django-celery-expert
Expert Django Celery guidance for asynchronous task processing. Use when designing background tasks, configuring Celery workers, handling task retries and errors, optimizing Celery performance, implementing periodic tasks with Celery Beat, or setting up production monitoring for Celery. Do not use for general Django questions unrelated to Celery, non-Celery task systems (Django Q, Huey, RQ), ML/data pipeline orchestration (Airflow, Prefect), or frontend and API-only concerns. Follows Vinta's Django Celery best practices.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/vintasoftware/django-ai-plugins --skill django-celery-expert- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads django-celery-expert automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Django Celery Expert
Instructions
Step 1: Classify the Request
Identify the task category from the request:
- Django integration — transaction safety, ORM patterns, testing, request correlation → read
references/django-integration.md - Task design — new tasks, calling patterns, chains/groups/chords, idempotency → read
references/task-design-patterns.md - Configuration — broker setup, result backend, worker settings, queue routing → read
references/configuration-guide.md - Error handling — retries, backoff, dead letter queues, timeouts → read
references/error-handling.md - Periodic tasks — Celery Beat, crontab schedules, dynamic schedules, timezone handling → read
references/periodic-tasks.md - Monitoring — Flower, Prometheus, logging, debugging stuck tasks → read
references/monitoring-observability.md - Production deployment — scaling, supervision, containers, health checks → read
references/production-deployment.md
If the request spans multiple categories, read all relevant reference files before continuing.
Step 2: Read the Reference File(s)
Read each reference file identified in Step 1. Do not proceed to implementation without reading the relevant reference.
Step 3: Implement
Apply the patterns from the reference file. Before presenting the solution, verify:
- Task arguments are serializable (pass IDs, not model instances)
- Tasks with retries enabled are idempotent
- Errors are logged with context
- Long-running tasks have timeouts configured
Examples
Basic Background Task
Request: "Send welcome emails in the background after user registration"
# tasks.py
from celery import shared_task
from django.core.mail import send_mail
@shared_task(bind=True, max_retries=3)
def send_welcome_email(self, user_id):
from users.models import User
try:
user = User.objects.get(id=user_id)
send_mail(
subject="Welcome!",
message=f"Hi {user.name}, welcome to our platform!",
from_email="noreply@example.com",
recipient_list=[user.email],
)
except User.DoesNotExist:
pass
except Exception as exc:
raise self.retry(exc=exc, countdown=60 * (2 ** self.request.retries))
# views.py — queue only after the transaction commits
from django.db import transaction
def register(request):
user = User.objects.create(...)
transaction.on_commit(lambda: send_welcome_email.delay(user.id))
return redirect("dashboard")
Task with Progress Tracking
Request: "Process a large CSV import with progress updates"
@shared_task(bind=True)
def import_csv(self, file_path, total_rows):
from myapp.models import Record
with open(file_path) as f:
reader = csv.DictReader(f)
for i, row in enumerate(reader):
Record.objects.create(**row)
if i % 100 == 0:
self.update_state(
state="PROGRESS",
meta={"current": i, "total": total_rows},
)
return {"status": "complete", "processed": total_rows}
# Poll progress
result = import_csv.AsyncResult(task_id)
if result.state == "PROGRESS":
progress = result.info.get("current", 0) / result.info.get("total", 1)
Workflow with Chains
Request: "Process an order: validate inventory, charge payment, then send confirmation"
from celery import chain
@shared_task
def validate_inventory(order_id):
order = Order.objects.get(id=order_id)
if not order.items_in_stock():
raise ValueError("Items out of stock")
return order_id
@shared_task
def charge_payment(order_id):
order = Order.objects.get(id=order_id)
order.charge()
return order_id
@shared_task
def send_confirmation(order_id):
Order.objects.get(id=order_id).send_confirmation_email()
def process_order(order_id):
chain(
validate_inventory.s(order_id),
charge_payment.s(),
send_confirmation.s(),
).delay()
FAQ
What does the django-celery-expert skill do?
Expert Django Celery guidance for asynchronous task processing. Use when designing background tasks, configuring Celery workers, handling task retries and errors, optimizing Celery performance, implementing periodic tasks with Celery Beat, or setting up production monitoring for Celery. Do not use for general Django questions unrelated to Celery, non-Celery task systems (Django Q, Huey, RQ), ML/data pipeline orchestration (Airflow, Prefect), or frontend and API-only concerns. Follows Vinta's Django Celery best practices.
How do I install django-celery-expert?
Run npx skills add https://github.com/vintasoftware/django-ai-plugins --skill django-celery-expert in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/django-celery-expert/ (personal) or .claude/skills/django-celery-expert/ (project).
Which AI agents can use django-celery-expert?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is django-celery-expert safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains django-celery-expert?
It is published by vintasoftware in vintasoftware/django-ai-plugins. It has 1,135 recorded installs.
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