Which programming languages does this skill support?

This skill provides specific instrumentation examples and patterns for Python (Flask), Node.js (Express), and Go using OpenTelemetry.

What is distributed tracing?

Distributed tracing is a method used to profile and monitor applications, especially those built using a microservices architecture, by tracking the path of a request as it travels through various services.

Can I deploy the tracing infrastructure on Kubernetes?

Yes, the skill includes detailed deployment configurations for both Kubernetes (via the Jaeger Operator) and Docker Compose for local development.

How does this help with performance tuning?

It provides specific Jaeger queries and analysis patterns to find slow requests, identify service bottlenecks, and visualize request duration across the entire stack.

Does it support Grafana integration?

Yes, the skill includes setup instructions for Grafana Tempo, allowing you to integrate traces directly with your existing Grafana dashboards and logs.

Distributed Tracing & Observability

Name: Distributed Tracing & Observability
Author: HermeticOrmus

byHermeticOrmus

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Analytics & Monitoring

Implements end-to-end request tracking using Jaeger and Tempo to monitor microservice performance and resolve complex system bottlenecks.

This skill provides a comprehensive framework for implementing distributed tracing in microservice architectures using OpenTelemetry, Jaeger, and Grafana Tempo. It enables developers to visualize the complete journey of a request across multiple services, identifying high-latency spans and debugging complex error propagation with precision. By providing ready-to-use instrumentation patterns for Python, Node.js, and Go, alongside robust deployment configurations for Kubernetes and Docker, it ensures that deep observability is integrated into distributed systems and game backends. This is particularly useful for teams needing to understand service dependencies and optimize request paths in high-scale environments.

Key Features

01Distributed context propagation and HTTP header management

02Advanced sampling strategies including probabilistic and rate-limiting

03OpenTelemetry instrumentation for Python, Node.js, and Go

044 GitHub stars

05Production-ready Jaeger and Grafana Tempo deployment configurations

06Trace analysis patterns for latency detection and error correlation

Use Cases

01Debugging latency and performance bottlenecks in microservice architectures

02Visualizing complex service dependencies and request flow paths

03Tracing error propagation and failure points across distributed system boundaries

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add hermeticormus/claude-code-game-development distributed-tracing

For use in Claude.ai and ChatGPT

Key Features

01Distributed context propagation and HTTP header management

02Advanced sampling strategies including probabilistic and rate-limiting

03OpenTelemetry instrumentation for Python, Node.js, and Go

044 GitHub stars

05Production-ready Jaeger and Grafana Tempo deployment configurations

06Trace analysis patterns for latency detection and error correlation

Use Cases

01Debugging latency and performance bottlenecks in microservice architectures

02Visualizing complex service dependencies and request flow paths

03Tracing error propagation and failure points across distributed system boundaries