What is the main benefit of this skill for microservices?

It provides end-to-end visibility into request flows, allowing developers to pinpoint exactly which service is causing latency or errors in a complex distributed system.

Does this skill support multiple programming languages?

Yes, it includes specific instrumentation examples and best practices for Python (Flask), Node.js (Express), and Go using the OpenTelemetry SDK.

How does it handle large volumes of trace data?

The skill provides guidance on various sampling strategies, such as probabilistic and rate-limiting sampling, to help you balance observability needs with performance overhead.

Can I use this with existing Kubernetes clusters?

Absolutely. The skill includes deployment manifests for Jaeger Operators and Grafana Tempo configuration specifically designed for Kubernetes environments.

Distributed Tracing & Observability

Name: Distributed Tracing & Observability
Author: ngxtm

byngxtm

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分析与监控

Implements distributed tracing with Jaeger and Tempo to monitor microservices performance and visualize request flows.

This skill empowers developers to integrate comprehensive observability into distributed systems using industry-standard tools like Jaeger, Tempo, and OpenTelemetry. It provides standardized patterns for instrumenting Python, Node.js, and Go applications, setting up backend collectors via Kubernetes or Docker Compose, and managing context propagation across service boundaries. By identifying latency bottlenecks and tracing error propagation, this skill is essential for debugging complex microservice architectures and ensuring system reliability through deep visibility into every span and trace.

主要功能

01Ready-to-use infrastructure setup for Jaeger and Grafana Tempo via Kubernetes and Docker

02Multi-language instrumentation patterns for Python, Node.js, and Go using OpenTelemetry

03Advanced sampling strategy implementation including probabilistic and rate-limiting methods

04Trace analysis queries for identifying slow requests and visualizing service dependency graphs

05Automated context propagation techniques for maintaining trace integrity across HTTP boundaries

065 GitHub stars

使用场景

01Debugging high latency and performance bottlenecks in microservice architectures

02Visualizing complex request paths to pinpoint the root cause of cascading failures

03Correlating application logs with specific trace IDs for faster production troubleshooting

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add ngxtm/devkit distributed-tracing

For use in Claude.ai and ChatGPT