opentelemetry

Q: How do I install opentelemetry?

Run npx skills add https://github.com/julianobarbosa/claude-code-skills --skill opentelemetry in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/opentelemetry/ (personal) or .claude/skills/opentelemetry/ (project).

by julianobarbosaDevOps & Cloud

Implement OpenTelemetry (OTEL) observability - Collector configuration, Kubernetes deployment, traces/metrics/logs pipelines, instrumentation, and troubleshooting. Use when working with OTEL Collector, telemetry pipelines, observability infrastructure, or Kubernetes monitoring.

Installs: 134
Last 24h: —
Trending rank: —
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/julianobarbosa/claude-code-skills --skill opentelemetry

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads opentelemetry automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

OpenTelemetry Implementation Guide

Overview

OpenTelemetry (OTel) is a vendor-neutral observability framework for instrumenting, generating, collecting, and exporting telemetry data (traces, metrics, logs). This skill provides guidance for implementing OTEL in Kubernetes environments.

Quick Start

Deploy OTEL Collector on Kubernetes

# Add Helm repo
helm repo add open-telemetry https://open-telemetry.github.io/opentelemetry-helm-charts
helm repo update

# Install with basic config
helm install otel-collector open-telemetry/opentelemetry-collector \
  --namespace monitoring --create-namespace \
  --set mode=daemonset

Send Test Data via OTLP

# gRPC endpoint: 4317, HTTP endpoint: 4318
curl -X POST http://otel-collector:4318/v1/traces \
  -H "Content-Type: application/json" \
  -d '{"resourceSpans":[]}'

Core Concepts

Signals: Three types of telemetry data:

Traces: Distributed request flows across services
Metrics: Numerical measurements (counters, gauges, histograms)
Logs: Event records with structured/unstructured data

Collector Components:

Receivers: Accept data (OTLP, Prometheus, Jaeger, Zipkin)
Processors: Transform data (batch, memory_limiter, k8sattributes)
Exporters: Send data (prometheusremotewrite, loki, otlp)
Extensions: Add capabilities (health_check, pprof, zpages)

Collector Configuration

Basic Pipeline Structure

config:
  receivers:
    otlp:
      protocols:
        grpc:
          endpoint: ${env:MY_POD_IP}:4317
        http:
          endpoint: ${env:MY_POD_IP}:4318

  processors:
    batch:
      timeout: 10s
      send_batch_size: 1024
    memory_limiter:
      check_interval: 5s
      limit_percentage: 80
      spike_limit_percentage: 25

  exporters:
    prometheusremotewrite:
      endpoint: "http://prometheus:9090/api/v1/write"
    loki:
      endpoint: "http://loki:3100/loki/api/v1/push"

  service:
    pipelines:
      metrics:
        receivers: [otlp]
        processors: [memory_limiter, batch]
        exporters: [prometheusremotewrite]
      logs:
        receivers: [otlp]
        processors: [memory_limiter, batch]
        exporters: [loki]
      traces:
        receivers: [otlp]
        processors: [memory_limiter, batch]
        exporters: [otlp/tempo]

Kubernetes Attributes Enrichment

processors:
  k8sattributes:
    auth_type: "serviceAccount"
    passthrough: false
    filter:
      node_from_env_var: ${env:K8S_NODE_NAME}
    extract:
      metadata:
        - k8s.pod.name
        - k8s.namespace.name
        - k8s.deployment.name
        - k8s.node.name

Deployment Modes

Mode	Use Case	Pros	Cons
DaemonSet	Node-level collection	Full coverage, host metrics	Higher resource usage
Deployment	Centralized gateway	Scalable, easier management	Single point of failure
Sidecar	Per-pod collection	Isolated, fine-grained	Resource overhead per pod

Common Patterns

Development Environment

Enable debug exporter for visibility
Lower resource limits (250m CPU, 512Mi memory)
Include spot instance tolerations for cost savings

Production Environment

Implement sampling (10-50% for traces)
Higher batch sizes (2048-4096)
Enable autoscaling and PodDisruptionBudget
Use TLS for all endpoints

Detailed References

For in-depth guidance, see:

Collector Configuration: COLLECTOR.md
Kubernetes Deployment: KUBERNETES.md
Troubleshooting: TROUBLESHOOTING.md
Instrumentation: INSTRUMENTATION.md

Validation Commands

# Check collector pods
kubectl get pods -n monitoring -l app.kubernetes.io/name=otel-collector

# View collector logs
kubectl logs -n monitoring -l app.kubernetes.io/name=otel-collector --tail=100

# Test OTLP endpoint
kubectl run test-otlp --image=curlimages/curl:latest --rm -it -- \
  curl -v http://otel-collector.monitoring:4318/v1/traces

# Validate config syntax
otelcol validate --config=config.yaml

Key Helm Chart Values

mode: "daemonset"  # or "deployment"
presets:
  logsCollection:
    enabled: true
  hostMetrics:
    enabled: true
  kubernetesAttributes:
    enabled: true
  kubeletMetrics:
    enabled: true
useGOMEMLIMIT: true
resources:
  limits:
    cpu: 500m
    memory: 1Gi
  requests:
    cpu: 100m
    memory: 256Mi

Gotchas

Collector tail_sampling at end of pipeline doesn't release inflight buffer — drop decisions made late still hold memory; OOM under load.
Auto-instrumentation + manual instrumentation can double-count traces — pick one strategy per service or de-dupe explicitly via sampler.
Context propagation: HTTP B3 vs W3C traceparent headers don't auto-convert; mixed environments silently break trace continuity.
OTel SDK vs Collector protocol versions: minor version mismatches can drop attributes silently (especially semantic-convention attributes).
Resource detection adds platform attributes (cloud.account.id, host.id) that bloat traces — disable when not used for routing.
Batch processor + memory limiter ordering: limiter must precede batch in the pipeline or memory pressure causes batch drops without backpressure.

FAQ

What does the opentelemetry skill do?

How do I install opentelemetry?

Run npx skills add https://github.com/julianobarbosa/claude-code-skills --skill opentelemetry in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/opentelemetry/ (personal) or .claude/skills/opentelemetry/ (project).

Which AI agents can use opentelemetry?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is opentelemetry safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains opentelemetry?

It is published by julianobarbosa in julianobarbosa/claude-code-skills. It has 134 recorded installs.