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Django Storages S3

CommunityPopular
Jeffallan
django-storages-s3

Use when configuring Django to store static and media files on AWS S3 with django-storages. Invoke when working with the STORAGES setting, S3 buckets, presigned URLs, CloudFront, or boto3-backed file storage in settings.py. Configures the Django 4.2+ STORAGES dict, public/private custom backends, presigned GET/POST URLs, IAM policies, and S3 mocking for tests. Trigger terms: django-storages, S3, boto3, S3Boto3Storage, STORAGES, presigned URL, CloudFront, media files, collectstatic, AWS_STORAGE_BUCKET_NAME.

Overview

PublisherJeffallan
Repositoryclaude-skills
Skill namedjango-storages-s3
Stars
11.5K
Forks
1.1K
Bundled files
4
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.

  • 4 bundled files

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

  • Open source

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

Installation

Install the Django Storages S3 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.
  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/Jeffallan/claude-skills.git /tmp/claude-skills
mkdir -p .claude/skills
cp -r /tmp/claude-skills/skills/django-storages-s3 .claude/skills/django-storages-s3
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Django Storages S3 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 Storages S3 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 Storages S3 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 Storages S3

Senior Django specialist for production-grade file storage on AWS S3 via django-storages and boto3 — public and private media, static files, presigned URLs, and CloudFront.

When to Use This Skill

  • Serving static and/or media files from AWS S3 instead of the local filesystem
  • Configuring the Django 4.2+ STORAGES dict or legacy DEFAULT_FILE_STORAGE
  • Separating public (CDN-served) and private (presigned) file backends
  • Generating presigned download or direct browser-to-S3 upload URLs
  • Fronting S3 with CloudFront and writing a least-privilege IAM policy
  • Migrating local FileField/ImageField storage to S3 without code changes
  • Testing storage code without hitting S3

Core Workflow

  1. Install & registerpip install django-storages[s3] boto3; add "storages" to INSTALLED_APPS
  2. Configure credentials — Load from env vars or rely on an attached IAM role; never hardcode
  3. Wire the STORAGES dict — Set default (media) and staticfiles backends with separate location prefixes
  4. Add named backends — Split public vs. private buckets/ACLs as additional STORAGES entries when needed
  5. Verify & test — Run collectstatic, confirm uploads land in S3, and mock S3 in tests with InMemoryStorage or moto

Reference Guide

Load detailed guidance based on context:

TopicReferenceLoad When
Settings & STORAGESreferences/configuration.mdCore settings, 4.2+ vs legacy, CloudFront
Custom backendsreferences/custom-backends.mdPublic vs. private buckets, per-field storage
Presigned URLsreferences/presigned-urls.mdDownload links, direct browser uploads
Testing & IAMreferences/testing-storages.mdMocking S3, IAM policy, common pitfalls

Minimal Working Example

The snippet below demonstrates the core MUST DO constraints: env-loaded credentials, STORAGES dict, separate media/static locations, and default_acl=None on the media backend.

python
# settings.py
import os

AWS_STORAGE_BUCKET_NAME = os.environ["AWS_STORAGE_BUCKET_NAME"]
AWS_S3_REGION_NAME = os.environ.get("AWS_S3_REGION_NAME", "us-east-1")
AWS_S3_CUSTOM_DOMAIN = f"{AWS_STORAGE_BUCKET_NAME}.s3.{AWS_S3_REGION_NAME}.amazonaws.com"
# On EC2/ECS/Lambda, omit keys entirely — boto3 uses the attached IAM role.

STORAGES = {
    "default": {  # media uploads
        "BACKEND": "storages.backends.s3boto3.S3Boto3Storage",
        "OPTIONS": {
            "bucket_name": AWS_STORAGE_BUCKET_NAME,
            "location": "media",
            "default_acl": None,        # rely on bucket policy, not per-object ACLs
            "file_overwrite": False,
            "querystring_auth": False,  # public objects → clean URLs
        },
    },
    "staticfiles": {
        "BACKEND": "storages.backends.s3boto3.S3StaticStorage",
        "OPTIONS": {
            "bucket_name": AWS_STORAGE_BUCKET_NAME,
            "location": "static",
        },
    },
}

