Agent skill

Django Celery

by affaan-m in affaan-m/ECC

Django + Celery async task patterns — configuration, task design, beat scheduling, retries, canvas workflows, monitoring, and testing.

MITAuto-check passedBackend & APIs

Install Django Celery

skills CLI
$ npx skills add affaan-m/ECC --skill django-celery -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install affaan-m/ECC django-celery --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/django-celery .claude/skills/django-celery && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
django-celery
GitHub stars
277k
Used in
1 other repo
Token cost
~3.3k tokens
SKILL.md length
226 words
Files
1
Skills in repo
683
Repo updated
First seen
Licence
MIT

At a glance

Django + Celery async task patterns — configuration, task design, beat scheduling, retries, canvas workflows, monitoring, and testing.

  • Adding background jobs
  • SKILL.md covers When to Activate, Project Setup, Task Design Patterns and Calling Tasks, plus 8 more sections
  • Calls pip and redis-cli
  • Scheduled tasks

What it does

Django Celery is an agent skill from affaan-m/ECC. Django + Celery async task patterns — configuration, task design, beat scheduling, retries, canvas workflows, monitoring, and testing. Use when adding background jobs, scheduled tasks, or async processing to a Django app.

Its SKILL.md is about 3.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Backend & APIs, covering Background jobs and Backend development. It works with Django. The repository describes itself as: The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond. The licence is MIT.

When your agent uses it

  • Adding background jobs
  • Scheduled tasks
  • Async processing to a Django app

Example prompts

  • “/django-celery”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 2d515e4. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • pip
    • redis-cli

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Django Celery loads about 3.3k tokens when it runs. Until then it costs about 59 tokens; SKILL.md has 226 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~59
When it runs · the whole SKILL.md, loaded when a task matches
~3.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from affaan-m/ECC at commit 2d515e4, republished under its MIT licence (© affaan-m). 226 words, ~3,279 tokens.

Download SKILL.mdSave it as .claude/skills/django-celery/SKILL.md (or your agent's skills folder).
name
django-celery
description
Django + Celery async task patterns — configuration, task design, beat scheduling, retries, canvas workflows, monitoring, and testing. Use when adding background jobs, scheduled tasks, or async processing to a Django app.
metadata.origin
ECC

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
bash
pip install 'celery[redis]' django-celery-results django-celery-beat
celery.py — App Entrypoint
python
# 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}')
python
# config/__init__.py
from .celery import app as celery_app

__all__ = ('celery_app',)
Django Settings
python
# 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',
]
Running Workers
bash
# Start worker (development)
celery -A config worker --loglevel=info

# Start beat scheduler (periodic tasks)
celery -A config beat --loglevel=info --scheduler django_celery_beat.schedulers:DatabaseScheduler

# Combined worker + beat (dev only, never production)
celery -A config worker --beat --loglevel=info

# Production: multiple workers with concurrency
celery -A config worker --loglevel=warning --concurrency=4 -Q default,high_priority

Task Design Patterns

Basic Task
python
# apps/notifications/tasks.py
from celery import shared_task
import logging

logger = logging.getLogger(__name__)

@shared_task(name='notifications.send_welcome_email')
def send_welcome_email(user_id: int) -> None:
    """Send welcome email to newly registered user."""
    from apps.users.models import User
    from apps.notifications.services import EmailService

    try:
        user = User.objects.get(pk=user_id)
    except User.DoesNotExist:
        logger.warning('send_welcome_email: user %s not found', user_id)
        return  # Idempotent — do not raise, task already impossible to complete

