How does distributed tracing help with debugging?

It assigns a unique ID to every request, allowing you to track that request as it passes through multiple services, pinpointing exactly which service or database call is causing a delay or error.

Can I use this with existing Kubernetes clusters?

Absolutely. The skill includes Kubernetes manifests for the Jaeger Operator and Tempo deployments specifically designed for cloud-native environments.

What is the performance impact of adding tracing?

When using the recommended sampling strategies (like 1% production sampling) and batch span processors included in this skill, the CPU overhead is typically less than 1%.

Does this skill support OpenTelemetry standards?

Yes, it focuses on OpenTelemetry (OTel) as the primary instrumentation standard, ensuring compatibility with most modern observability backends.

Distributed Tracing Implementation

Name: Distributed Tracing Implementation
Author: nguyendinhquocx

bynguyendinhquocx

分析と監視

Implements end-to-end request tracking across microservices using Jaeger and Tempo to identify and resolve performance bottlenecks.

概要

This skill provides a comprehensive framework for implementing distributed tracing within complex microservices architectures. It offers production-ready deployment configurations for Jaeger and Grafana Tempo using Kubernetes and Docker Compose, alongside detailed instrumentation patterns for Python, Node.js, and Go via OpenTelemetry. By leveraging this skill, developers can gain deep visibility into request flows, automate context propagation, and utilize advanced sampling strategies to monitor distributed systems with minimal performance overhead.

主な機能

Multi-language OpenTelemetry instrumentation patterns for Python, Node.js, and Go
Advanced sampling strategies including Probabilistic, Rate Limiting, and Adaptive sampling
Production-grade Kubernetes manifests and Docker Compose setup for Jaeger and Tempo
Integration patterns for correlated logging and service dependency mapping
Automated context propagation for HTTP headers and service boundaries
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ユースケース

Root cause analysis for error propagation in distributed architectures
Debugging high-latency requests across complex microservices environments
Visualizing request paths to identify hidden service dependencies and bottlenecks

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add nguyendinhquocx/code-ai distributed-tracing

For use in Claude.ai and ChatGPT

Download Skill

GitHub

概要