Which programming languages are supported for instrumentation?

This skill provides production-ready instrumentation patterns for Python (Flask), Node.js (Express), and Go using the OpenTelemetry SDK.

What backends are supported for trace storage?

The skill covers setup for Jaeger (using Elasticsearch for production) and Grafana Tempo (using S3-compatible storage).

Will distributed tracing slow down my application?

By implementing the sampling strategies included in this skill (such as 1% probabilistic sampling), you can maintain visibility while keeping CPU overhead below 1%.

How does this help identify performance issues?

It breaks down a single request into 'spans,' allowing you to visualize the duration of every operation across all services and database calls in a single timeline.

Can I use this with the Grafana stack?

Yes, the skill includes specific Kubernetes and configuration setup for Grafana Tempo, which integrates perfectly with Grafana dashboards.

Distributed Tracing Implementation

Name: Distributed Tracing Implementation
Author: HermeticOrmus

byHermeticOrmus

0•

Analytics & Monitoring

Implements end-to-end request tracking across microservices using Jaeger, Tempo, and OpenTelemetry to identify performance bottlenecks and resolve latency issues.

This skill provides a comprehensive framework for implementing distributed tracing in microservice architectures. It guides users through setting up observability backends like Jaeger and Grafana Tempo via Kubernetes or Docker Compose, while offering standardized instrumentation patterns for Python, Node.js, and Go using OpenTelemetry. By establishing trace context propagation and sampling strategies, it enables developers to visualize the entire request journey, pinpoint service dependencies, and debug complex failure points in distributed systems with precision.

Key Features

01Multi-language instrumentation for Python, Node.js, and Go using OpenTelemetry

02Integrated trace analysis tools for identifying slow requests and mapping service dependencies

030 GitHub stars

04Deployment configurations for Jaeger and Grafana Tempo via Kubernetes and Docker Compose

05Advanced sampling strategies including probabilistic, rate-limiting, and adaptive sampling

06Automated context propagation and HTTP header management for cross-service tracking

Use Cases

01Debugging high-latency requests in complex, multi-service production environments

02Identifying the root cause of error propagation across microservice boundaries

03Generating service dependency graphs to understand architecture bottlenecks

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add hermeticormus/floreserlife distributed-tracing

For use in Claude.ai and ChatGPT

Download Skill