golang-samber-ro

Implement reactive streams and event-driven pipelines in Go with samber/ro.

1|Updated May 27, 2026
One-click install
npx skills add https://github.com/dmwin72015/netdisk --skill golang-samber-ro-dmwin72015
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: golang-samber-ro
Source: https://github.com/dmwin72015/netdisk/tree/main/.agents/skills/golang-samber-ro
Command: npx skills add https://github.com/dmwin72015/netdisk --skill golang-samber-ro-dmwin72015

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you implement reactive, event-driven processing in Go without manually wiring complex goroutine and channel lifecycles for asynchronous or unbounded data flows.

Core Features & Use Cases

  • Type-safe ReactiveX-style streams: Build declarative Observable pipelines with typed operators and Pipe composition for asynchronous event processing.
  • Subjects for event multicasting and state: Use Publish/Behavior/Replay/Async/Unicast subjects to model event buses, shared streams, and “latest value” state distribution.
  • Operational safety for real systems: Support backpressure, error propagation through the pipeline, retry/fallback patterns, and context-aware cancellation to avoid leaks.
  • Plugin ecosystem for production scenarios: Extend with HTTP, cron scheduling, fsnotify file watching, JSON/logging integrations, and OS signal handling for operational workflows.

Quick Start

Ask for a samber/ro pipeline that uses a typed Pipe chain with proper error handling, context cancellation, and a bounded shutdown strategy, then implement it directly in your Go service.

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 type-safe reactive pipelines in Go for event-driven streams?

Reactive pipelines in Go can be built using declarative Observable chains with typed operators and Pipe composition, enabling asynchronous event processing without manually wiring complex goroutine and channel lifecycles for unbounded data flows.

How do I handle backpressure and context-aware cancellation in Go observable streams?

Backpressure and context-aware cancellation in observable streams are handled natively to prevent resource leaks, ensuring correct error propagation through the pipeline and safe shutdown during real-time data processing.

What are the best ways to implement WebSocket feeds and file watchers in Go without managing raw channels?

WebSocket feeds and file watchers are implemented as long-lived observables using reactive streams, leveraging declarative operators, plugin ecosystems for fsnotify, and event multicasting subjects to distribute shared streams.

Can I use Publish and Behavior subjects in Go for multi-consumer event flows and state distribution?

Yes, Publish, Behavior, Replay, Async, and Unicast subjects model event buses and shared streams in Go, providing hot observable behavior and latest value state distribution for multi-consumer event flows.

Does reactive programming in Go support retry patterns and operational safety for production scenarios?

Reactive programming in Go supports operational safety for production scenarios through built-in retry and fallback patterns, context-aware cancellation, and plugin extensions for HTTP, cron scheduling, JSON, logging, and OS signal handling.

When do I need reactive streams instead of standard Go channels for asynchronous data processing?

Reactive streams are needed for infinite or long-lived observables requiring declarative operator composition, backpressure management, and event multicasting, whereas standard Go channels require manual lifecycle wiring for complex asynchronous data flows.