How does this skill differ from using a standard AI model for Django coding?

This skill enforces production-hardened patterns like multi-brand factory architectures, native PostgreSQL optimizations, and lock-safe migrations that standard AI models often overlook or misconfigure.

Which AI agents or IDEs are compatible with this skill?

It is optimized for use with advanced coding assistants like GitHub Copilot, Cursor, and Windsurf, specifically when they are configured to follow custom project instructions or .cursorrules files.

Can I apply this skill to an existing Django project, or is it only for new services?

Yes, while the skill promotes high-performance PostgreSQL and Celery patterns, it is designed to be injected into existing codebases to refactor legacy views and optimize current database queries.

Are there specific technical dependencies required for this skill to function correctly?

The skill requires a PostgreSQL database environment and assumes the use of Django REST Framework; it also performs best when structured logging and Celery are part of your stack.

What exactly is included in the purchase of this skill?

You receive the complete logic set for enforcing these engineering standards, including architectural checklists, PR review guidelines, and scaffold templates for the supported patterns.

How are updates handled if Django or PostgreSQL release new versions?

The skill is periodically updated to reflect the latest stable releases of Django and DRF, as well as evolving best practices for high-concurrency PostgreSQL management.

Django Backend Engineering Standards

Example session with this skill installed

Add an endpoint that lets a partner push product listings in bulk. They may resend the same payload, so it has to be safe to retry. Follow our Django backend standards.

Read your context and instructions
Compiled the django backend engineering

Summary

Added a bulk listings endpoint that is safe to retry. Example output: the exact file layout depends on your service style (factory or layered), which the skill checks first.

What the agent did

Step	Decision	Why
Identify style	Layered service, so `view -> service -> repository`	Match the file being edited, then the repo
Validate	Scrubber runs before any write and returns clean DTOs	Bad rows are rejected early with clear errors
Persist	One bulk upsert keyed on `(partner_id, external_id)`	A resend updates rows instead of duplicating them
Publish	A Celery task serializes and indexes the changed ids	The request stays fast, the task is idempotent and retried
Auth	Partner API key, private-by-default permission class	No endpoint is public by accident

Core of the change

Listing.objects.bulk_create(
    rows,
    update_conflicts=True,
    unique_fields=["partner_id", "external_id"],
    update_fields=["title", "price", "updated_at"],
)
publish_listings.delay(ids=changed_ids)  # idempotent, retried with backoff

Checks before merge

No N+1 on the serializer context (prefetch the related rows once)
Migration for the unique constraint uses a concurrent index
Structured log line with partner id and row counts (structlog)

Next steps

Add an API test that posts the same payload twice and asserts the row count is unchanged.
Add a Celery test for the retry path.

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About this skill

Stop re-deciding how a Django backend should be built

This skill gives your coding agent a complete, opinionated rulebook for production Django and DRF services. It doesn't teach Django. It tells the agent which pattern to use, why, and what the bad alternative looks like, so the code it writes matches a mature codebase on the first try.

How it helps

Consistent architecture. The agent works out whether a service is multi-brand factory style or layered/repository style, then follows that style instead of mixing both.
Safer data writes. Validate, upsert, publish: PostgreSQL-native ON CONFLICT upserts, then serialize to the index and cache.
Fewer incidents. Timeout-aware migrations, concurrent indexes, and idempotent Celery tasks with retries.
Less review back-and-forth. Naming, exceptions, structured logging (structlog) and test conventions are spelled out.
Auth that fits. Keycloak authentication, a Principal permission model, and API keys for service-to-service calls.

What's inside

A short SKILL.md router plus 10 reference guides the agent loads only when it touches that area: architecture, API and views, serializers and scrubbers, models and migrations, queries and database, auth and Keycloak, Celery and jobs, caching and search, logging and observability, and code style and testing.

Every practice explains why it exists, where it's used, when to use it, the tradeoffs, and a production example.

Use it when

Building or reviewing Django/DRF features (views, models, serializers, tasks, queries, auth, caching, search) or scaffolding a new service.

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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Django Backend Engineering Standards