    EmailService.send_welcome(user)
    logger.info('Welcome email sent to user %s', user_id)
Retryable Task
python
@shared_task(
    bind=True,
    name='integrations.sync_to_crm',
    max_retries=5,
    default_retry_delay=60,       # seconds before first retry
    autoretry_for=(ConnectionError, TimeoutError),
    retry_backoff=True,           # exponential backoff
    retry_backoff_max=600,        # cap at 10 minutes
    retry_jitter=True,            # randomise to avoid thundering herd
)
def sync_contact_to_crm(self, contact_id: int) -> dict:
    """Sync contact to external CRM with retry on transient failures."""
    from apps.crm.services import CRMClient

    try:
        result = CRMClient().sync(contact_id)
        return result
    except CRMClient.RateLimitError as exc:
        # Specific retry delay from response header
        raise self.retry(exc=exc, countdown=int(exc.retry_after))
Idempotent Task Pattern

Design tasks so they can safely run multiple times with the same inputs:

python
@shared_task(name='orders.mark_shipped')
def mark_order_shipped(order_id: int, tracking_number: str) -> None:
    """Mark order as shipped — safe to run multiple times."""
    from apps.orders.models import Order

    updated = Order.objects.filter(
        pk=order_id,
        status=Order.Status.PROCESSING,    # Guard: only update if not already shipped
    ).update(
        status=Order.Status.SHIPPED,
        tracking_number=tracking_number,
    )

    if not updated:
        logger.info('mark_order_shipped: order %s already shipped or not found', order_id)
Task with Soft Time Limit
python
from celery.exceptions import SoftTimeLimitExceeded

@shared_task(
    bind=True,
    name='reports.generate_pdf',
    soft_time_limit=120,
    time_limit=150,
)
def generate_pdf_report(self, report_id: int) -> str:
    """Generate PDF report with graceful timeout handling."""
    from apps.reports.services import PDFGenerator

    try:
        path = PDFGenerator.build(report_id)
        return path
    except SoftTimeLimitExceeded:
        # Clean up partial files before hard kill
        PDFGenerator.cleanup(report_id)
        raise

Calling Tasks

python
from datetime import timedelta
from django.utils import timezone

# Fire and forget (async)
send_welcome_email.delay(user.pk)

# Schedule in the future
send_reminder.apply_async(args=[user.pk], countdown=3600)  # 1 hour from now
send_reminder.apply_async(args=[user.pk], eta=timezone.now() + timedelta(days=1))

# Apply with queue routing
sync_contact_to_crm.apply_async(args=[contact.pk], queue='high_priority')

# Run synchronously (tests / debugging only)
result = generate_pdf_report.apply(args=[report.pk])

Beat Scheduling (Periodic Tasks)

Code-Defined Schedule
python
# config/settings/base.py
from celery.schedules import crontab

CELERY_BEAT_SCHEDULE = {
    'cleanup-expired-sessions': {
        'task': 'users.cleanup_expired_sessions',
        'schedule': crontab(hour=2, minute=0),   # 2am daily
    },
    'sync-inventory': {
        'task': 'products.sync_inventory',
        'schedule': 60.0,                         # every 60 seconds
    },
    'weekly-digest': {
        'task': 'notifications.send_weekly_digest',
        'schedule': crontab(day_of_week='monday', hour=8, minute=0),
    },
}
Database-Defined Schedule (via django-celery-beat)
python
# Manage periodic tasks from Django admin or code
from django_celery_beat.models import PeriodicTask, CrontabSchedule
import json

schedule, _ = CrontabSchedule.objects.get_or_create(
    hour='*/6', minute='0',
    timezone='UTC',
)

PeriodicTask.objects.update_or_create(
    name='Sync inventory every 6 hours',
    defaults={
        'crontab': schedule,
        'task': 'products.sync_inventory',
        'args': json.dumps([]),
        'enabled': True,
    }
)

Canvas: Chaining and Grouping Tasks

python
from celery import chain, group, chord

# Chain: run tasks sequentially, passing results
pipeline = chain(
    fetch_data.s(source_id),
    transform_data.s(),          # receives fetch_data result as first arg
    load_to_warehouse.s(),
)
pipeline.delay()

# Group: run tasks in parallel
parallel = group(
    send_welcome_email.s(user_id)
    for user_id in new_user_ids
)
parallel.delay()

