celery-worker

Name: celery-worker
Availability: InStock
Author: AdelAbdElGawad

Configure Celery workers with late acknowledgments, time limits, and queue routing.

Updated Jan 10, 2026

Search Tags:#python #background tasks #distributed systems #celery #task queues #worker configuration

Ready to run

npx skills add https://github.com/adelabdelgawad/claude-fullstack-skills --skill celery-worker

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: celery-worker
Source: https://github.com/adelabdelgawad/claude-fullstack-skills/tree/main/celery-worker
Command: npx skills add https://github.com/adelabdelgawad/claude-fullstack-skills --skill celery-worker

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a production-ready setup for Celery workers, ensuring reliable background task processing, efficient queue management, and robust error handling for distributed systems.

Core Features & Use Cases

Reliable Task Execution: Ensures tasks are processed even if workers crash, using task_acks_late and reject_on_worker_lost.
Performance Optimization: Configures worker concurrency, prefetching, and time limits to prevent hung tasks.
Queue Management: Enables routing tasks to specific queues for better workload separation and prioritization.
Use Case: When building a web application that needs to send emails, process uploaded files, or perform long-running computations, this Skill helps configure the background job system to handle these tasks efficiently and reliably.

Quick Start

Configure your Celery application by importing the provided celery_app.py and defining your tasks in the tasks/ directory.

Frequently Asked Questions about celery-worker

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

FAQPage Schema

How do I configure Celery workers for reliable background task processing in production?▼

Celery worker configuration for production uses late acknowledgments and reject-on-worker-lost settings to ensure background tasks are reprocessed if workers crash, preventing task loss in distributed Python applications.

Why do my Celery tasks get stuck or hang during long-running computations?▼

Celery workers can hang when tasks exceed time limits without proper configuration. Setting worker prefetch limits, concurrency parameters, and hard time limits prevents hung tasks and ensures reliable asynchronous execution.

What's the best way to route Celery tasks to specific queues for workload separation?▼

Queue routing in Celery directs tasks to specific queues for workload separation and prioritization. You define task routes and manage queues so that emails, file processing, and long computations run on dedicated workers.

When do I need task_acks_late and reject_on_worker_lost in my Celery setup?▼

Task_acks_late and reject_on_worker_lost are needed when workers may crash during processing. Late acknowledgment delays task completion signals until after execution, while reject-on-worker-lost requeues tasks if a worker disappears unexpectedly.

Can I use this Celery worker configuration for distributed systems handling emails and file uploads?▼

Yes, this Celery worker setup targets distributed Python applications that need reliable asynchronous execution for sending emails, processing uploaded files, or running long computations with robust error handling and queue management.