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
- 01Language-specific instrumentation guidance
- 02Guidance for spans, logs, and metrics
- 03Resource and Kubernetes configuration
- 04Sensitive-data handling
- 05Post-deployment telemetry validation
- 06Collector-based sampling guidance
Use cases
- 01Instrument a new application with OpenTelemetry
- 02Add telemetry to a supported language stack
- 03Review telemetry for PII exposure
- 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-instrumentationAfter installing, type/otel-instrumentationin Claude Code to confirm it loaded.
Bundled files
References and scripts installed along with SKILL.md.
README.mdrules/capture-database-query-parameters.mdlogs.mdmetrics.mdresolve-values.mdresources.mdsensitive-data.mdspans.mdtelemetry.mdvalidation.mdverify-dependencies.mdrules/platforms/k8s.mdrules/sdks/browser.mddotnet.mdgo.mdjava.mdnextjs.mdnodejs.mdphp.mdpython.mdruby.mdscala.mdDetails
From the project READMEOpenTelemetry Instrumentation Guide
Expert guidance for implementing high-quality, cost-efficient OpenTelemetry telemetry.
Rules & Quick Reference
| Use Case / Rule | Description |
|---|---|
| telemetry | Entrypoint — signal types, correlation, and navigation |
| resolve-values | Resolving configuration values from the codebase |
| verify-dependencies | Verifying instrumentation packages and versions exist before adding them |
| resources | Resource attributes — service identity and environment |
| k8s | Kubernetes deployment — downward API, pod spec |
| spans | Spans — naming, kind, status, and hygiene |
| logs | Logs — structured logging, severity, trace correlation |
| metrics | Metrics — instrument types, naming, units, cardinality |
| sensitive-data | Sensitive data — PII prevention, sanitization, redaction |
| capture-database-query-parameters | Prepared-statement parameter capture per language (Java, .NET, Python, Node.js, Go) |
| validation | Telemetry validation — post-deployment verification checklist |
| nodejs | Node.js instrumentation setup |
| go | Go instrumentation setup |
| python | Python instrumentation setup |
| java | Java instrumentation setup |
| scala | Scala instrumentation setup |
| dotnet | .NET instrumentation setup |
| ruby | Ruby instrumentation setup |
| php | PHP instrumentation setup |
| browser | Browser instrumentation setup |
| nextjs | Next.js full-stack instrumentation (App Router) |
Official documentation
Getting started
Follow these steps when instrumenting an application from scratch:
- Pick your SDK rule — choose the language-specific rule from the table above (e.g., nodejs, python).
- Set up resource attributes — define service identity and environment per resources.
- Add spans, metrics, and logs — instrument your code following spans, metrics, and logs.
- Guard sensitive data — scrub PII before export per sensitive-data.
- 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