golang-samber-ro

Compose type-safe reactive pipelines in Go using samber/ro operators.

2.9k|191|Updated Mar 21, 2026
One-click install
npx skills add https://github.com/samber/cc-skills-golang --skill golang-samber-ro
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: golang-samber-ro
Source: https://github.com/samber/cc-skills-golang/tree/main/skills/golang-samber-ro
Command: npx skills add https://github.com/samber/cc-skills-golang --skill golang-samber-ro

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Reactive streams in Go can be hard to implement with manual goroutines, channels, and error handling. This Skill provides a cohesive, type-safe, declarative approach to building event-driven pipelines using samber/ro, reducing boilerplate and improving readability.

Core Features & Use Cases

  • Comprehensive operator set: 150+ operators, support for cold/hot observables, and 5 Subject types to model real-time data.
  • Plugin ecosystem and real-time pipelines: integrates with 40+ plugins for encoding, I/O, scheduling, observability, and more.
  • Use Case Examples: building real-time dashboards, event-driven services, and resilient data pipelines with backpressure and context support.

Quick Start

Create a simple ro pipeline that emits a few numbers with ro.Just, transforms them with ro.Map, and subscribes to print results.

Frequently Asked Questions about golang-samber-ro

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

FAQPage Schema
How do I build reactive streams in Go without managing goroutines and channels manually?▼

Reactive streams in Go can be built declaratively using a type-safe toolkit with 150+ operators, replacing manual goroutines and channels with cohesive pipelines for event-driven processing.

What are reactive observables and how do they handle backpressure in Go pipelines?▼

Reactive observables model real-time data streams in Go, handling backpressure, timeouts, and multi-source combination through context-aware execution to compose robust, resilient data pipelines.

Can I use reactive operators for real-time dashboards and event-driven services in Go?▼

Yes, reactive operators support real-time dashboards and event-driven services by providing 150+ transformations, five Subject types, and hot/cold observables to model live data efficiently.

What is the best way to combine multiple data streams with timeout support in Go?▼

The best way to combine multiple data streams with timeout support in Go is using a declarative reactive toolkit that offers multi-source combination operators and context-aware execution.

Does this Go reactive streams approach support observability and plugin integrations?▼

Yes, this Go reactive streams approach supports observability and plugin integrations, featuring a plugin ecosystem with 40+ integrations for encoding, I/O, scheduling, and monitoring pipelines.

When should I avoid declarative reactive pipelines in favor of manual channels in Go?▼

You should avoid declarative reactive pipelines in Go when your data flows are simple enough that manual channels and goroutines introduce less overhead than a 150-operator toolkit.