MEDIA_URL = f"https://{AWS_S3_CUSTOM_DOMAIN}/media/"
STATIC_URL = f"https://{AWS_S3_CUSTOM_DOMAIN}/static/"
python
# models.py — uploads go straight to S3 on save()
from django.db import models

class Document(models.Model):
    file = models.FileField(upload_to="docs/")  # uses STORAGES["default"]

Auditing an Existing Configuration

When reviewing a project that already uses S3 (not greenfield), walk this checklist — each item is a constraint below rephrased as "find X, confirm Y":

  1. Credentialsgrep -rn "AWS_SECRET_ACCESS_KEY\|aws_secret" settings/ → confirm values come from os.environ/django-environ or an IAM role, never literals committed to the repo.
  2. ACLsgrep -rn "default_acl\|AWS_DEFAULT_ACL" . → on buckets created after April 2023, every value must be None. Any "public-read"/"private" will raise AccessControlListNotSupported; public access belongs in a bucket policy.
  3. Storage backend — confirm Django 4.2+ uses the STORAGES dict, not DEFAULT_FILE_STORAGE/STATICFILES_STORAGE (removed in Django 5.1, so silently ignored on 5.1/5.2/6.0); confirm the static class is S3StaticStorage, not a fabricated name.
  4. Locations — confirm default (media) and staticfiles have distinct location prefixes or buckets so collectstatic never collides with uploads.
  5. Region — confirm region_name (or the global AWS_S3_REGION_NAME) matches the bucket's real region and that AWS_S3_CUSTOM_DOMAIN includes the region segment for non-us-east-1 buckets.
  6. Presigning — for private backends, confirm querystring_auth=True and custom_domain=None; confirm presigned .url() results aren't cached past AWS_QUERYSTRING_EXPIRE.
  7. Overwrite cleanup — where file_overwrite=False, confirm replaced files are explicitly deleted (otherwise superseded objects leak).
  8. IAM — confirm the policy grants only Get/Put/Delete/ListBucket on the bucket ARN, not broader S3 access.

Constraints

MUST DO

  • Load AWS credentials from environment variables or an attached IAM role
  • Set default_acl=None so bucket policies (not object ACLs) control access
  • Give static and media files separate location prefixes or separate buckets
  • Use the STORAGES dict on Django 4.2+ (same config through 5.2 LTS and 6.0); DEFAULT_FILE_STORAGE/STATICFILES_STORAGE were removed in 5.1, so reserve them for < 4.2 only
  • Set custom_domain=None on any backend that issues presigned URLs
  • Mock S3 (InMemoryStorage or moto) in tests instead of hitting real buckets

MUST NOT DO

  • Hardcode AWS_SECRET_ACCESS_KEY in settings.py or commit it
  • Mix querystring_auth=True with a custom_domain (presigning breaks)
  • Mix static and media files under the same prefix
  • Grant the IAM user broader than Get/Put/Delete/ListBucket on the bucket ARN
  • Rely on per-object ACLs on buckets created after April 2023 (ACLs disabled by default)

Knowledge Reference

django-storages, S3Boto3Storage, S3StaticStorage, boto3, STORAGES dict, presigned URLs, generate_presigned_post, CloudFront, IAM policy, InMemoryStorage, moto

Related Skills

  • django-expert — core Django models, DRF, and ORM that produce the files this skill persists to S3
  • fullstack-guardian — secure end-to-end upload flows and access control around stored files
  • devops-engineer — provisioning the S3 buckets, IAM roles, and CloudFront distributions this skill targets

Documentation

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 Storages S3 AI skill do?

Use when configuring Django to store static and media files on AWS S3 with django-storages. Invoke when working with the STORAGES setting, S3 buckets, presigned URLs, CloudFront, or boto3-backed file storage in settings.py. Configures the Django 4.2+ STORAGES dict, public/private custom backends, presigned GET/POST URLs, IAM policies, and S3 mocking for tests. Trigger terms: django-storages, S3, boto3, S3Boto3Storage, STORAGES, presigned URL, CloudFront, media files, collectstatic, AWS_STORAGE_BUCKET_NAME.

Why use Django Storages S3 on TypingMind?

Because you install it once and use it with any model. Django Storages S3 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 Storages S3 in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Jeffallan/claude-skills/tree/main/skills/django-storages-s3. 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 Storages S3?

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 Storages S3?

As many as you like. As long as a model supports skills, you can use Django Storages S3 with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Django Storages S3 AI skill free?

Yes. It is published on GitHub by Jeffallan 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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