Can I use this for non-Kubernetes environments?

Yes, the skill includes configurations for both Kubernetes deployments and Docker Compose for local development and standalone server setups.

What tools does this skill support?

This skill primarily supports Jaeger and Grafana Tempo for storage and visualization, using OpenTelemetry for application instrumentation across various languages.

How do I find specific errors in my traces?

The skill includes specific Jaeger and Tempo query patterns to filter traces by service name, duration, error tags, and HTTP status codes.

Is this compatible with Python and Node.js?

Yes, it provides specific instrumentation patterns and production-ready code snippets for Python (Flask), Node.js (Express), and Go.

Does distributed tracing impact application performance?

While tracing adds some overhead, it is typically less than 1% CPU impact. This skill provides sampling strategies to minimize performance impact in production environments.

Distributed Tracing Implementation

Name: Distributed Tracing Implementation
Author: HermeticOrmus

byHermeticOrmus

Analytics & Monitoring

Implements end-to-end request tracking using Jaeger and Tempo to monitor microservices performance and reliability.

About

This skill provides comprehensive guidance for implementing distributed tracing across complex microservice architectures. By leveraging industry-standard tools like Jaeger, Grafana Tempo, and OpenTelemetry, it enables developers to visualize request flows, identify latency bottlenecks, and debug service dependencies in real-time. Whether you are working with Python, Node.js, or Go, this skill offers standardized patterns for instrumentation, context propagation, and sampling strategies to ensure deep observability without significant performance overhead.

Key Features

Log correlation techniques to link traces with application logs
0 GitHub stars
Advanced context propagation patterns for seamless request tracking
Multi-language instrumentation (Python, Node.js, Go) using OpenTelemetry
Configurable sampling strategies including Probabilistic and Rate Limiting
Standardized deployment configurations for Jaeger and Grafana Tempo

Use Cases

Debugging latency and performance bottlenecks in complex microservices
Visualizing request paths and service dependencies across distributed systems
Identifying and tracing error propagation to find root causes of failures

About

Key Features

Log correlation techniques to link traces with application logs
0 GitHub stars
Advanced context propagation patterns for seamless request tracking
Multi-language instrumentation (Python, Node.js, Go) using OpenTelemetry
Configurable sampling strategies including Probabilistic and Rate Limiting
Standardized deployment configurations for Jaeger and Grafana Tempo

Use Cases

Debugging latency and performance bottlenecks in complex microservices
Visualizing request paths and service dependencies across distributed systems
Identifying and tracing error propagation to find root causes of failures