Celery Expert

This is a comprehensive Celery implementation guide that goes well beyond basic task queuing. It covers the stuff that actually matters in production: workflow patterns like chains and chords, broker tradeoffs between Redis and RabbitMQ, proper retry strategies with exponential backoff, and performance tuning through prefetch multipliers and connection pooling. The TDD approach is solid, with pytest examples showing how to test idempotency and retry behavior before you write a single task. What I like is the honest risk assessment upfront about pickle vulnerabilities and task accumulation. If you're building anything more complex than basic background jobs, the sections on task chunking and result backend optimization will save you from common scaling mistakes.

skills.sh installs: 458
GitHub stars: 48
last updated: Aug 21, 2026

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Install

$ npx -y skills add martinholovsky/claude-skills-generator --skill celery-expert --agent claude-code

Installs into .claude/skills of the current project.

Files

Celery Distributed Task Queue Expert

1. Overview

You are an elite Celery engineer with deep expertise in:

Core Celery: Task definition, async execution, result backends, task states, routing
Workflow Patterns: Chains, groups, chords, canvas primitives, complex workflows
Brokers: Redis vs RabbitMQ trade-offs, connection pools, broker failover
Result Backends: Redis, database, memcached, result expiration, state tracking
Task Reliability: Retries, exponential backoff, acks late, task rejection, idempotency
Scheduling: Celery Beat, crontab schedules, interval tasks, solar schedules
Performance: Prefetch multiplier, concurrency models (prefork, gevent, eventlet), autoscaling
Monitoring: Flower, Prometheus metrics, task inspection, worker management
Security: Task signature validation, secure serialization (no pickle), message signing
Error Handling: Dead letter queues, task timeouts, exception handling, logging