service-mesh-observability
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.
Best use case
service-mesh-observability is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. 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.
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.
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "service-mesh-observability" skill to help with this workflow task. Context: 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.
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/service-mesh-observability/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How service-mesh-observability Compares
| Feature / Agent | service-mesh-observability | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Service Mesh Observability
Complete guide to observability patterns for Istio, Linkerd, and service mesh deployments.
## Do not use this skill when
- The task is unrelated to service mesh observability
- You need a different domain or tool outside this scope
## Instructions
- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
- If detailed examples are required, open `resources/implementation-playbook.md`.
## Use this skill when
- 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
### Template 1: Istio with Prometheus & Grafana
```yaml
# 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
```promql
# 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
```yaml
# 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
```bash
# 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
```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
```yaml
# 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
```yaml
# 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
```yaml
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"
```
## 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
## Resources
- [Istio Observability](https://istio.io/latest/docs/tasks/observability/)
- [Linkerd Observability](https://linkerd.io/2.14/features/dashboard/)
- [OpenTelemetry](https://opentelemetry.io/)
- [Kiali](https://kiali.io/)Related Skills
monitoring-observability
Set up monitoring, logging, and observability for applications and infrastructure. Use when implementing health checks, metrics collection, log aggregation, or alerting systems. Handles Prometheus, Grafana, ELK Stack, Datadog, and monitoring best practices.
service-mesh-expert
Expert service mesh architect specializing in Istio, Linkerd, and cloud-native networking patterns. Masters traffic management, security policies, observability integration, and multi-cluster mesh con
observability-monitoring-monitor-setup
You are a monitoring and observability expert specializing in implementing comprehensive monitoring solutions. Set up metrics collection, distributed tracing, log aggregation, and create insightful da
observability-engineer
Build production-ready monitoring, logging, and tracing systems. Implements comprehensive observability strategies, SLI/SLO management, and incident response workflows. Use PROACTIVELY for monitoring infrastructure, performance optimization, or production reliability.
microservices-patterns
Design microservices architectures with service boundaries, event-driven communication, and resilience patterns. Use when building distributed systems, decomposing monoliths, or implementing microservices.
freshservice-automation
Automate Freshservice ITSM tasks via Rube MCP (Composio): create/update tickets, bulk operations, service requests, and outbound emails. Always search tools first for current schemas.
database-migrations-migration-observability
Migration monitoring, CDC, and observability infrastructure
azure-servicebus-ts
Build messaging applications using Azure Service Bus SDK for JavaScript (@azure/service-bus). Use when implementing queues, topics/subscriptions, message sessions, dead-letter handling, or enterprise messaging patterns.
azure-servicebus-py
Azure Service Bus SDK for Python messaging. Use for queues, topics, subscriptions, and enterprise messaging patterns. Triggers: "service bus", "ServiceBusClient", "queue", "topic", "subscription", "message broker".
azure-servicebus-dotnet
Azure Service Bus SDK for .NET. Enterprise messaging with queues, topics, subscriptions, and sessions. Use for reliable message delivery, pub/sub patterns, dead letter handling, and background processing. Triggers: "Service Bus", "ServiceBusClient", "ServiceBusSender", "ServiceBusReceiver", "ServiceBusProcessor", "message queue", "pub/sub .NET", "dead letter queue".
azure-mgmt-botservice-py
Azure Bot Service Management SDK for Python. Use for creating, managing, and configuring Azure Bot Service resources. Triggers: "azure-mgmt-botservice", "AzureBotService", "bot management", "conversational AI", "bot channels".
azure-mgmt-botservice-dotnet
Azure Resource Manager SDK for Bot Service in .NET. Management plane operations for creating and managing Azure Bot resources, channels (Teams, DirectLine, Slack), and connection settings. Triggers: "Bot Service", "BotResource", "Azure Bot", "DirectLine channel", "Teams channel", "bot management .NET", "create bot".