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Use when building, reviewing, securing, testing or shipping a Django app — models, migrations, QuerySets/managers, FBV/CBV views, forms, the admin, settings split, and Django REST Framework (serializers, ModelViewSet, permissions). NOT async FastAPI/Pydantic services (that is `fastapi`), NOT Postgres schema/index work (that is `postgresdb`).

Overview

Publisherericrisco
Repositoryrsc-harness
Skill namedjango
Stars
239
Forks
24
Bundled files
6
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ericrisco on GitHub. Read the source before you install it.

Installation

Install the Django AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/ericrisco/rsc-harness/tree/main/skills/django
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ericrisco/rsc-harness.git /tmp/rsc-harness
mkdir -p .claude/skills
cp -r /tmp/rsc-harness/skills/django .claude/skills/django
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Django in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Django on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Django is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Django web applications

The single authoritative skill for building, reviewing, securing, testing and shipping a Django app — the batteries-included, ORM-first, request/response Python framework.

Mental model: a Django project is apps composed of fat-but-thin-enough models (domain + query logic on the model/manager), views that orchestrate (FBV/CBV/DRF) and never own SQL, an admin/forms layer, and a settings module split by environment. The ORM, migrations, auth, admin, CSP and the test runner are all first-party. Reach for the framework before you add a dependency.

Pinned stack (2026-06)

  • Django 5.2 LTS — the production default. Released 2025-04-02, security fixes until ~April 2028, supports Python 3.10–3.14. New in 5.2: all models auto-imported in shell, CompositePrimaryKey, BoundField customization.
  • Django 6.0 — released 2025-12-03 (non-LTS, ~8 months until 6.1). Choose it only when you want the new built-in Tasks framework (background jobs without Celery) or native CSP (ContentSecurityPolicyMiddleware, SECURE_CSP) and can take the shorter support window. Drops Python 3.10/3.11; supports 3.12–3.14.
  • Django REST Framework 3.17.1 (2026-03-24) — adds Django 6.0 + Python 3.14 support.
  • Python 3.12+, pytest-django, factory_boy. ruff/uv and type-hint policy live in python.

Version rule: default to 5.2 LTS. Pick 6.0 only for a concrete Tasks/CSP need, and say so.

Route elsewhere

SituationRoute to
Async service, fastapi/pydantic/uvicorn, async SQLAlchemyfastapi
Postgres schema design, EXPLAIN ANALYZE, indexing strategy, RLS, poolingpostgresdb
Cross-stack OWASP/STRIDE threat modelingsecure-coding
Container/Compose/CI, gunicorn prod tuning, collectstatic pipelinedeployment
REST contract design (cursor vs offset, status codes, versioning)api-design
ruff/uv/general type hints, packagingpython

Project shape

Split settings by environment; never ship one settings.py toggled by DEBUG.

text
src/
  manage.py
  config/
    settings/
      base.py      # shared; reads secrets from os.environ
      dev.py       # from base import *; DEBUG=True; local hosts
      prod.py      # from base import *; DEBUG=False; SECURE_*; CSP
  catalog/         # an app = a bounded domain
    models.py  managers.py  views.py  serializers.py  urls.py  admin.py
    migrations/
    tests/
  • Read secrets with os.environ["SECRET_KEY"] (or django-environ). Never commit a literal SECRET_KEY — a leaked key forges sessions and signed tokens.
  • Select env via DJANGO_SETTINGS_MODULE=config.settings.prod, not an if DEBUG branch.
  • One app = one domain. Resist a single core app that accretes everything.

Models

Put domain and query logic on the model and its manager. The view stays thin.

python
# managers.py
from django.db import models

class ArticleQuerySet(models.QuerySet):
    def published(self):
        return self.filter(status=Article.Status.PUBLISHED)

    def for_reader(self):  # composes; reused everywhere, tested once
        return self.published().select_related("author")

