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/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.

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

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/service-mesh-observability

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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.

SKILL.md

service-mesh-observability.SKILL.md
name: service-mesh-observability
description: 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.

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

| 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
Read more
Ships withwshobson-agents

Production-ready agentic workflow building blocks: 94 plugins, 202 agents, 183 skills, 105 commands — built for Claude Code and consumed natively by OpenAI Codex CLI, Cursor, OpenCode, the Antigravity CLI, GitHub Copilot, and Pi from a single Markdown source.

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