Service Mesh Observability

Service Mesh Observability

Overview

This public intake copy packages plugins/antigravity-awesome-skills-claude/skills/service-mesh-observability from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

Service Mesh Observability Complete guide to observability patterns for Istio, Linkerd, and service mesh deployments.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Core Concepts, Templates, Limitations.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• The task is unrelated to service mesh observability
• You need a different domain or tool outside this scope
• Setting up distributed tracing across services
• Implementing service mesh metrics and dashboards
• Debugging latency and error issues
• Defining SLOs for service communication

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Clarify goals, constraints, and required inputs.
2. Apply relevant best practices and validate outcomes.
3. Provide actionable steps and verification.
4. If detailed examples are required, open resources/implementation-playbook.md.
5. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
6. Read the overview and provenance files before loading any copied upstream support files.
7. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.

Imported Workflow Notes

Imported: 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.

Imported: 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

Examples

Example 1: Ask for the upstream workflow directly

Use @service-mesh-observability to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @service-mesh-observability against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @service-mesh-observability for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @service-mesh-observability using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• name: mesh.rules
• alert: HighErrorRate
• alert: HighLatency
• alert: MeshCertExpiring
• Sample appropriately - 100% in dev, 1-10% in prod
• Use trace context - Propagate headers consistently
• Set up alerts - For golden signals

Imported Operating Notes

Imported: 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"

Imported: 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

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills-claude/skills/service-mesh-observability, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

Imported Reference Notes

Imported: Resources

• Istio Observability
• Linkerd Observability
• OpenTelemetry
• Kiali

Imported: 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

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.