Does this skill help with context propagation between services?

Yes, one of its primary functions is configuring the necessary headers and context propagation logic to ensure trace continuity across microservices.

Does it handle high-traffic tracing costs?

The skill provides guidance and configuration for sampling strategies, allowing you to control the volume of traces collected to manage storage and performance overhead.

Which tracing backends are supported by this skill?

The skill supports popular tracing backends including Jaeger, Zipkin, Datadog, and any backend compatible with OpenTelemetry standards.

Is this specific to Python or FastAPI?

While it integrates well with the Nixtla plugin ecosystem, the core tracing logic and OpenTelemetry patterns it provides are applicable to most modern microservices architectures.

Can I use this with an existing production application?

Absolutely. The skill generates instrumentation code and configuration files that can be integrated into existing codebases to add observability without a full rewrite.

Distributed Tracing Setup

Name: Distributed Tracing Setup
Author: intent-solutions-io

byintent-solutions-io

分析と監視

Automates the implementation of end-to-end request visibility and observability across microservices architectures.

概要

This skill streamlines the complex process of configuring distributed tracing by guiding developers through backend selection, instrumentation strategy design, and automated code generation. It facilitates the integration of OpenTelemetry, Jaeger, and other tracing backends, ensuring seamless context propagation and span creation across multiple services. By automating the setup of observability infrastructure, this skill helps teams rapidly identify performance bottlenecks, troubleshoot latency issues, and maintain full visibility into request lifecycles in distributed environments.

主な機能

Customizable sampling strategies for volume and cost management
0 GitHub stars
Tailored instrumentation strategy design for microservices
Automated backend selection for Jaeger, Zipkin, or Datadog
Standardized context propagation across service boundaries
Generation of OpenTelemetry configuration and code snippets

ユースケース

Implementing distributed tracing for a new microservice stack
Debugging latency issues and bottlenecks in complex transaction flows
Adding OpenTelemetry observability to existing FastAPI or REST services

概要

主な機能

Customizable sampling strategies for volume and cost management
0 GitHub stars
Tailored instrumentation strategy design for microservices
Automated backend selection for Jaeger, Zipkin, or Datadog
Standardized context propagation across service boundaries
Generation of OpenTelemetry configuration and code snippets

ユースケース

Implementing distributed tracing for a new microservice stack
Debugging latency issues and bottlenecks in complex transaction flows
Adding OpenTelemetry observability to existing FastAPI or REST services