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Django Senior
Senior-level Django 5/6 optimization, ORM tuning, transaction patterns, and Celery task architecture.
$5
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Django Senior
Example session with this skill installed
Review this view for N+1 queries and transaction safety:
@transaction.atomic
def update_order(request, pk):
order = Order.objects.get(pk=pk)
order.status = 'paid'
order.save()
send_email.delay(order)
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- Compiled the django senior
I have reviewed the update_order view. There are two critical issues: a race condition during the update and a serialization error in the Celery task.
Senior Django Review & Refactor
The current implementation retrieves the order without a lock, meaning concurrent requests could overwrite state. Additionally, passing a model instance to a Celery task is an anti-pattern; you must pass the primary key.
from django.db import transaction
from .tasks import send_email
from .models import Order
@transaction.atomic
def update_order(request, pk):
# 1. Use select_for_update to lock the row and prevent race conditions
order = Order.objects.select_for_update().get(pk=pk)
order.status = 'paid'
order.save()
# 2. Use on_commit to ensure the task only triggers if the DB transaction succeeds
# 3. Pass order.id instead of the model instance to avoid stale data/serialization issues
transaction.on_commit(lambda: send_email.delay(order.id))
Next steps
- Update the
send_emailtask definition to accept anorder_idand perform a.get()inside the task. - Add a
pytest-djangotest case usingassertNumQueriesto verify the locking behavior. - Ensure your DB backend supports
select_for_update(PostgreSQL/MySQL recommended).
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About this skill
Django development often suffers from silent performance killers like N+1 queries, race conditions in transactions, and incorrect Celery task patterns. This skill provides a senior-level reference and implementation guide to build production-grade Django 5.x and 6.x applications with a focus on optimization and modern API design.
What it does
- ORM optimization provides patterns for N+1 detection, select_related/prefetch_related, and range-based index lookups.
- Transaction management implements pessimistic locking with select_for_update and safe side-effect deferral using on_commit.
- Modern API design supports django-ninja for FastAPI-style endpoints and TurboDRF for role-based REST.
- Background processing handles Celery task design, retries, and safe model ID passing to avoid serialization issues.
- Migration assistance tracks breaking changes and new native features for moving from Django 5.x to 6.0.
How it works
- Analyze code by providing a model, view, or task for a senior-level review against the 20-point anti-pattern checklist.
- Optimize queries by requesting a refactor of existing ORM lookups to use better indexing or prefetching strategies.
- Design APIs by defining a model and requesting either a Ninja schema or a TurboDRF configuration.
- Hardened security by asking for a settings audit or CSP configuration for Django 6.0 environments.
Frameworks & tools
Built for Django 5.x and 6.x. Supports django-ninja, TurboDRF, django-bolt, pytest-django, Celery, HTMX, and django-storages (S3/GCS). Requires Python 3.10+ for Django 5 and 3.12+ for Django 6.
Why this beats prompting it yourself
Generic prompts often suggest outdated patterns or ignore subtle Django-specific pitfalls like using .first() on prefetched sets. This skill enforces strict senior-level practices, such as preventing race conditions and ensuring database integrity through proper transaction blocks.
Use cases
- Refactor a slow dashboard view by fixing N+1 queries and implementing prefetch to_attr.
- Implement a thread-safe payment processing flow using select_for_update and on_commit notifications.
- Transition a legacy DRF codebase to django-ninja for better performance and type safety.
- Upgrade a Django 5.1 project to 6.0 while adopting new native CSP middleware and template partials.
Known limitations
This skill is strictly for Django 5.x and 6.x projects. It does not support Flask, FastAPI, or older Django versions below 5.0. It requires manual setup of Celery and Redis/RabbitMQ infrastructure.
How to use
Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.
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