How does the skill handle task execution history?

It includes specialized database models that automatically log the status, start time, end time, duration, and any error messages for every task execution, providing full observability.

Can this skill work without Celery?

Yes, the architecture includes an 'inline' execution mode that allows tasks to run directly within the FastAPI application process for simpler deployments that don't require distributed workers.

Does it support automatic retries for failed tasks?

Yes, it implements a production-grade retry pattern using exponential backoff and jitter, ensuring that transient failures are handled gracefully without manual intervention.

Is the scheduler instance-aware in distributed environments?

The architecture includes instance management and distributed locking patterns to ensure that scheduled jobs are coordinated correctly even when running multiple API instances.

What scheduling formats are supported?

The skill supports both standard Cron expressions for complex calendar-based timing and Interval scheduling for simple periodic runs (every N seconds, minutes, hours, or days).

Task Management & Scheduling

Name: Task Management & Scheduling
Author: adelabdelgawad

byadelabdelgawad

Productivity & Workflow

Implements a robust background task management system using APScheduler for complex scheduling and Celery for distributed execution.

About

This skill provides a comprehensive architectural blueprint for building scalable background job systems within FastAPI applications. It bridges APScheduler for sophisticated time-based scheduling (supporting both Cron and Interval patterns) with Celery for reliable, distributed task execution. By offering a standardized implementation that includes job persistence, execution tracking, and automatic retries with exponential backoff, it ensures that long-running or periodic processes are handled efficiently and resiliently across distributed environments.

Key Features

Pre-configured FastAPI service and repository layers for job CRUD operations
0 GitHub stars
Dual-layer architecture combining APScheduler's timing logic with Celery's distributed execution
Support for both Cron-based and Interval-based job scheduling patterns
Comprehensive database models for persistent job storage and execution history tracking
Advanced retry strategies featuring exponential backoff, jitter, and task-level time limits

Use Cases

Automating periodic database maintenance, history cleanup, and report generation
Managing long-running asynchronous processes like bulk email dispatch or third-party API synchronization
Scaling backend processing power by offloading heavy computations to distributed Celery workers

About

Key Features

Pre-configured FastAPI service and repository layers for job CRUD operations
0 GitHub stars
Dual-layer architecture combining APScheduler's timing logic with Celery's distributed execution
Support for both Cron-based and Interval-based job scheduling patterns
Comprehensive database models for persistent job storage and execution history tracking
Advanced retry strategies featuring exponential backoff, jitter, and task-level time limits

Use Cases

Automating periodic database maintenance, history cleanup, and report generation
Managing long-running asynchronous processes like bulk email dispatch or third-party API synchronization
Scaling backend processing power by offloading heavy computations to distributed Celery workers