distributed-tracing

Implement distributed tracing in microservices with Jaeger and OpenTelemetry.

7|1|Updated Jan 14, 2026

Search Tags:#microservices #observability #latency #Jaeger #OpenTelemetry #distributed tracing

Ready to run

npx skills add https://github.com/eyadsibai/ltk --skill distributed-tracing-eyadsibai

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: distributed-tracing
Source: https://github.com/eyadsibai/ltk/tree/main/plugins/ltk-devops/skills/distributed-tracing
Command: npx skills add https://github.com/eyadsibai/ltk --skill distributed-tracing-eyadsibai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps developers understand and debug the flow of requests across multiple microservices, identify latency bottlenecks, and improve overall system observability.

Core Features & Use Cases

Implement Distributed Tracing: Set up tracing with Jaeger and OpenTelemetry.
Debug Microservices: Analyze request paths, identify slow services, and pinpoint errors.
Use Case: When a user reports slow performance on the e-commerce site, you can use this Skill to trace their request from the frontend through the API gateway, order service, and payment service to find where the delay is occurring.

Quick Start

Use the distributed tracing skill to set up OpenTelemetry with Jaeger for a Python Flask application.

Frequently Asked Questions about distributed-tracing

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

FAQPage Schema

How do I debug microservice latency bottlenecks with distributed tracing?▼

Distributed tracing helps debug microservice latency by visualizing request flows across services to pinpoint slow components. It traces requests from the frontend through gateways and downstream services to identify exactly where delays occur.

How do I set up OpenTelemetry and Jaeger for a Python Flask application?▼

You can set up OpenTelemetry and Jaeger for Python Flask by configuring trace providers, span processors, and context propagation. This implementation captures and exports trace data to visualize microservice request paths.

Does this distributed tracing implementation work with Node.js environments?▼

Yes, this distributed tracing implementation supports both Python and Node.js environments. You can configure trace providers and context propagation across both stacks to achieve comprehensive microservice observability.

What is the best way to visualize microservice request flows for observability?▼

Visualizing microservice request flows is best achieved by implementing distributed tracing with Jaeger and OpenTelemetry. This approach maps end-to-end request paths, allowing you to analyze interactions and pinpoint errors across services.

Why do I need to configure context propagation for microservices tracing?▼

Context propagation is required for microservices tracing because it passes trace context across service boundaries. Without proper span processors and context propagation, distributed traces break and cannot visualize a continuous end-to-end request flow.