What tracing backends does this skill support?

The skill provides configuration and implementation patterns specifically for Jaeger and Grafana Tempo, though it utilizes OpenTelemetry standards which are compatible with most modern backends.

Does it support context propagation?

Yes, it covers standard W3C Trace Context and HTTP header injection/extraction to ensure trace IDs are preserved across different services.

How does this skill handle tracing overhead?

It provides guidance on sampling strategies, such as probabilistic and adaptive sampling, to ensure observability data is collected with less than 1% impact on system performance.

Can I deploy these observability tools on Kubernetes?

Yes, the skill includes Kubernetes manifests and operator configurations for deploying Jaeger and Tempo instances within a cluster.

Which programming languages are covered for instrumentation?

It includes production-ready code examples for Python (Flask), Node.js (Express), and Go, covering both manual and automatic instrumentation.

Distributed Tracing & Observability

Name: Distributed Tracing & Observability
Author: HermeticOrmus

byHermeticOrmus

0•

Analíticas y Monitorización

Implements end-to-end request tracking across microservices using Jaeger and Tempo to identify performance bottlenecks and service failures.

This skill provides a comprehensive framework for implementing distributed tracing in microservice architectures, allowing developers to track requests as they move across service boundaries. It offers implementation patterns for OpenTelemetry across Python, Node.js, and Go, alongside infrastructure-as-code configurations for Jaeger and Grafana Tempo. By visualizing system dependencies and request spans, it enables teams to pinpoint the exact source of latency or errors in complex, distributed environments while ensuring minimal performance overhead through optimized sampling strategies.

Características Principales

01Customizable sampling strategies including probabilistic and rate-limiting

02Trace-to-log correlation for simplified root-cause analysis

03Multi-language instrumentation patterns for Python, Node.js, and Go

04Infrastructure setups for Jaeger and Grafana Tempo using K8s and Docker

05Automated context propagation across HTTP service boundaries

060 GitHub stars

Casos de Uso

01Monitoring and optimizing Service Level Objectives (SLOs) for distributed systems

02Analyzing service dependency graphs and failure propagation points

03Debugging high-latency requests in complex microservice meshes

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add hermeticormus/after-the-third-cup distributed-tracing

For use in Claude.ai and ChatGPT

Características Principales

01Customizable sampling strategies including probabilistic and rate-limiting

02Trace-to-log correlation for simplified root-cause analysis

03Multi-language instrumentation patterns for Python, Node.js, and Go

04Infrastructure setups for Jaeger and Grafana Tempo using K8s and Docker

05Automated context propagation across HTTP service boundaries

060 GitHub stars

Casos de Uso

01Monitoring and optimizing Service Level Objectives (SLOs) for distributed systems

02Analyzing service dependency graphs and failure propagation points

03Debugging high-latency requests in complex microservice meshes

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add hermeticormus/after-the-third-cup distributed-tracing

For use in Claude.ai and ChatGPT