How does Claude verify the accuracy of the Effect code it generates?

The skill includes a workflow that directs Claude to search local Effect source files (like Effect.ts and Fiber.ts) to check actual type definitions and implementation details before writing code.

Does this skill help with background task management?

Yes, it provides specific patterns for forking tasks into the background using Effect.fork and ensures they are properly joined or interrupted to prevent memory leaks and orphaned processes.

How do I prevent resource exhaustion with this skill?

The skill emphasizes using concurrency limits within functions like Effect.all and Effect.forEach, ensuring that parallel tasks do not overwhelm CPU, IO, or external API rate limits.

What is a Fiber in the context of Effect.ts?

A Fiber is a lightweight, low-overhead 'green thread' managed by the Effect runtime, allowing developers to execute tasks concurrently with built-in support for interruption and coordination.

Effect Concurrency & Fibers

Name: Effect Concurrency & Fibers
Author: mepuka

bymepuka

API Development

Manages complex asynchronous operations and parallel task execution using Effect.ts concurrency patterns and Fiber lifecycles.

About

This skill empowers Claude to implement robust, resilient concurrency in TypeScript projects using the Effect library. It provides patterns for parallelizing independent tasks with Effect.all and Effect.forEach, managing the lifecycle of background fibers with fork, join, and interrupt, and handling race conditions or timeouts for latency control. By prioritizing local source code inspection over assumptions, this skill ensures that implementations are idiomatic, type-safe, and follow best practices for resource management, such as avoiding unbounded concurrency and ensuring clean fiber shutdowns.

Key Features

Safe background worker coordination and lifecycle management
0 GitHub stars
Local source code verification for idiomatic Effect.ts implementation
Parallel task execution with configurable concurrency limits
Resilient racing and timeout patterns for latency control
Granular Fiber management including fork, join, and interruption

Use Cases

Implementing long-running background polling loops with safe interruption logic
Parallelizing multiple API requests while strictly respecting external rate limits
Managing complex resource-intensive tasks using concurrency pools to prevent CPU exhaustion

About

Key Features

Safe background worker coordination and lifecycle management
0 GitHub stars
Local source code verification for idiomatic Effect.ts implementation
Parallel task execution with configurable concurrency limits
Resilient racing and timeout patterns for latency control
Granular Fiber management including fork, join, and interruption

Use Cases

Implementing long-running background polling loops with safe interruption logic
Parallelizing multiple API requests while strictly respecting external rate limits
Managing complex resource-intensive tasks using concurrency pools to prevent CPU exhaustion