Can I use this for multiple programming languages?

Yes, it provides specific implementation patterns and code examples for Python (Flask), Node.js (Express), and Go.

What tools are supported for distributed tracing?

This skill supports Jaeger and Tempo for trace storage and visualization, using OpenTelemetry for standardized application instrumentation across services.

How does it handle production performance?

It implements best practices for probabilistic, rate-limiting, and adaptive sampling to ensure minimal CPU and network overhead in production environments.

Does this include deployment configurations?

Yes, the skill includes production-ready Kubernetes manifests (including Jaeger Operator) and Docker Compose configurations.

Distributed Tracing & Observability

Name: Distributed Tracing & Observability
Author: anton-abyzov

byanton-abyzov

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分析与监控

Implements comprehensive request tracking across microservices using Jaeger and Tempo to identify performance bottlenecks and service dependencies.

关于

This skill enables autonomous implementation of distributed tracing within microservices architectures using industry-standard tools like Jaeger, Tempo, and OpenTelemetry. It provides standardized patterns for instrumentation in Python, Node.js, and Go, allowing developers to visualize end-to-end request journeys, debug latency issues, and monitor service dependencies with precision. Whether you are troubleshooting error propagation or optimizing request paths, this skill provides the necessary configuration and code snippets to establish robust observability across your entire infrastructure.

主要功能

OpenTelemetry instrumentation for Python, Node.js, and Go
Advanced sampling strategies (probabilistic, rate-limiting, adaptive)
Trace-to-log correlation for enhanced troubleshooting
Jaeger and Tempo setup via Docker Compose and Kubernetes
Standardized context propagation across HTTP boundaries
13 GitHub stars

使用场景

Mapping service dependencies and identifying failure points
Implementing performance monitoring and SLO tracking for distributed systems
Debugging high latency in complex microservices architectures

关于

主要功能

OpenTelemetry instrumentation for Python, Node.js, and Go
Advanced sampling strategies (probabilistic, rate-limiting, adaptive)
Trace-to-log correlation for enhanced troubleshooting
Jaeger and Tempo setup via Docker Compose and Kubernetes
Standardized context propagation across HTTP boundaries
13 GitHub stars

使用场景

Mapping service dependencies and identifying failure points
Implementing performance monitoring and SLO tracking for distributed systems
Debugging high latency in complex microservices architectures