# models.py
class Article(models.Model):
    class Status(models.TextChoices):
        DRAFT = "draft", "Draft"
        PUBLISHED = "published", "Published"

    tenant = models.ForeignKey("Tenant", on_delete=models.CASCADE)
    slug = models.SlugField()
    author = models.ForeignKey("Author", on_delete=models.PROTECT)
    status = models.CharField(max_length=16, choices=Status.choices, default=Status.DRAFT)
    published_at = models.DateTimeField(null=True, blank=True)

    objects = ArticleQuerySet.as_manager()

    class Meta:
        constraints = [
            models.UniqueConstraint(fields=["tenant", "slug"], name="uniq_tenant_slug"),
            models.CheckConstraint(
                check=models.Q(status="draft") | models.Q(published_at__isnull=False),
                name="published_needs_date",
            ),
        ]
        indexes = [models.Index(fields=["tenant", "status"])]
  • Constraints live in the DB, not just Python. A UniqueConstraint/CheckConstraint is enforced under concurrency; a clean() check is not. Validate-in-Python-only is a foot-gun.
  • on_delete is mandatory and load-bearing: CASCADE deletes children, PROTECT blocks the delete, SET_NULL orphans. Choosing wrong silently destroys data — pick deliberately.
  • Multi-column PK (5.2+): pk = models.CompositePrimaryKey("tenant_id", "id").
  • Bad→Good for business logic:
python
# Bad: logic in the view — untested, unreusable, duplicated across endpoints
def publish(request, pk):
    a = Article.objects.get(pk=pk)
    a.status = "published"; a.published_at = timezone.now(); a.save()

# Good: a method on the model — one place, testable, reused by view/admin/command
class Article(models.Model):
    def publish(self):
        self.status = self.Status.PUBLISHED
        self.published_at = timezone.now()
        self.save(update_fields=["status", "published_at"])

QuerySet performance

The N+1 is the single most common Django defect: one query for the list, then one more per row.

python
# Bad: 1 + N queries — each .author touches the DB inside the loop
for a in Article.objects.all():
    print(a.author.name)

# Good: 2 queries total (FK -> JOIN; reverse/M2M -> second query)
for a in Article.objects.select_related("author").prefetch_related("tags"):
    print(a.author.name, [t.name for t in a.tags.all()])
You are followingUseCost
Forward ForeignKey / OneToOneselect_related(...)SQL JOIN, 1 query
Reverse FK, ManyToManyprefetch_related(...)2nd query, joined in Python
Prefetch that itself needs filter/orderPrefetch("x", queryset=...)controlled 2nd query
  • Need existence, not rows? qs.exists(), never len(qs) or if qs.count().
  • Need a few columns of a wide row? .only("id", "slug") / .defer("body").
  • Computed totals belong in the DB: annotate(...) / aggregate(...), not a Python loop.
  • Many inserts: bulk_create(objs) — one round-trip, not N .save() calls.
  • Never Model.objects.all() then slice/filter in Python; push it into the QuerySet.

Deeper recipes (assertNumQueries, Prefetch, .explain(), ORM indexing) → references/orm-performance.md.

Views & URLs

Keep views thin: validate input, call a model/manager method, return a response. No SQL.

NeedUse
One bespoke action, custom flowfunction-based view (FBV)
Standard list/detail/create/update/delete on a modelgeneric CBV (ListView, DetailView, …)
JSON API consumed by a client/SPAdrop to DRF (do not hand-roll JsonResponse CRUD)

For the DRF surface — serializers, ModelViewSet, routers, permissions, throttling, pagination, filtering, nested-serializer N+1, versioning — see references/drf.md. The thin-view rule still holds: a fat serializer that walks relations per row is just an N+1 wearing a tie.

Migrations

bash
python manage.py makemigrations catalog   # generate from model diff
python manage.py migrate                   # apply
python manage.py makemigrations --check    # CI gate: fail if a model drifts from migrations
  • Never edit a migration that has been applied anywhere. Add a new one. Editing rewrites history and breaks every environment that already ran it.
  • Data backfills go through migrations.RunPython(forward, reverse) with a reverse, not a one-off script. Use the historical model from apps.get_model(...), not the imported class.
  • Schema changes on a live table that you cannot afford to lock are expand-and-contract; the Postgres-side mechanics (lock modes, batching) live in postgresdb.

Security

Set these in prod.py. Then prove it: python manage.py check --deploy must come back clean.

