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otel-instrumentation

otel-instrumentation

github.com/dash0hqDevelopment98984 installsUpdated 1w ago
Repository

About otel-instrumentation

otel-instrumentation is a practical guide for developers adding OpenTelemetry to applications. It organizes guidance by signal, language, deployment, and validation, with attention to data hygiene and cost-conscious sampling.

Key features

  1. 01Language-specific instrumentation guidance
  2. 02Guidance for spans, logs, and metrics
  3. 03Resource and Kubernetes configuration
  4. 04Sensitive-data handling
  5. 05Post-deployment telemetry validation
  6. 06Collector-based sampling guidance

Use cases

  1. 01Instrument a new application with OpenTelemetry
  2. 02Add telemetry to a supported language stack
  3. 03Review telemetry for PII exposure
  4. 04Validate telemetry after deployment

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/dash0hq/agent-skills --skill otel-instrumentation

After installing, type/otel-instrumentationin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

README.md
rules/
capture-database-query-parameters.md
logs.md
metrics.md
resolve-values.md
resources.md
sensitive-data.md
spans.md
telemetry.md
validation.md
verify-dependencies.md
rules/platforms/
k8s.md
rules/sdks/
browser.md
dotnet.md
go.md
java.md
nextjs.md
nodejs.md
php.md
python.md
ruby.md
scala.md

Details

From the project README

OpenTelemetry Instrumentation Guide

Expert guidance for implementing high-quality, cost-efficient OpenTelemetry telemetry.

Rules & Quick Reference

Use Case / RuleDescription
telemetryEntrypoint — signal types, correlation, and navigation
resolve-valuesResolving configuration values from the codebase
verify-dependenciesVerifying instrumentation packages and versions exist before adding them
resourcesResource attributes — service identity and environment
k8sKubernetes deployment — downward API, pod spec
spansSpans — naming, kind, status, and hygiene
logsLogs — structured logging, severity, trace correlation
metricsMetrics — instrument types, naming, units, cardinality
sensitive-dataSensitive data — PII prevention, sanitization, redaction
capture-database-query-parametersPrepared-statement parameter capture per language (Java, .NET, Python, Node.js, Go)
validationTelemetry validation — post-deployment verification checklist
nodejsNode.js instrumentation setup
goGo instrumentation setup
pythonPython instrumentation setup
javaJava instrumentation setup
scalaScala instrumentation setup
dotnet.NET instrumentation setup
rubyRuby instrumentation setup
phpPHP instrumentation setup
browserBrowser instrumentation setup
nextjsNext.js full-stack instrumentation (App Router)

Official documentation

Getting started

Follow these steps when instrumenting an application from scratch:

  1. Pick your SDK rule — choose the language-specific rule from the table above (e.g., nodejs, python).
  2. Set up resource attributes — define service identity and environment per resources.
  3. Add spans, metrics, and logs — instrument your code following spans, metrics, and logs.
  4. Guard sensitive data — scrub PII before export per sensitive-data.
  5. Validate — confirm telemetry reaches the backend using the checklist in validation.

The snippet below shows a complete span with attributes and status for Node.js — see nodejs for full setup including SDK initialisation, exporter configuration, and auto-instrumentation:

import { trace, SpanStatusCode } from '@opentelemetry/api';
const tracer = trace.getTracer('my-service', '1.0.0');

tracer.startActiveSpan('operation-name', async (span) => {
  try {
    span.setAttribute('user.id', userId);
    span.setAttribute('order.id', orderId);

    const result = await processOrder(orderId);

    span.setAttribute('order.status', result.status);
    span.setStatus({ code: SpanStatusCode.OK });
    return result;
  } catch (err) {
    // Record the exception as a structured log record, not span.recordException — see rules/spans.md
    span.setStatus({ code: SpanStatusCode.ERROR, message: `${err.name}: ${err.message}` });
    const spanContext = span.spanContext();
    logger.error('operation-name.failed', {
      'trace_id': spanContext.traceId,
      'span_id': spanContext.spanId,
      'exception.type': err.name,
      'exception.message': err.message,
      'exception.stacktrace': err.stack,
    });
    throw err;
  } finally {
    span.end();
  }
});

Key principles

Signal density over volume

Every telemetry item should serve one of three purposes:

  • Detect - Help identify that something is wrong
  • Localize - Help pinpoint where the problem is
  • Explain - Help understand why it happened

If it doesn't serve one of these purposes, don't emit it.

Sample in the pipeline, not the SDK

Set the AlwaysOn sampler explicitly in every SDK (OTEL_TRACES_SAMPLER=always_on). This overrides the OpenTelemetry default, ParentBased(root=AlwaysOn), under which callers outside the trust boundary can silently switch off a service's tracing through the traceparent header. Do not configure ratio-based SDK samplers either — they make irreversible decisions before the outcome of a request is known. Defer all sampling to the Collector, where policies can be changed centrally without redeploying applications. See SDK sampling for the full rationale and the conditions under which ParentBased is acceptable.

SDK (AlwaysOn, set explicitly)  →  Collector (sampling)  →  Backend (retention)
             ↓                           ↓                       ↓
       All spans                   Head or tail            Storage policies
       exported                    sampling applied

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