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Batch Processing Jobs

  • 428 installs
  • 305 repo stars
  • Updated March 4, 2026
  • aj-geddes/useful-ai-prompts

batch-processing-jobs is an aj-geddes/useful-ai-prompts agent skill that designs queue-driven batch workers and schedulers for developers processing ETL, reports, and bulk transforms off the request path.

About

batch-processing-jobs is an aj-geddes/useful-ai-prompts Claude Code skill for scalable background processing from a library of 260+ skills. It architects job queues, cron schedulers, distributed workers, and retry policies for ETL pipelines, scheduled report generation, email campaigns, data imports, image processing, and bulk database updates. The quick-start TypeScript example scaffolds a Bull Queue BatchProcessor with typed JobData, result queues, Redis backing, idempotency, exponential backoff, dead letter queues, and graceful shutdown. Three reference guides document Bull Queue for Node.js, Celery-style Python workers, and cron job schedulers. Best practices warn against synchronous in-request processing, unlimited retries, and large payloads stored directly in queues. Developers reach for batch-processing-jobs when APIs need async offload patterns instead of blocking primary databases during heavy transforms.

  • Idempotent job design
  • Queue and cron triggers
  • Chunked processing
  • Failure retries and DLQs
  • Observability for long runs

Batch Processing Jobs by the numbers

  • 428 all-time installs (skills.sh)
  • Ranked #1,028 of 4,347 Backend & APIs skills by installs in the Skillselion catalog
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/aj-geddes/useful-ai-prompts --skill batch-processing-jobs

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Listed on Skillselion
Installs428
repo stars305
Last updatedMarch 4, 2026
Repositoryaj-geddes/useful-ai-prompts

How do you design queue-driven batch jobs for ETL?

Architect scheduled or queue-driven batch jobs for ETL, report generation, and bulk transforms without blocking online request paths or overloading primary databases.

Who is it for?

Backend developers adding Redis-backed job queues and scheduled workers for reports, imports, or bulk database updates.

Skip if: Real-time request handlers that should stay synchronous or teams already standardized on a managed queue without custom worker scaffolding.

When should I use this skill?

Scheduled reports, ETL pipelines, or bulk transforms must run off the online request path without overloading primary databases.

What you get

BatchProcessor class, Bull or Celery worker configs, cron schedules, and dead-letter queue patterns

  • Queue worker scaffolding
  • Cron scheduler config
  • Retry and dead-letter patterns

By the numbers

  • Includes 3 reference guides for Bull Queue, Celery-style Python, and cron schedulers
  • From useful-ai-prompts library with 260+ Claude Code skills

Files

SKILL.mdMarkdownGitHub ↗

Batch Processing Jobs

Table of Contents

Overview

Implement scalable batch processing systems for handling large-scale data processing, scheduled tasks, and async operations efficiently.

When to Use

  • Processing large datasets
  • Scheduled report generation
  • Email/notification campaigns
  • Data imports and exports
  • Image/video processing
  • ETL pipelines
  • Cleanup and maintenance tasks
  • Long-running computations
  • Bulk data updates

Quick Start

Minimal working example:

import Queue from "bull";
import { v4 as uuidv4 } from "uuid";

interface JobData {
  id: string;
  type: string;
  payload: any;
  userId?: string;
  metadata?: Record<string, any>;
}

interface JobResult {
  success: boolean;
  data?: any;
  error?: string;
  processedAt: number;
  duration: number;
}

class BatchProcessor {
  private queue: Queue.Queue<JobData>;
  private resultQueue: Queue.Queue<JobResult>;

  constructor(redisUrl: string) {
    // Main processing queue
// ... (see reference guides for full implementation)

Reference Guides

Detailed implementations in the references/ directory:

GuideContents
Bull Queue (Node.js)Bull Queue (Node.js)
Celery-Style Worker (Python)Celery-Style Worker (Python)
Cron Job SchedulerCron Job Scheduler

Best Practices

✅ DO

  • Implement idempotency for all jobs
  • Use job queues for distributed processing
  • Monitor job success/failure rates
  • Implement retry logic with exponential backoff
  • Set appropriate timeouts
  • Log job execution details
  • Use dead letter queues for failed jobs
  • Implement job priority levels
  • Batch similar operations together
  • Use connection pooling
  • Implement graceful shutdown
  • Monitor queue depth and processing time

❌ DON'T

  • Process jobs synchronously in request handlers
  • Ignore failed jobs
  • Set unlimited retries
  • Skip monitoring and alerting
  • Process jobs without timeouts
  • Store large payloads in queue
  • Forget to clean up completed jobs

Related skills

How it compares

Pick batch-processing-jobs when you need queue architecture patterns for Redis-backed workers rather than single-server cron scripts alone.

FAQ

Which queue libraries does batch-processing-jobs cover?

batch-processing-jobs documents Bull Queue for Node.js, Celery-style Python workers, and cron job schedulers across three reference guides with retry and dead-letter patterns.

What workloads suit batch-processing-jobs?

batch-processing-jobs targets ETL pipelines, scheduled report generation, email campaigns, data imports, image processing, cleanup tasks, and bulk database updates off the request path.

What batch job anti-patterns does the skill warn against?

batch-processing-jobs warns against synchronous in-request processing, unlimited retries, missing timeouts, storing large payloads in queues, and skipping dead letter queues for failed jobs.

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