Agent skill · data analytics · wshobson
service-mesh-observability
Implement comprehensive observability for service meshes including distributed tracing, metrics, and visualization. Use when setting up mesh monitoring, debugging latency issues, or implementing SLOs for service communication.
Why this skill is useful
Provides specific observability patterns and metrics for service meshes that the AI wouldn't reliably generate on its own.
What it needs
About 1k tokens when loaded. Last updated 2026-08-05. 38,570 stars on the source repository.
What this skill does
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 2. Golden Signals for Mesh Signal Description Alert Threshold -------------- ------------------------- ----------------- Latency Request duration P50, P99 P99 > 500ms Traffic Requests per second Anomaly detection Errors 5xx error rate > 1% Saturation Resource 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
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills wshobson/service-mesh-observability