Does it support automatic instrumentation?

It provides patterns for both manual instrumentation for fine-grained control and auto-instrumentation for popular frameworks like Flask, Express, and HTTP clients.

Can I use this for production environments?

Yes, it includes production-grade configurations such as sampling strategies and persistent storage options (like Elasticsearch and S3) for tracing data.

Which tracing backends are supported by this skill?

The skill provides detailed implementation patterns for both Jaeger and Grafana Tempo, focusing on OpenTelemetry as the primary instrumentation standard.

How does it help with log correlation?

The skill includes best practices for injecting Trace IDs into application logs, allowing developers to jump directly from a log entry to the associated distributed trace.

What is the overhead of using this tracing skill?

By following the included sampling best practices, you can maintain deep visibility with minimal CPU and network overhead, typically keeping impact below 1%.

Distributed Tracing & Observability

Name: Distributed Tracing & Observability
Author: sickn33

bysickn33

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17,693

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Analytics & Monitoring

Implements end-to-end request tracking with Jaeger and Tempo to identify performance bottlenecks across distributed systems.

This skill empowers AI agents to architect and implement robust distributed tracing systems using industry-standard tools like Jaeger and Grafana Tempo. It provides comprehensive guidance for instrumenting microservices with OpenTelemetry across Python, Node.js, and Go, enabling deep visibility into request flows. By implementing context propagation and strategic sampling, developers can effectively debug latency issues, map service dependencies, and resolve complex error propagation in distributed architectures, ensuring high-performance and reliable production environments.

Key Features

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

02Advanced sampling strategies including probabilistic and rate-limiting configurations

03Trace analysis patterns for identifying slow operations and service bottlenecks

04Automated deployment patterns for Jaeger and Tempo via Kubernetes and Docker

0517,693 GitHub stars

06Cross-service context propagation for seamless end-to-end request tracking

Use Cases

01Debugging high latency and performance degradation in complex microservice meshes

02Root cause analysis for cascading failures and error propagation between services

03Visualizing service dependencies and request paths across distributed systems

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add sickn33/antigravity-awesome-skills distributed-tracing

For use in Claude.ai and ChatGPT

Download Skill