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distributed-tracing

Implement distributed tracing for microservices with Jaeger and Tempo.

Updated Jan 13, 2026
Search Tags:#microservices#observability#opentelemetry#performance monitoring#distributed tracing#jaeger#tempo
One-click install
npx skills add https://github.com/ollieb89/vibe_coding --skill distributed-tracing-ollieb89
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: distributed-tracing
Source: https://github.com/ollieb89/vibe_coding/tree/main/.agent/observability-monitoring/skills/distributed-tracing
Command: npx skills add https://github.com/ollieb89/vibe_coding --skill distributed-tracing-ollieb89

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides deep visibility into how requests travel across multiple microservices, helping to pinpoint performance bottlenecks and errors in distributed systems.

Core Features & Use Cases

  • End-to-End Request Tracking: Follow a single request from its origin through all interacting services.
  • Performance Bottleneck Identification: Quickly identify which service or operation is causing latency.
  • Error Propagation Analysis: Trace the path of errors to understand their root cause.
  • Use Case: When a user reports slow performance on the e-commerce site, use this Skill to trace their specific request, identify the database query or API call that is taking too long, and then optimize that specific component.

Quick Start

Implement distributed tracing for your Python Flask application using OpenTelemetry and Jaeger.

Frequently Asked Questions about distributed-tracing

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

FAQPage Schema
How do I trace requests across microservices to find performance bottlenecks?▼

Distributed tracing visualizes request flows across microservices using Jaeger and Tempo, letting you follow a single request from origin through all interacting services to pinpoint slow operations and errors.

How do I instrument a Python Flask application with OpenTelemetry for distributed tracing?▼

You instrument a Python Flask application with OpenTelemetry by setting up trace generation and context propagation via HTTP headers, then configure Jaeger or Tempo to collect and visualize the resulting trace data.

Can I deploy Jaeger and Tempo for distributed tracing on both Kubernetes and Docker Compose?▼

Yes, this distributed tracing setup supports both Kubernetes and Docker Compose environments, allowing you to deploy Jaeger and Tempo to collect traces and visualize request flows across your microservices architecture.

Does distributed tracing with OpenTelemetry work for Node.js and Go microservices?▼

Yes, distributed tracing with OpenTelemetry supports application instrumentation for Python, Node.js, and Go, enabling you to track request flows and analyze performance bottlenecks across polyglot microservices architectures.

What is the best way to analyze error propagation in a microservices architecture?▼

The best way to analyze error propagation is using distributed tracing to trace the path of errors across services, helping you understand root causes by visualizing how failures travel through interacting microservices.

How does context propagation work in distributed tracing across HTTP services?▼

Context propagation in distributed tracing works by passing trace context via HTTP headers between services, allowing Jaeger and Tempo to stitch together individual spans into a complete end-to-end request flow visualization.

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