SettingValueWhy
DEBUGFalseTrue leaks settings + a stack-trace shell to the world
ALLOWED_HOSTSexplicit domains['*'] enables Host-header attacks
SECRET_KEYfrom os.environa literal in source forges signed cookies/tokens
SECURE_SSL_REDIRECTTrueforce HTTPS
SECURE_HSTS_SECONDS31536000 (+ include-subdomains, preload)the check --deploy warning you saw is this being 0
SESSION_COOKIE_SECURE / CSRF_COOKIE_SECURETruestop cookie leak over HTTP
SECURE_CSP (Django 6.0)a real policy + noncenative CSP; pre-6.0 use django-csp
  • CSRF protection is on by default — keep CsrfViewMiddleware; do not blanket-exempt views.
  • The ORM parameterizes queries. Only .raw(), .extra() and cursor.execute() with an f-string/%-built string reopen SQL injection. Pass params, never interpolate.

Full SECURE_* checklist, CSP nonce/report-only, upload/SSRF, ORM-injection → references/security.md.

Testing

python
import pytest
from rest_framework.test import APIClient

@pytest.mark.django_db
def test_owner_only(article, owner):
    client = APIClient()
    assert client.get(f"/api/articles/{article.pk}/").status_code == 403  # anon
    client.force_authenticate(owner)
    assert client.get(f"/api/articles/{article.pk}/").status_code == 200
  • pytest-django + @pytest.mark.django_db; run with --reuse-db to skip rebuilds locally.
  • TestCase wraps each test in a rolled-back transaction (fast). Use TransactionTestCase only when you test on_commit hooks or real commit behavior.
  • Lock in N+1 fixes with assertNumQueries(2) — it fails the build when a relation regresses.
  • Build instances with factory_boy, not 30 lines of Model.objects.create(...).

Setup, fixtures, transactional DB, coverage → references/testing.md.

Background work

NeedUse
New project on Django 6.0, simple enqueue-and-forget jobsthe built-in Tasks framework
Pre-6.0, or you need schedules/retries/fan-out/result backends/workers at scaleCelery

Either way: enqueue from the model/service layer, never block the request thread.

Anti-patterns

Anti-patternWhy it bitesDo instead
Business logic in the viewuntested, duplicated across endpointsmethod on the model/manager
f-string SQL into .raw()/.extra()/cursor.executeSQL injectionparameterized queries
Looping rows touching .authorN+1 queriesselect_related/prefetch_related
DEBUG=True in prodleaks settings + stack tracesDEBUG=False in prod.py
SECRET_KEY literal in sourceforged sessions/tokensos.environ
Validation only in clean()races under concurrencyDB UniqueConstraint/CheckConstraint
Model.objects.all() in a template loopone query per iterationprefetch in the view
ModelViewSet with no permission_classesendpoint open to the worldexplicit permission class
Fat serializer walking relationsN+1 per responseprefetch + assertNumQueries
Editing an applied migrationbreaks every env that ran itnew migration
len(qs) / qs.count() to test existencefull fetch/COUNTqs.exists()
Swallowing Model.DoesNotExist silentlyhidden bugsget_object_or_404 or handle explicitly

Verify

scripts/verify.sh [TARGET] greps tracked Django source for high-signal foot-guns: FAIL on a literal SECRET_KEY, ALLOWED_HOSTS = ['*'], or f-string SQL in .raw()/.extra()/cursor.execute; WARN on DEBUG = True outside a dev settings file and a ModelViewSet/APIView with no permission_classes. Read-only, exit 0 on a clean or empty target. It is a lint, not a substitute for manage.py check --deploy or the test suite.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Django AI skill do?

Use when building, reviewing, securing, testing or shipping a Django app — models, migrations, QuerySets/managers, FBV/CBV views, forms, the admin, settings split, and Django REST Framework (serializers, ModelViewSet, permissions). NOT async FastAPI/Pydantic services (that is `fastapi`), NOT Postgres schema/index work (that is `postgresdb`).

Why use Django on TypingMind?

Because you install it once and use it with any model. Django is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Django in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ericrisco/rsc-harness/tree/main/skills/django. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Django?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Django?

As many as you like. As long as a model supports skills, you can use Django with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Django AI skill free?

Yes. It is published on GitHub by ericrisco under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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