# Chord: parallel tasks + callback when all complete
result = chord(
    group(process_chunk.s(chunk) for chunk in data_chunks),
    aggregate_results.s(),       # called with list of chunk results
)
result.delay()

Error Handling and Dead Letter Queue

python
# apps/core/tasks.py
from celery.signals import task_failure

@task_failure.connect
def on_task_failure(sender, task_id, exception, args, kwargs, traceback, einfo, **kw):
    """Log all task failures to Sentry / alerting."""
    import sentry_sdk
    with sentry_sdk.new_scope() as scope:
        scope.set_context('celery', {
            'task': sender.name,
            'task_id': task_id,
            'args': args,
            'kwargs': kwargs,
        })
        sentry_sdk.capture_exception(exception)
python
# Route failed tasks to dead-letter queue after max retries
@shared_task(
    bind=True,
    max_retries=3,
    name='payments.charge_card',
)
def charge_card(self, order_id: int) -> None:
    from apps.payments.models import Order, FailedCharge

    try:
        _do_charge(order_id)
    except Exception as exc:
        if self.request.retries >= self.max_retries:
            # Persist to dead-letter table for manual review
            FailedCharge.objects.create(
                order_id=order_id,
                error=str(exc),
                task_id=self.request.id,
            )
            return  # Don't raise — task is permanently failed
        raise self.retry(exc=exc)

Testing Celery Tasks

Unit Testing (No Broker)
python
# tests/test_tasks.py
import pytest
from unittest.mock import patch, MagicMock
from apps.notifications.tasks import send_welcome_email

class TestSendWelcomeEmail:

    @pytest.mark.django_db
    def test_sends_email_to_existing_user(self, user):
        with patch('apps.notifications.services.EmailService') as mock_email:
            send_welcome_email(user.pk)
            mock_email.send_welcome.assert_called_once_with(user)

    @pytest.mark.django_db
    def test_skips_missing_user_gracefully(self):
        """Should not raise when user is deleted between enqueue and execute."""
        send_welcome_email(99999)  # Non-existent user — must not raise
Integration Testing with CELERY_TASK_ALWAYS_EAGER
python
# config/settings/test.py
CELERY_TASK_ALWAYS_EAGER = True      # Run tasks synchronously in tests
CELERY_TASK_EAGER_PROPAGATES = True  # Re-raise exceptions from tasks

# tests/test_integration.py
@pytest.mark.django_db
def test_registration_triggers_welcome_email(client):
    with patch('apps.notifications.services.EmailService') as mock_email:
        response = client.post('/api/users/', {
            'email': 'new@example.com',
            'password': 'strongpass123',
        })

    assert response.status_code == 201
    mock_email.send_welcome.assert_called_once()
Testing Retries
python
@pytest.mark.django_db
def test_task_retries_on_connection_error():
    with patch('apps.crm.services.CRMClient.sync') as mock_sync:
        mock_sync.side_effect = ConnectionError('timeout')

        with pytest.raises(ConnectionError):
            sync_contact_to_crm.apply(args=[1], throw=True)

        assert mock_sync.call_count == 1  # First attempt only when eager

Monitoring

bash
# Inspect active workers and queues
celery -A config inspect active
celery -A config inspect stats
celery -A config inspect reserved

# Check queue lengths (Redis)
redis-cli llen celery

# Flower: web-based real-time monitor
pip install flower
celery -A config flower --port=5555

Anti-Patterns

python
# BAD: Passing model instances — they may be stale by execution time
send_welcome_email.delay(user)        # Never pass ORM objects
send_welcome_email.delay(user.pk)     # Always pass PKs

# BAD: Calling tasks synchronously in production views
result = generate_report.apply()      # Blocks the request thread

# BAD: Non-idempotent task without guards
@shared_task
def charge_and_fulfill(order_id):
    order.charge()     # May charge twice if task retries!
    order.fulfill()

