技能 service-mesh-observability
📦

service-mesh-observability

安全

實作服務網格可觀測性

也可從以下取得: wshobson

為您的服務網格部署設置全面的監控、追蹤和告警。取得適用於 Istio、Linkerd、Prometheus、Grafana 和 Jaeger 的可即用配置。

支援: Claude Codex Code(CC)
🥉 75 青銅
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前往 設定 → 功能 → 技能 → 上傳技能

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測試它

正在使用「service-mesh-observability」。 Generate Prometheus config for Istio metrics

預期結果:

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

正在使用「service-mesh-observability」。 Create alert for high latency

預期結果:

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

安全審計

安全
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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審計者: claude

品質評分

38
架構
100
可維護性
87
內容
50
社群
100
安全
100
規範符合性

你能建構什麼

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.

試試這些提示

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.

最佳實務

  • 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

避免

  • 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

常見問題

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.

開發者詳情

作者

sickn33

授權

MIT

儲存庫

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

引用

main

檔案結構

📄 SKILL.md