service-mesh-observability
🎯SkillHelps you see what happens as the parts of a large app talk to each other, so you can find slow or failing requests. It guides you in setting up request tracking, key health numbers, dashboards and alerts.
Installation
npx skills add wshobson/agents --skill service-mesh-observabilityRun it in a terminal; it asks which AI coding tools to install it for (Claude Code, Codex, Cursor and more).
Uses skills.sh · Node.js 22.20+
After installing
Ask for related work and the agent uses it on its own, or call it yourself.
/service-mesh-observabilityInstall by hand
Copy the whole skill folder from GitHub to the place below. Copying only SKILL.md can leave out files the skill uses.
Open the skill folder →- This project
.claude/skills/service-mesh-observability/ - All projects
~/.claude/skills/service-mesh-observability/
These paths are for Claude Code. For other tools, the command above puts it in the right place.
What this skill does
Overview
This skill helps an AI assistant set up monitoring for a service mesh. A service mesh is a layer that manages how the many small services in an app talk to each other. It covers Istio and Linkerd. It uses three kinds of data: metrics (numbers over time), traces (the path of one request) and logs (event records).
Key Features
- Follow a single request across services to find where it slows down.
- Track four key signals: speed, traffic, errors and how busy your resources are.
- Set alert levels, such as a warning when more than 1% of requests fail.
- Build dashboards that show which services depend on which.
- Keep storage costs down by recording only a sample of requests in production.
Who is this for?
It suits teams running many services on Istio or Linkerd who need to find the cause of slow or failing calls. It also helps teams setting SLOs, which are reliability targets such as how fast most requests must finish.
Summarized by AI from this skill's SKILL.md. Suggest a better one
SKILL.md
View on GitHub →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
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