# GOOD: Idempotent with status guard
@shared_task
def charge_and_fulfill(order_id):
    order = Order.objects.select_for_update().get(pk=order_id)
    if order.status != Order.Status.PENDING:
        return  # Already processed
    order.charge()
    order.fulfill()

Production Checklist

CheckSetting
Worker restarts on crashsupervisord or systemd unit
CELERY_TASK_ACKS_LATE = TrueRe-queue tasks on worker crash
CELERY_WORKER_PREFETCH_MULTIPLIER = 1Fair distribution of long tasks
Separate queues per priority-Q default,high_priority,low_priority
CELERY_TASK_SOFT_TIME_LIMIT setGraceful timeout before hard kill
Sentry integrationCapture all task_failure signals
Flower or other monitorVisibility into queue depths
Beat runs on single node onlyPrevents duplicate scheduled task execution
  • django-patterns — ORM, service layer, and project structure
  • django-tdd — Testing Django models, views, and services
  • python-testing — pytest configuration and fixtures

© affaan-m, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/django-celery of affaan-m/ECC.

Open the folder on GitHubat commit 2d515e4

Used in 1 other repository

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in affaan-m/ECC, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Django Celery next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Django Celery compared with similar skills
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Django Celery this skillaffaan-m/ECC277k1 repos~3.3kAutomated safety check: PassMIT
Dj Architecturedvf/opinionated-django109—~4kAutomated safety check: NotesMIT
Django Celery Expertvintasoftware/django-ai-plugins153—~1.2kAutomated safety check: PassNone
Sentry Python SDKgetsentry/sentry-for-ai268—~4.1kAutomated safety check: PassApache-2.0
Django Prodavila7/claude-code-templates33k8 repos~1.7kAutomated safety check: PassMIT
Dj Servicesdvf/opinionated-django109—~3kAutomated safety check: NotesMIT

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Works with

Categories

Questions about Django Celery

What does Django Celery do?

Django + Celery async task patterns — configuration, task design, beat scheduling, retries, canvas workflows, monitoring, and testing. Django Celery is an agent skill from affaan-m/ECC. Django + Celery async task patterns — configuration, task design, beat scheduling, retries, canvas workflows, monitoring, and testing.

When should I use Django Celery?

Django Celery fits situations like: adding background jobs; scheduled tasks; async processing to a Django app.

How do I install Django Celery in Claude Code?

Run `npx skills add affaan-m/ECC --skill django-celery -a claude-code`. Or copy the skill folder (skills/django-celery in affaan-m/ECC) into .claude/skills/django-celery in your project. Claude Code loads it when a task matches its description.

How do I install Django Celery in Codex?

Run `npx skills add affaan-m/ECC --skill django-celery -a codex`. Or copy the skill folder (skills/django-celery in affaan-m/ECC) into .agents/skills/django-celery in your project. Codex loads it when a task matches its description.

Can I use Django Celery in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add affaan-m/ECC --skill django-celery -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/django-celery, .gemini/skills/django-celery, .github/skills/django-celery and .opencode/skills/django-celery in your project.

What does Django Celery need to run?

Going by SKILL.md and its folder, Django Celery needs the command-line tools its instructions call (pip and redis-cli). Our summary lists: Python 3.

Does Django Celery access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Django Celery safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Django Celery use?

Django Celery is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Django Celery use?

About 3.3k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Django Celery?

Skills that share tags, products or a category with Django Celery: Dj Architecture (dvf/opinionated-django, 109 stars), Django Celery Expert (vintasoftware/django-ai-plugins, 153 stars), Sentry Python SDK (getsentry/sentry-for-ai, 268 stars) and Django Pro (davila7/claude-code-templates, 33k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Django Celery?

affaan-m (a GitHub user) maintains it in affaan-m/ECC, which has 276,673 GitHub stars. The repository holds 683 skills in this directory. The repository was last updated on October 11, 2026.

Source: affaan-m/ECC on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.