Service Mesh Observability

SkillMonitoring & ops

service-mesh-observability is a skill that guides an AI agent through implementing observability for service meshes. It covers distributed tracing, metrics collection, and visualization, and is useful when setting up mesh monitoring, debugging latency issues, or implementing SLOs for service communication.

Use Service Mesh Observability in Claude, ChatGPT or Ahel Desktop

Free. Sign in, add Service Mesh Observability and connect your AI. About a minute.

Also: Claude Code · Cursor · Codex

Then ask your AI: use the Service Mesh Observability skill

Details

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Have an AI agent that can load skills.

Service Mesh ObservabilityStart free

What your AI can do with it

  • Implement distributed tracing across service mesh traffic
  • Set up metrics collection for service communication
  • Build visualizations and dashboards for mesh monitoring
  • Debug latency issues in meshed services
  • Implement SLOs for service communication

Getting started

  1. Have an AI agent that can load skills.
  2. Add the service-mesh-observability skill to the agent's available skills.
  3. Have a service mesh in place whose traffic you want to observe.
  4. Ask the agent to set up mesh monitoring, debug latency, or implement SLOs, and it will apply the skill.

What this skill tells your AI

The instructions your AI receives, as published by wshobson/agents in plugins/cloud-infrastructure/skills/service-mesh-observability/SKILL.md and read by ahel’s review.

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

Signals

GitHub stars
40k
Forks
4k
Last commit
Sep 2026

Questions

When should this skill be used?
Use it when setting up mesh monitoring, debugging latency issues, or implementing SLOs for service communication.
What does it cover?
It covers distributed tracing, metrics, and visualization for service meshes.
Advanced
Item type
skill
Key
service-mesh-observability
Source
github.com/wshobson/agents