django-celery

django-celery is a skill for Claude Code, Codex from unrealandychan/clean-code-skill. It costs 49 tokens per session (3,099 once invoked), scanned A, a copy of django-celery, MIT.

A guide to running background and scheduled work in Django with Celery, a task queue that processes jobs outside a web request, using Redis or RabbitMQ to pass messages.

In plain words
What is it for?
Use it to add background jobs, recurring schedules, retry handling, monitoring, queue workflows, and tests for Django tasks.
Why use it?
It helps keep slow work such as emails, PDF creation, and external API calls from making web requests wait, while providing patterns for retries and failures.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to add background jobs, recurring schedules, retry handling, monitoring, queue workflows, and tests for Django tasks.

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Install with agentmods
npx agentmods add skills/unrealandychan/clean-code-skill/django-celery
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.

Any agent
npx skills add unrealandychan/clean-code-skill --skill django-celery
Clone the repo
git clone --depth 1 https://github.com/unrealandychan/clean-code-skill

Made for: Claude Code, Codex.

Wrote this? Show the measurements

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README.md
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Per session 49 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,099 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 88% copy Near-identical to another mod 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 5.1 $0.00049 $0.03099
Opus 5 $0.00024 $0.01550
Sonnet 5 $0.00010 $0.00620
Haiku 4.5 $0.00005 $0.00310

Measured today against content hash 1f9189ece910, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, 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 today.

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.

Origin

This is a copy

88% identical to django-celery — 29 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/ecc/django-celery/SKILL.md · 459 lines

How it starts

The opening of the file, as written. The whole thing — 459 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 · 459 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. today First seen · 459 lines · 49 tokens per session scan A 1f9189ece910

Subscribe to this mod's changes

django-celery is a skill published in the GitHub repository unrealandychan/clean-code-skill (6 stars, last pushed yesterday), licensed MIT. It adds 49 tokens to every session and 3,099 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to django-celery, differing in 29 lines, and is treated as a copy.