What technologies does this skill support?

It provides instrumentation patterns for OpenTelemetry in Python, Node.js, and Go, and supports backend storage/visualization via Jaeger and Grafana Tempo.

How does this help with performance tuning?

It allows you to visualize the entire request journey, showing exactly how much time each span (operation) takes, helping you pinpoint slow database queries or service calls.

What is the performance overhead of distributed tracing?

When configured with recommended sampling strategies (typically 1-10% in production) and batch span processing, the CPU impact is usually less than 1%.

Can I use this for production environments?

Yes, the skill includes production-grade configurations, such as the Jaeger Operator for Kubernetes and S3-backed storage for Tempo.

Does it support log correlation?

Yes, it includes best practices for injecting Trace IDs into your application logs, allowing you to jump from a specific log message to its corresponding trace.

Distributed Tracing Implementation

Name: Distributed Tracing Implementation
Author: amurata

byamurata

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分析と監視

Implements and configures distributed tracing using Jaeger and Tempo to visualize request flows and identify performance bottlenecks across microservices.

This skill empowers developers to gain deep observability into distributed systems by implementing end-to-end request tracing. It provides standardized patterns for instrumenting applications with OpenTelemetry across Python, Node.js, and Go, while offering deployment configurations for Jaeger and Grafana Tempo on both Kubernetes and Docker. By enabling precise tracking of latency, service dependencies, and error propagation, this skill is essential for debugging complex microservice architectures and optimizing system performance through data-driven analysis.

主な機能

01Deployment configurations for Jaeger and Grafana Tempo on Kubernetes and Docker Compose

02Advanced sampling strategies including probabilistic, rate-limiting, and adaptive sampling

033 GitHub stars

04Seamless context propagation techniques for tracking requests across service boundaries

05Integration patterns for correlating application logs with traces for faster troubleshooting

06Multi-language instrumentation patterns for Python, Node.js, and Go using OpenTelemetry

ユースケース

01Visualizing service dependencies and identifying hidden bottlenecks in distributed workflows

02Debugging high-latency requests in a complex microservices architecture

03Troubleshooting error propagation and identifying failure points across downstream services

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add amurata/cc-tools distributed-tracing

For use in Claude.ai and ChatGPT

主な機能

01Deployment configurations for Jaeger and Grafana Tempo on Kubernetes and Docker Compose

02Advanced sampling strategies including probabilistic, rate-limiting, and adaptive sampling

033 GitHub stars

04Seamless context propagation techniques for tracking requests across service boundaries

05Integration patterns for correlating application logs with traces for faster troubleshooting

06Multi-language instrumentation patterns for Python, Node.js, and Go using OpenTelemetry

ユースケース

01Visualizing service dependencies and identifying hidden bottlenecks in distributed workflows

02Debugging high-latency requests in a complex microservices architecture

03Troubleshooting error propagation and identifying failure points across downstream services