How does it handle performance overhead from tracing?

The skill includes best practices for sampling strategies, such as probabilistic and adaptive sampling, to keep CPU impact below 1%.

Which programming languages are supported for instrumentation?

The skill provides specific implementation patterns and code snippets for Python (Flask), Node.js (Express), and Go.

Can this skill help with Kubernetes deployments?

Yes, it includes YAML configurations for the Jaeger Operator and Tempo deployments within Kubernetes environments.

Does it cover context propagation between services?

Absolutely. It provides logic for injecting and extracting traceparent headers to ensure trace continuity across service boundaries.

Does it support both Jaeger and Tempo?

Yes, it covers setup and configuration for both Jaeger (All-in-one and Production strategies) and Grafana Tempo.

Distributed Tracing & Observability

Name: Distributed Tracing & Observability
Author: lwhguge-dot

bylwhguge-dot

0•

Analytics & Monitoring

Implements end-to-end request tracking and latency analysis across microservices using OpenTelemetry, Jaeger, and Tempo.

This skill provides a comprehensive framework for implementing distributed tracing in complex microservice architectures. It guides users through the entire observability lifecycle: from deploying infrastructure like Jaeger and Tempo to instrumenting applications in Python, Node.js, and Go using OpenTelemetry. It ensures seamless context propagation across service boundaries, enabling developers to visualize request flows, identify performance bottlenecks, and trace error propagation across distributed systems with production-grade sampling strategies.

Key Features

01Configurable sampling strategies including probabilistic and rate-limiting

02Automated context propagation logic for HTTP and gRPC headers

03Infrastructure deployment guides for Jaeger and Tempo on Kubernetes/Docker

040 GitHub stars

05Multi-language instrumentation templates for Python, Node.js, and Go

06Trace analysis workflows for debugging latency and service dependencies

Use Cases

01Visualizing complex service dependencies and failure points during outages

02Debugging high latency and performance bottlenecks in microservice request chains

03Implementing standardized OpenTelemetry-based observability in distributed systems

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add lwhguge-dot/online-education-platform distributed-tracing

For use in Claude.ai and ChatGPT

Key Features

01Configurable sampling strategies including probabilistic and rate-limiting

02Automated context propagation logic for HTTP and gRPC headers

03Infrastructure deployment guides for Jaeger and Tempo on Kubernetes/Docker

040 GitHub stars

05Multi-language instrumentation templates for Python, Node.js, and Go

06Trace analysis workflows for debugging latency and service dependencies

Use Cases

01Visualizing complex service dependencies and failure points during outages

02Debugging high latency and performance bottlenecks in microservice request chains

03Implementing standardized OpenTelemetry-based observability in distributed systems

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add lwhguge-dot/online-education-platform distributed-tracing

For use in Claude.ai and ChatGPT