# celery-expert

Guides background job design on Celery: retries, chains, groups, chords, Celery Beat schedules and Flower monitoring.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 454 installs · ★ 48
- Author: martinholovsky
- License: Unlicense · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/martinholovsky/claude-skills-generator/tree/HEAD/skills/celery-expert/SKILL.md
- Updated: 2025-12-06
- HTML: https://affpapa.org/en/nevibecode/skills/celery-expert

## What it does

This skill makes the agent act as a Celery engineer. It covers task definitions, Redis and RabbitMQ brokers, result backends, retries with exponential backoff, rate limiting and the Beat scheduler. Security gets its own section: signed tasks and serialization that avoids pickle.

The workflow is test-first. You write a failing pytest-celery test, get a minimal task passing, then refactor with time limits and observability. Ready-made patterns are included: chunking items into batches instead of firing thousands of tasks, tuning the prefetch multiplier for CPU-bound versus I/O-bound work, and skipping result storage on fire-and-forget tasks.

The skill rates itself medium risk and names the failure modes. Pickle serialization opens the door to code execution, and missing retries or timeouts let tasks pile up until workers stall. If your project already has its own Celery test conventions, expect to adapt the templates rather than drop them in.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add martinholovsky/claude-skills-generator --skill celery-expert -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/celery-expert/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/celery-expert/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add martinholovsky/claude-skills-generator --skill celery-expert -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/celery-expert/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add martinholovsky/claude-skills-generator --skill celery-expert -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/celery-expert/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install martinholovsky/claude-skills-generator celery-expert
```

2. **Manually: put the files in place**
- File: `.github/skills/celery-expert/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add martinholovsky/claude-skills-generator --skill celery-expert -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/martinholovsky/claude-skills-generator --path skills/celery-expert
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/celery-expert/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add martinholovsky/claude-skills-generator --skill celery-expert -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/martinholovsky/claude-skills-generator/tree/HEAD/skills/celery-expert/SKILL.md

## When to use it
- move heavy processing off the request path into background tasks
- wire several tasks into a chain or a group
- add retries with backoff without losing tasks when a worker dies
- pick between Redis and RabbitMQ for a given workload
- hook Flower and Prometheus metrics up to workers

## Example prompts
- Split order processing into Celery tasks with retries and idempotency
- Set up Celery Beat to recalculate reports nightly on a crontab
- Why are my tasks piling up in the queue? Check prefetch multiplier and acks late

## Source text · SKILL.md · Unlicense

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 99fbd99b3e40608e.

BEGIN THIRD-PARTY FILE 99fbd99b3e40608e
````text
---
name: celery-expert
description: "Expert Celery distributed task queue engineer specializing in async task processing, workflow orchestration, broker configuration (Redis/RabbitMQ), Celery Beat scheduling, and production monitoring. Deep expertise in task patterns (chains, groups, chords), retries, rate limiting, Flower monitoring, and security best practices. Use when designing distributed task systems, implementing background job processing, building workflow orchestration, or optimizing task queue performance."
model: sonnet
---

# 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

### Core Principles

1. **TDD First** - Write tests before implementation; verify task behavior with pytest-celery
2. **Performance Aware** - Optimize for throughput with chunking, pooling, and proper prefetch
3. **Reliability** - Task retries, acknowledgment strategies, no task loss
4. **Scalability** - Distributed workers, routing, autoscaling, queue prioritization
5. **Security** - Signed tasks, safe serialization, broker authentication
6. **Observable** - Comprehensive monitoring, metrics, tracing, alerting

**Risk Level**: MEDIUM
- Task processing failures can impact business operations
- Improper serialization (pickle) can lead to code execution vulnerabilities
- Missing retries/timeouts can cause task accumulation and system degradation
- Broker misconfigurations can lead to task loss or message exposure

---

## 2. Implementation Workflow (TDD)

### Step 1: Write Failing Test First

```python
# tests/test_tasks.py
import pytest
from celery.contrib.testing.tasks import ping
from celery.result import EagerResult

@pytest.fixture
def celery_config():
    return {
        'broker_url': 'memory://',
````
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