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Service Mesh Observability

Seth HobsonCommunityCreated by Seth Hobson

  • Skill
  • Guidelines
  • Cloud and DevOps
  • MIT
  • No issues found
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From the author

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.
— the description by Seth Hobson

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured approach to monitoring service meshes like Istio and Linkerd. It covers the three pillars of observability—metrics, traces, and logs—and defines golden signals for effective mesh health monitoring. The guide includes practical templates for Prometheus, Grafana, and Jaeger, alongside best practices for sampling, alerting, and cost management in production environments.

What it does

  • Defines core observability pillars for service mesh environments
  • Establishes golden signals for monitoring latency, traffic, errors, and saturation
  • Provides configuration templates for Istio and Prometheus integration
  • Offers PromQL queries for tracking request rates, error rates, and P99 latency
  • Details deployment patterns for Jaeger distributed tracing
  • Outlines CLI commands for Linkerd visualization and traffic inspection

When to use it

  • When setting up monitoring for a new service mesh deployment
  • When debugging latency bottlenecks or intermittent service errors
  • When defining and implementing Service Level Objectives (SLOs)
  • When visualizing complex service dependencies within a cluster
  • When troubleshooting connectivity issues between microservices

Advantages

  • Standardizes observability practices across different mesh technologies
  • Includes ready-to-use templates for common monitoring stacks
  • Provides actionable PromQL queries for immediate implementation
  • Balances performance monitoring with cost-conscious sampling strategies

Usage examples

“How do I calculate the P99 latency for my services in Istio?”
The skill provides a specific PromQL query using histogram_quantile to calculate P99 latency based on istio_request_duration_milliseconds_bucket metrics.
“What are the best practices for sampling traces in production?”
The guide recommends 100% sampling in development environments and 1-10% in production to balance visibility with storage costs.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/wshobson/service-mesh-observability/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip service-mesh-observability.zip -d .agents/skills

Documentation by Seth Hobson

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
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

Files

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
Seth Hobson
Publisher
Seth Hobson (Community)
Source
wshobson/agents · plugins/cloud-infrastructure/skills/service-mesh-observability · commit 4bde316
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by Seth Hobson.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download service-mesh-observability.zip