Can I use this for scheduling periodic tasks?

Absolutely. It includes comprehensive configurations for Celery Beat, covering interval-based tasks and complex crontab schedules for daily or weekly jobs.

How does it handle complex task dependencies?

The skill includes workflow patterns for Chains (sequential), Groups (parallel), and Chords (parallel with a callback) to manage sophisticated task dependencies.

Does this skill help with Celery retry strategies?

Yes, it provides implementation patterns for automatic retries, exponential backoff, and jitter to handle transient failures in distributed systems.

Is it compatible with FastAPI?

Yes, it features specific integration patterns for triggering Celery tasks from FastAPI endpoints and retrieving task status or results via polling.

Does it provide guidance on worker optimization?

Yes, it includes best practices for worker concurrency, prefetch multipliers, and avoiding anti-patterns like blocking code or passing large arguments through the broker.

Celery & Beat Task Development

Name: Celery & Beat Task Development
Author: FortiumPartners

byFortiumPartners

API Development

Streamlines the implementation of distributed task queues and periodic scheduling using Celery and Beat for Python applications.

About

This skill provides Claude with expert knowledge for architecting and implementing robust asynchronous task systems using Celery 5.3+. It offers standardized patterns for task definitions, complex workflow orchestration (including chains, groups, and chords), and seamless integration with FastAPI. Users can quickly configure Redis or RabbitMQ brokers, implement advanced retry logic with exponential backoff, and set up periodic jobs via the Beat scheduler. By following these production-grade implementation patterns, developers can avoid common anti-patterns like blocking workers or memory leaks while ensuring high-performance background processing.

Key Features

Distributed task queue configuration for Redis and RabbitMQ brokers.
0 GitHub stars
Resilient error handling with auto-retries, jittered backoff, and time limits.
Advanced workflow orchestration using Chains, Groups, and Chords.
Seamless FastAPI integration for triggering and monitoring background jobs.
Periodic task scheduling with Celery Beat and crontab patterns.

Use Cases

Building complex, multi-stage data pipelines that require parallel execution and result aggregation.
Offloading resource-intensive processes like PDF generation or image processing from the main API thread.
Automating recurring maintenance tasks such as database cleanups, daily reports, or data syncing.

About

Key Features

Distributed task queue configuration for Redis and RabbitMQ brokers.
0 GitHub stars
Resilient error handling with auto-retries, jittered backoff, and time limits.
Advanced workflow orchestration using Chains, Groups, and Chords.
Seamless FastAPI integration for triggering and monitoring background jobs.
Periodic task scheduling with Celery Beat and crontab patterns.

Use Cases

Building complex, multi-stage data pipelines that require parallel execution and result aggregation.
Offloading resource-intensive processes like PDF generation or image processing from the main API thread.
Automating recurring maintenance tasks such as database cleanups, daily reports, or data syncing.