Otel Instrumentation logo

Otel Instrumentation

Organization
dash0hq
otel-instrumentation

Configures trace spans, defines custom metrics, sets up log exporters, and optimizes sampling strategies for OpenTelemetry instrumentation. Use when instrumenting applications with traces, metrics, or logs. Triggers on requests for observability, telemetry, tracing, metrics collection, logging integration, or OTel setup.

Overview

Publisherdash0hq
Repositoryagent-skills
Skill nameotel-instrumentation
Stars
98
Forks
16
Bundled files
21
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 21 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by dash0hq on GitHub. Read the source before you install it.

Installation

Install the Otel Instrumentation AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/dash0hq/agent-skills/tree/main/skills/otel-instrumentation
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/dash0hq/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/skills/otel-instrumentation .claude/skills/otel-instrumentation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Otel Instrumentation in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Otel Instrumentation on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Otel Instrumentation is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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:

js
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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Otel Instrumentation AI skill do?

Configures trace spans, defines custom metrics, sets up log exporters, and optimizes sampling strategies for OpenTelemetry instrumentation. Use when instrumenting applications with traces, metrics, or logs. Triggers on requests for observability, telemetry, tracing, metrics collection, logging integration, or OTel setup.

Why use Otel Instrumentation on TypingMind?

Because you install it once and use it with any model. Otel Instrumentation is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Otel Instrumentation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/dash0hq/agent-skills/tree/main/skills/otel-instrumentation. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Otel Instrumentation?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Otel Instrumentation?

As many as you like. As long as a model supports skills, you can use Otel Instrumentation with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Otel Instrumentation AI skill free?

Yes. It is published on GitHub by dash0hq under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