Opentelemetry

This is your reference when you need to instrument services with OpenTelemetry and actually get telemetry flowing to a backend. It covers the full stack: auto-instrumentation and manual spans for traces, metrics, structured logs with trace correlation, OTLP export, and Collector pipeline configuration including processors and tail sampling. The progressive disclosure keeps the entry point light (resource attributes, quick OTLP setup) but the full skill runs about 9000 tokens across concepts, semantic conventions, context propagation, and troubleshooting cardinality or sampling issues. Honest take: OpenTelemetry has a lot of moving parts, and this gives you the patterns to avoid the common pitfalls like breaking trace context or blowing up metric cardinality.

skills.sh installs: 647
GitHub stars: 79
last updated: Aug 21, 2026

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Install

$ npx -y skills add bobmatnyc/claude-mpm-skills --skill opentelemetry --agent claude-code

Installs into .claude/skills of the current project.

Files

OpenTelemetry

Quick Start (signal design)

Export OTLP via an OpenTelemetry Collector (vendor-neutral endpoint).
Standardize resource attributes: service.name, service.version, deployment.environment.
Start with auto-instrumentation, then add manual spans and log correlation.

Load Next (References)

references/concepts.md — traces/metrics/logs, context propagation, sampling, semantic conventions
references/collector-and-otlp.md — Collector pipelines, processors, deployment patterns, tail sampling
references/instrumentation-and-troubleshooting.md — manual spans, propagation pitfalls, cardinality, debugging

SKILL.md on GitHub

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