Service Mesh Observability
SkillMonitoring & opsservice-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
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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.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
No other account needed.
Have an AI agent that can load skills.
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
- Have an AI agent that can load skills.
- Add the service-mesh-observability skill to the agent's available skills.
- Have a service mesh in place whose traffic you want to observe.
- 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
| 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
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
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