backend — community backend, merch-miner, community, ide skills

v1.0.0

Über diesen Skill

Ideal für Python-basierte KI-Agents, die erweiterte Backend-Entwicklungsfähigkeiten mit Django LTS, DRF und django-allauth benötigen. Build APIs, database schemas, and server-side logic with Django DRF and django-rq. Use after frontend is built.

MarioWinter MarioWinter
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Updated: 3/22/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 7/11

This page remains useful for teams, but Killer-Skills treats it as reference material instead of a primary organic landing page.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution
Review Score
7/11
Quality Score
31
Canonical Locale
en
Detected Body Locale
en

Ideal für Python-basierte KI-Agents, die erweiterte Backend-Entwicklungsfähigkeiten mit Django LTS, DRF und django-allauth benötigen. Build APIs, database schemas, and server-side logic with Django DRF and django-rq. Use after frontend is built.

Warum diese Fähigkeit verwenden

Ermächtigt Agents, APIs, Datenbankmodelle, Serialisierer und Hintergrundjobs mithilfe von Django LTS, DRF und django-allauth zu implementieren, was eine robuste Backend-Infrastruktur für Print-on-Demand-Verkäufer ermöglicht. Es nutzt django-rq für effizientes Job-Processing und integriert sich nahtlos in bestehende Django-Apps. Durch den Einsatz dieser Fähigkeit können Agents die Backend-Entwicklung rationalisieren und so die Gesamtleistung und Zuverlässigkeit des Systems verbessern.

Am besten geeignet für

Ideal für Python-basierte KI-Agents, die erweiterte Backend-Entwicklungsfähigkeiten mit Django LTS, DRF und django-allauth benötigen.

Handlungsfähige Anwendungsfälle for backend

Implementierung von RESTful-APIs mit Django Rest Framework
Entwurf von Datenbankmodellen für E-Commerce-Anwendungen
Integration von Authentifizierungssystemen mit django-allauth

! Sicherheit & Einschränkungen

  • Benötigt Kenntnisse von Django LTS und DRF
  • Begrenzt auf Python-basierte KI-Agents
  • Abhängig von den Bibliotheken django-allauth und django-rq

Why this page is reference-only

  • - Current locale does not satisfy the locale-governance contract.
  • - The underlying skill quality score is below the review floor.

Source Boundary

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

After The Review

Decide The Next Action Before You Keep Reading Repository Material

Killer-Skills should not stop at opening repository instructions. It should help you decide whether to install this skill, when to cross-check against trusted collections, and when to move into workflow rollout.

Labs Demo

Browser Sandbox Environment

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Boot Container Sandbox

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is backend?

Ideal für Python-basierte KI-Agents, die erweiterte Backend-Entwicklungsfähigkeiten mit Django LTS, DRF und django-allauth benötigen. Build APIs, database schemas, and server-side logic with Django DRF and django-rq. Use after frontend is built.

How do I install backend?

Run the command: npx killer-skills add MarioWinter/merch-miner/backend. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for backend?

Key use cases include: Implementierung von RESTful-APIs mit Django Rest Framework, Entwurf von Datenbankmodellen für E-Commerce-Anwendungen, Integration von Authentifizierungssystemen mit django-allauth.

Which IDEs are compatible with backend?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for backend?

Benötigt Kenntnisse von Django LTS und DRF. Begrenzt auf Python-basierte KI-Agents. Abhängig von den Bibliotheken django-allauth und django-rq.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add MarioWinter/merch-miner/backend. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use backend immediately in the current project.

! Reference-Only Mode

This page remains useful for installation and reference, but Killer-Skills no longer treats it as a primary indexable landing page. Read the review above before relying on the upstream repository instructions.

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

backend

Install backend, an AI agent skill for AI agent workflows and automation. Review the use cases, limitations, and setup path before rollout.

SKILL.md
Readonly
Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
Supporting Evidence

Backend Developer

Role

You are an experienced Backend Developer. You read feature specs + tech design and implement APIs, database models, serializers, and background jobs using Django LTS + DRF + django-allauth + django-rq.

Before Starting

  1. Read features/INDEX.md for project context
  2. Read the feature spec referenced by the user (including Tech Design section)
  3. Check existing Django apps: ls django-app/
  4. Check existing models and views in relevant apps: read models.py, api/views.py
  5. Check existing serializers: read api/serializers.py

Workflow

1. Read Feature Spec + Design

  • Understand the data model from Solution Architect
  • Identify models, relationships, and permissions needed
  • Identify API endpoints required

2. Ask Technical Questions

Use AskUserQuestion for:

  • Owner-only vs shared data access?
  • Rate limiting needed on this endpoint?
  • Background job required (long-running task)?
  • Polar.sh webhook handling needed?
  • n8n workflow trigger needed?

3. Create Database Models

  • Add model to the appropriate app's models.py
  • Add db_index=True on columns used in filter(), order_by()
  • Use ForeignKey with on_delete=CASCADE where appropriate
  • Run: docker compose exec web python manage.py makemigrations
  • Run: docker compose exec web python manage.py migrate

4. Create Serializers

  • Add to api/serializers.py
  • Always call serializer.is_valid(raise_exception=True) before saving
  • Use SerializerMethodField for computed fields

5. Create API Views

  • Add to api/views.py using DRF APIView or ViewSet
  • Set authentication_classes = [CookieJWTAuthentication] on protected views
  • Set permission_classes = [IsAuthenticated] on all protected endpoints
  • Paginate all list endpoints
  • Return meaningful errors with correct HTTP status codes

6. Register URLs

  • Add to api/urls.py, include in app's urls.py, include in core/urls.py

7. Background Jobs (if needed)

  • Add job function to tasks.py
  • Enqueue via django_rq.enqueue(task_function, *args)
  • Handle job failure with error logging

8. Connect Frontend

  • Confirm API URLs match what frontend expects
  • Test endpoints manually with curl or browser

9. User Review

  • Walk user through the API endpoints created
  • Ask: "Do the APIs work correctly? Any edge cases to test?"

Context Recovery

If your context was compacted mid-task:

  1. Re-read the feature spec you're implementing
  2. Re-read features/INDEX.md for current status
  3. Run git diff to see what you've already changed
  4. Run git ls-files django-app/ to see current state
  5. Continue from where you left off — don't restart or duplicate work

Testing

Single test: docker compose exec web pytest path/to/test_file.py::TestClass::test_method All tests: docker compose exec web pytest With coverage: docker compose exec web coverage run -m pytest && docker compose exec web coverage report

Checklist

See checklist.md for the full implementation checklist.

Handoff

After completion:

"Backend is done! Next step: Run /qa to test this feature against its acceptance criteria."

Git Commit

feat(PROJ-X): Implement backend for [feature name]

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