Is Context propagation covered?

Yes, the skill provides specific instructions on propagating Context through the call stack to manage timeouts and cancellations across multiple goroutines.

How do I prevent goroutine leaks with this skill?

The skill emphasizes best practices like using Context for cancellation and ensuring channels are closed from the sender side so all goroutines can exit properly.

Can this skill help with race conditions?

Yes, it provides patterns for using Mutexes for shared state and synchronization primitives, while recommending 'go run -race' for detection.

Does it support worker pool patterns?

Absolutely, it includes standardized implementations for creating worker pools to handle job processing with bounded concurrency.

Go Goroutine Patterns

Name: Go Goroutine Patterns
Author: armanzeroeight

byarmanzeroeight

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Desarrollo de API

Implements robust Go concurrency patterns using goroutines, channels, and synchronization primitives for high-performance applications.

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The goroutine-patterns skill equips developers with standardized implementations of Go’s powerful concurrency model. It provides ready-to-use blueprints for worker pools, pipelines, fan-out/fan-in architectures, and complex synchronization logic using Mutexes and WaitGroups. Whether you are building scalable backend services, implementing rate limiting, or managing goroutine lifecycles with Context, this skill ensures best practices are followed to prevent common issues like deadlocks, race conditions, and goroutine leaks in concurrent systems.

Características Principales

Standard concurrency patterns including Worker Pools and Pipelines
Context-aware lifecycle management and graceful cancellation
Advanced synchronization using Mutex, RWMutex, and WaitGroups
Implementation of Fan-out/Fan-in and Rate Limiting patterns
Concurrent error handling using the Error Group pattern
19 GitHub stars

Casos de Uso

Managing asynchronous task execution and resource-bounded worker pools
Building high-throughput API backends and microservices
Implementing parallel data processing pipelines for large datasets

Acerca de

Características Principales

Standard concurrency patterns including Worker Pools and Pipelines
Context-aware lifecycle management and graceful cancellation
Advanced synchronization using Mutex, RWMutex, and WaitGroups
Implementation of Fan-out/Fan-in and Rate Limiting patterns
Concurrent error handling using the Error Group pattern
19 GitHub stars

Casos de Uso

Managing asynchronous task execution and resource-bounded worker pools
Building high-throughput API backends and microservices
Implementing parallel data processing pipelines for large datasets