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opentelemetry

Instrument Go, Python, and Node.js services with OpenTelemetry and configure collectors.

Updated Apr 3, 2026
Search Tags:#metrics#kubernetes#observability#opentelemetry#distributed-tracing#jaeger#tempo
One-click install
npx skills add https://github.com/kettleofketchup/KettleOfSkills --skill opentelemetry-kettleofketchup
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: opentelemetry
Source: https://github.com/kettleofketchup/KettleOfSkills/tree/main/plugins/opentelemetry/skills/opentelemetry
Command: npx skills add https://github.com/kettleofketchup/KettleOfSkills --skill opentelemetry-kettleofketchup

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill eliminates the complexity of implementing vendor-neutral observability across distributed applications, removing the need to manually piece together tracing, metrics, and logging solutions for different programming languages and frameworks.

Core Features & Use Cases

  • Multi-Language Instrumentation: Supports Go, Python (Django, FastAPI), and Node.js (Express, NestJS, Fastify) with auto and manual instrumentation patterns.
  • Infrastructure Deployment: Guides for deploying OpenTelemetry Collectors on Kubernetes via DaemonSet, Sidecar, or Gateway modes.
  • Backend Integration: Configuration for Jaeger and Tempo/Grafana trace storage, including TraceQL query patterns and performance analysis.
  • Use Case: For a team running a polyglot microservices stack, use this Skill to instrument all services, collect traces in Tempo, correlate logs with trace IDs, and identify latency bottlenecks via span analysis.

Quick Start

Use the opentelemetry skill to instrument your FastAPI service with tracing, configure it to send spans to a Tempo backend on Kubernetes, and set up log correlation to link error logs to their corresponding traces.

Frequently Asked Questions about opentelemetry

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I implement distributed tracing across Go, Python, and Node.js microservices?▼

Implement distributed tracing across polyglot microservices by applying OpenTelemetry auto and manual instrumentation patterns for Go, Python (Django, FastAPI), and Node.js (Express, NestJS) to achieve consistent observability and track requests end-to-end.

What is the best way to deploy the OpenTelemetry Collector on Kubernetes?▼

Deploying the OpenTelemetry Collector on Kubernetes is best achieved via DaemonSet, Sidecar, or Gateway modes to collect telemetry data, which can then be routed to backends like Jaeger or Tempo for performance analysis.

How do I correlate logs with trace IDs in FastAPI?▼

Correlate logs with trace IDs in FastAPI by configuring OpenTelemetry instrumentation to inject trace-context into application logs, linking error logs directly to their corresponding distributed traces in backends like Tempo or Jaeger.

Does OpenTelemetry support sending spans to a Tempo backend with TraceQL?▼

Yes, OpenTelemetry supports sending spans to a Tempo/Grafana backend, including configuration for trace storage and utilizing TraceQL query patterns to analyze latency bottlenecks and identify root causes via span analysis.

Can I use OpenTelemetry metrics with exemplars for root cause identification?▼

Yes, you can use OpenTelemetry metrics with exemplars to link metric data points to specific traces, enabling deep performance analysis and faster root cause identification in distributed applications.

Why should I use OpenTelemetry instead of vendor-specific tracing agents?▼

Use OpenTelemetry to establish vendor-neutral observability across distributed applications, eliminating the complexity of manually piecing together disparate tracing, metrics, and logging solutions for different programming languages and frameworks.

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