Service Mesh Observability

Seth HobsonCommunityCreated by Seth Hobson

Skill
Guidelines
Cloud and DevOps
MIT
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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

Download the skill as a ZIP:
https://autogineer.com/agent-skills/wshobson/service-mesh-observability/download

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

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

Files

ATTRIBUTION.mdAdded by AutoGineer1018 B
LICENSELicence file added by AutoGineer1.0 KiB
SKILL.md2.8 KiB
references/details.md7.5 KiB

references/details.md

service-mesh-observability — templates and worked examples

Templates

Template 1: Istio with Prometheus & Grafana

# Install Prometheus
apiVersion: v1
kind: ConfigMap
metadata:
  name: prometheus
  namespace: istio-system
data:
  prometheus.yml: |
    global:
      scrape_interval: 15s
    scrape_configs:
      - job_name: 'istio-mesh'
        kubernetes_sd_configs:
          - role: endpoints
            namespaces:
              names:
                - istio-system
        relabel_configs:
          - source_labels: [__meta_kubernetes_service_name]
            action: keep
            regex: istio-telemetry
---
# ServiceMonitor for Prometheus Operator
apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: istio-mesh
  namespace: istio-system
spec:
  selector:
    matchLabels:
      app: istiod
  endpoints:
    - port: http-monitoring
      interval: 15s

Template 2: Key Istio Metrics Queries

# Request rate by service
sum(rate(istio_requests_total{reporter="destination"}[5m])) by (destination_service_name)

# Error rate (5xx)
sum(rate(istio_requests_total{reporter="destination", response_code=~"5.."}[5m]))
  / sum(rate(istio_requests_total{reporter="destination"}[5m])) * 100

# P99 latency
histogram_quantile(0.99,
  sum(rate(istio_request_duration_milliseconds_bucket{reporter="destination"}[5m]))
  by (le, destination_service_name))

# TCP connections
sum(istio_tcp_connections_opened_total{reporter="destination"}) by (destination_service_name)

# Request size
histogram_quantile(0.99,
  sum(rate(istio_request_bytes_bucket{reporter="destination"}[5m]))
  by (le, destination_service_name))

Template 3: Jaeger Distributed Tracing

# Jaeger installation for Istio
apiVersion: install.istio.io/v1alpha1
kind: IstioOperator
spec:
  meshConfig:
    enableTracing: true
    defaultConfig:
      tracing:
        sampling: 100.0 # 100% in dev, lower in prod
        zipkin:
          address: jaeger-collector.istio-system:9411
---
# Jaeger deployment
apiVersion: apps/v1
kind: Deployment
metadata:
  name: jaeger
  namespace: istio-system
spec:
  selector:
    matchLabels:
      app: jaeger
  template:
    metadata:
      labels:
        app: jaeger
    spec:
      containers:
        - name: jaeger
          image: jaegertracing/all-in-one:1.50
          ports:
            - containerPort: 5775 # UDP
            - containerPort: 6831 # Thrift
            - containerPort: 6832 # Thrift
            - containerPort: 5778 # Config
            - containerPort: 16686 # UI
            - containerPort: 14268 # HTTP
            - containerPort: 14250 # gRPC
            - containerPort: 9411 # Zipkin
          env:
            - name: COLLECTOR_ZIPKIN_HOST_PORT
              value: ":9411"

Template 4: Linkerd Viz Dashboard

# Install Linkerd viz extension
linkerd viz install | kubectl apply -f -

# Access dashboard
linkerd viz dashboard

# CLI commands for observability
# Top requests
linkerd viz top deploy/my-app

# Per-route metrics
linkerd viz routes deploy/my-app --to deploy/backend

# Live traffic inspection
linkerd viz tap deploy/my-app --to deploy/backend

# Service edges (dependencies)
linkerd viz edges deployment -n my-namespace

Template 5: Grafana Dashboard JSON

{
  "dashboard": {
    "title": "Service Mesh Overview",
    "panels": [
      {
        "title": "Request Rate",
        "type": "graph",
        "targets": [
          {
            "expr": "sum(rate(istio_requests_total{reporter=\"destination\"}[5m])) by (destination_service_name)",
            "legendFormat": "{{destination_service_name}}"
          }
        ]
      },
      {
        "title": "Error Rate",
        "type": "gauge",
        "targets": [
          {
            "expr": "sum(rate(istio_requests_total{response_code=~\"5..\"}[5m])) / sum(rate(istio_requests_total[5m])) * 100"
          }
        ],
        "fieldConfig": {
          "defaults": {
            "thresholds": {
              "steps": [
                { "value": 0, "color": "green" },
                { "value": 1, "color": "yellow" },
                { "value": 5, "color": "red" }
              ]
            }
          }
        }
      },
      {
        "title": "P99 Latency",
        "type": "graph",
        "targets": [
          {
            "expr": "histogram_quantile(0.99, sum(rate(istio_request_duration_milliseconds_bucket{reporter=\"destination\"}[5m])) by (le, destination_service_name))",
            "legendFormat": "{{destination_service_name}}"
          }
        ]
      },
      {
        "title": "Service Topology",
        "type": "nodeGraph",
        "targets": [
          {
            "expr": "sum(rate(istio_requests_total{reporter=\"destination\"}[5m])) by (source_workload, destination_service_name)"
          }
        ]
      }
    ]
  }
}

