django-celery

A guide and set of patterns for running background and scheduled work in Django with Celery, a task queue for processing jobs outside web requests.

In plain words
What is it for?
Use it when adding background jobs, periodic tasks, email or PDF processing, API calls, failure handling, or Celery task tests.
Why use it?
It helps move slow work out of the request cycle and provides patterns for retries, scheduling, monitoring, workflows, and tests.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/junmystery/agent-guidance-python/django-celery
Any agent
npx skills add JunMystery/Agent-Guidance-Python --skill django-celery
Clone the repo
git clone --depth 1 https://github.com/JunMystery/Agent-Guidance-Python

Made for: Claude Code, Codex.

Per session 49 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,096 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00049 $0.03096
Opus 5 $0.00024 $0.01548
Sonnet 5 $0.00010 $0.00619
Haiku 4.5 $0.00005 $0.00310

Measured 2d ago against content hash 070faa076bfb, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

django-celery scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/django-celery/SKILL.md · 458 lines

How it starts

The opening of the file, as written. The whole thing — 458 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Django + Celery Async Task Patterns

Production-grade patterns for background task processing in Django using Celery with Redis or RabbitMQ.

When to Activate

  • Adding background jobs or async processing to a Django app
  • Implementing periodic/scheduled tasks
  • Offloading slow operations (email, PDF generation, API calls) from request cycle
  • Setting up Celery Beat for cron-like scheduling
  • Debugging task failures, retries, or queue backlogs
  • Writing tests for Celery tasks

Project Setup

Installation

pip install 'celery[redis]' django-celery-results django-celery-beat

celery.py — App Entrypoint

# config/celery.py
import os
from celery import Celery

os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'config.settings.development')

app = Celery('myproject')
app.config_from_object('django.conf:settings', namespace='CELERY')
app.autodiscover_tasks()  # Discovers tasks.py in each INSTALLED_APP

@app.task(bind=True, ignore_result=True)
def debug_task(self):
    print(f'Request: {self.request!r}')
# config/__init__.py
from .celery import app as celery_app

__all__ = ('celery_app',)

Django Settings

# config/settings/base.py

# Broker (Redis recommended for production)
CELERY_BROKER_URL = env('CELERY_BROKER_URL', default='redis://localhost:6379/0')
CELERY_RESULT_BACKEND = env('CELERY_RESULT_BACKEND', default='django-db')

# Serialization
CELERY_ACCEPT_CONTENT = ['json']
CELERY_TASK_SERIALIZER = 'json'
CELERY_RESULT_SERIALIZER = 'json'

# Task behavior
CELERY_TASK_TRACK_STARTED = True
CELERY_TASK_TIME_LIMIT = 30 * 60        # Hard limit: 30 min
CELERY_TASK_SOFT_TIME_LIMIT = 25 * 60   # Soft limit: sends SoftTimeLimitExceeded
CELERY_WORKER_PREFETCH_MULTIPLIER = 1   # Prevent worker hoarding long tasks
CELERY_TASK_ACKS_LATE = True            # Re-queue on worker crash

# Result persistence
CELERY_RESULT_EXPIRES = 60 * 60 * 24   # Keep results 24 hours

# Beat scheduler (for periodic tasks)
CELERY_BEAT_SCHEDULER = 'django_celery_beat.schedulers:DatabaseScheduler'

# Installed apps
INSTALLED_APPS += [
    'django_celery_results',
    'django_celery_beat',
]

Read the full file on GitHub · 458 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 458 lines · 49 tokens per session scan A 070faa076bfb

Subscribe to this mod's changes

django-celery is a skill published in the GitHub repository JunMystery/Agent-Guidance-Python (2 stars, last pushed 1mo ago), licensed MIT. It adds 49 tokens to every session and 3,096 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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