

installs
10.4K
GitHub stars
41.8K
last updated
Aug 21, 2026
View on GitHubSave

Install

$ npx -y skills add wshobson/agents --skill service-mesh-observability --agent claude-code

Installs into .claude/skills of the current project.

Files

Service Mesh Observability

Complete guide to observability patterns for Istio, Linkerd, and service mesh deployments.

When to Use This Skill

  • Setting up distributed tracing across services
  • Implementing service mesh metrics and dashboards
  • Debugging latency and error issues
  • Defining SLOs for service communication
  • Visualizing service dependencies
  • Troubleshooting mesh connectivity

Core Concepts

1. Three Pillars of Observability

┌─────────────────────────────────────────────────────┐
│                  Observability                       │
├─────────────────┬─────────────────┬─────────────────┤
│     Metrics     │     Traces      │      Logs       │
│                 │                 │                 │
│ • Request rate  │ • Span context  │ • Access logs   │
│ • Error rate    │ • Latency       │ • Error details │
│ • Latency P50   │ • Dependencies  │ • Debug info    │
│ • Saturation    │ • Bottlenecks   │ • Audit trail   │
└─────────────────┴─────────────────┴─────────────────┘

2. Golden Signals for Mesh

SignalDescriptionAlert Threshold
LatencyRequest duration P50, P99P99 > 500ms
TrafficRequests per secondAnomaly detection
Errors5xx error rate> 1%
SaturationResource utilization> 80%

Templates and detailed worked examples

Full template library and detailed worked examples live in references/details.md. Read that file when you need the concrete templates.

Best Practices

Do's

  • Sample appropriately - 100% in dev, 1-10% in prod
  • Use trace context - Propagate headers consistently
  • Set up alerts - For golden signals
  • Correlate metrics/traces - Use exemplars
  • Retain strategically - Hot/cold storage tiers

Don'ts

  • Don't over-sample - Storage costs add up
  • Don't ignore cardinality - Limit label values
  • Don't skip dashboards - Visualize dependencies
  • Don't forget costs - Monitor observability costs
SKILL.md on GitHub

Sponsors

SnugThe clipboard manager you and your agent can use, safelyAgent CaffeineClose your lid, come back to finished workWaxBlock anything with supervision, without erasing your iPhoneOpen StepsSponsoredFree and open-source skills that make AI coding agents easier to understand, verify, and control.

Details

Repository
github.com/wshobson/agents
Categories
DevOps & CI/CD, Data Science & ML, Debugging

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