Template 6: Kiali Service Mesh Visualization

# Kiali installation
apiVersion: kiali.io/v1alpha1
kind: Kiali
metadata:
  name: kiali
  namespace: istio-system
spec:
  auth:
    strategy: anonymous # or openid, token
  deployment:
    accessible_namespaces:
      - "**"
  external_services:
    prometheus:
      url: http://prometheus.istio-system:9090
    tracing:
      url: http://jaeger-query.istio-system:16686
    grafana:
      url: http://grafana.istio-system:3000

Template 7: OpenTelemetry Integration

# OpenTelemetry Collector for mesh
apiVersion: v1
kind: ConfigMap
metadata:
  name: otel-collector-config
data:
  config.yaml: |
    receivers:
      otlp:
        protocols:
          grpc:
            endpoint: 0.0.0.0:4317
          http:
            endpoint: 0.0.0.0:4318
      zipkin:
        endpoint: 0.0.0.0:9411

    processors:
      batch:
        timeout: 10s

    exporters:
      jaeger:
        endpoint: jaeger-collector:14250
        tls:
          insecure: true
      prometheus:
        endpoint: 0.0.0.0:8889

    service:
      pipelines:
        traces:
          receivers: [otlp, zipkin]
          processors: [batch]
          exporters: [jaeger]
        metrics:
          receivers: [otlp]
          processors: [batch]
          exporters: [prometheus]
---
# Istio Telemetry v2 with OTel
apiVersion: telemetry.istio.io/v1alpha1
kind: Telemetry
metadata:
  name: mesh-default
  namespace: istio-system
spec:
  tracing:
    - providers:
        - name: otel
      randomSamplingPercentage: 10

Alerting Rules

apiVersion: monitoring.coreos.com/v1
kind: PrometheusRule
metadata:
  name: mesh-alerts
  namespace: istio-system
spec:
  groups:
    - name: mesh.rules
      rules:
        - alert: HighErrorRate
          expr: |
            sum(rate(istio_requests_total{response_code=~"5.."}[5m])) by (destination_service_name)
            / sum(rate(istio_requests_total[5m])) by (destination_service_name) > 0.05
          for: 5m
          labels:
            severity: critical
          annotations:
            summary: "High error rate for {{ $labels.destination_service_name }}"

        - alert: HighLatency
          expr: |
            histogram_quantile(0.99, sum(rate(istio_request_duration_milliseconds_bucket[5m]))
            by (le, destination_service_name)) > 1000
          for: 5m
          labels:
            severity: warning
          annotations:
            summary: "High P99 latency for {{ $labels.destination_service_name }}"

        - alert: MeshCertExpiring
          expr: |
            (certmanager_certificate_expiration_timestamp_seconds - time()) / 86400 < 7
          labels:
            severity: warning
          annotations:
            summary: "Mesh certificate expiring in less than 7 days"

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

2026-11-09

AutoGineer is not affiliated with or endorsed by Seth Hobson or Seth Hobson.

Download ZIP

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

From the author

AutoGineer summary

What it does

When to use it

Advantages

Usage examples

Install

Documentation by Seth Hobson

Service Mesh Observability

When to Use This Skill

Core Concepts

1. Three Pillars of Observability

2. Golden Signals for Mesh

Templates and detailed worked examples

Best Practices

Do's

Don'ts

Files

references/details.md

service-mesh-observability — templates and worked examples

Templates

Template 1: Istio with Prometheus & Grafana

Template 2: Key Istio Metrics Queries

Template 3: Jaeger Distributed Tracing

Template 4: Linkerd Viz Dashboard

Template 5: Grafana Dashboard JSON

Template 6: Kiali Service Mesh Visualization

Template 7: OpenTelemetry Integration

Alerting Rules

Requirements and compatibility

Security

Attribution and licence

Related skills

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Gke Alert Configuration

Application Performance Observability Engineer

Distributed Debugging Devops Troubleshooter

Distributed Tracing

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