golang-samber-ro

Compose asynchronous data stream pipelines with reactive operators in Go.

Updated Apr 20, 2026
One-click install
npx skills add https://github.com/matdev83/go-llm-interactive-proxy --skill golang-samber-ro-matdev83
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: golang-samber-ro
Source: https://github.com/matdev83/go-llm-interactive-proxy/tree/main/.agents/skills/golang-samber-ro
Command: npx skills add https://github.com/matdev83/go-llm-interactive-proxy --skill golang-samber-ro-matdev83

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill simplifies handling asynchronous data streams in Go by providing reactive pipeline capabilities.

Core Features & Use Cases

  • Asynchronous Data Processing: Build declarative pipelines for real-time or infinite streams, such as WebSocket feeds or event logs.
  • Reactive Operators: Use operators like Map, Filter, Merge, Zip, and Error handling to compose complex data workflows.
  • Use Case: Process stock prices in real-time, compute running averages, and handle connectivity errors seamlessly with retry logic.

Quick Start

Use the golang-samber-ro skill to create a pipeline that filters and transforms a stream of integers.

Frequently Asked Questions about golang-samber-ro

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I handle asynchronous data streams in Go for real-time event processing?

Handle asynchronous data streams in Go by building declarative reactive pipelines using operators like Map, Filter, and Merge to compose complex real-time workflows for event logs or WebSocket feeds.

What is the best way to manage error propagation in reactive Go pipelines?

Manage error propagation in reactive Go pipelines by utilizing built-in error handling operators and retry logic to seamlessly handle connectivity errors during stream processing.

Can I use reactive operators to compute running averages on infinite streams in Go?

Yes, you can use reactive operators to process infinite streams in Go, computing running averages on continuous data feeds such as real-time stock prices.

Does the samber/ro library require specific concurrency patterns for stream composition?

The samber/ro library leverages existing Go concurrency patterns to support execution control and stream composition without requiring specialized external concurrency frameworks.

How do I filter and transform a stream of integers using reactive pipelines in Go?

Filter and transform a stream of integers by creating a declarative pipeline that applies reactive operators to sequentially process and modify the incoming asynchronous data.