Fähigkeiten service-mesh-observability
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service-mesh-observability

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Implement Service Mesh Observability

Auch verfĂĽgbar von: wshobson

Set up comprehensive monitoring, tracing, and alerting for your service mesh deployments. Get ready-to-use configurations for Istio, Linkerd, Prometheus, Grafana, and Jaeger.

UnterstĂĽtzt: Claude Codex Code(CC)
🥉 75 Bronze
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Verwendung von "service-mesh-observability". Generate Prometheus config for Istio metrics

Erwartetes Ergebnis:

YAML ServiceMonitor with scrape configs targeting istiod endpoints, 15 second intervals, and relabel rules for mesh discovery.

Verwendung von "service-mesh-observability". Create alert for high latency

Erwartetes Ergebnis:

PrometheusRule with histogram_quantile expression for P99 latency threshold, 5 minute evaluation window, and warning severity annotation.

Sicherheitsaudit

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v1 • 2/25/2026

This skill is a documentation-only guide for service mesh observability. Static analysis flagged 55 patterns, but all are false positives: backtick commands are markdown code blocks (not execution), hardcoded URLs/IPs are configuration examples, and crypto warnings are triggered by YAML config snippets. No actual code execution, network calls, or filesystem operations occur.

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Auditiert von: claude

Qualitätsbewertung

38
Architektur
100
Wartbarkeit
87
Inhalt
50
Community
100
Sicherheit
100
Spezifikationskonformität

Was du bauen kannst

Platform Engineer Setting Up Mesh Monitoring

Deploy complete observability stack for new Istio installation with Prometheus, Grafana, and Jaeger integration.

SRE Debugging Production Latency Issues

Query distributed traces to identify bottlenecks across microservices and set up P99 latency alerts.

DevOps Team Implementing SLOs

Define and monitor service level objectives for mesh traffic with automated alerting on error rate thresholds.

Probiere diese Prompts

Basic Mesh Metrics Setup 
Generate a Prometheus ServiceMonitor configuration for scraping Istio mesh metrics with 15 second intervals.
Distributed Tracing Configuration 
Create a Jaeger deployment manifest for Istio tracing with 100 percent sampling for development environments.
Grafana Dashboard Creation 
Build a Grafana dashboard JSON with panels for request rate, error rate, P99 latency, and service topology for Istio.
Production Alerting Rules 
Write PrometheusRule alerts for high error rate above 5 percent and P99 latency over 1 second with appropriate severity labels.

Bewährte Verfahren

  • Sample traces at 100 percent in development but reduce to 1-10 percent in production to control storage costs
  • Configure alerting on golden signals: latency, traffic, errors, and saturation with appropriate thresholds
  • Use trace context propagation consistently across all services for complete request visibility

Vermeiden

  • Over-sampling traces in production leading to excessive storage costs and performance overhead
  • Ignoring metric cardinality limits causing Prometheus memory issues and slow queries
  • Deploying observability tools without dashboards or alerts that provide actionable insights

Häufig gestellte Fragen

What service meshes does this skill support?
Primary support for Istio and Linkerd with configurations for Prometheus, Grafana, Jaeger, Kiali, and OpenTelemetry integration.
Does this skill deploy resources to my cluster?
No. This skill generates configuration templates and manifests that you review and apply manually with kubectl apply.
What sampling rate should I use for tracing?
Use 100 percent in development for complete visibility. In production, start with 10 percent and adjust based on traffic volume and storage budget.
Can I use this with managed service meshes?
Yes. Configurations work with managed Istio (GKE, AKS, EKS) and Linkerd, though some installation steps may differ from self-managed deployments.
How do I correlate metrics with traces?
Use Prometheus exemplars to link metric datapoints to trace spans. Configure your tracing backend to expose trace IDs in metric metadata.
What are the golden signals for service mesh?
Latency (request duration), Traffic (requests per second), Errors (5xx rate), and Saturation (resource utilization). Alert on anomalies in these metrics.

Entwicklerdetails

Autor

sickn33

Lizenz

MIT

Repository

https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/service-mesh-observability

Ref

main

Dateistruktur

đź“„ SKILL.md