backend — community backend, merch-miner, community, ide skills, Claude Code, Cursor, Windsurf

v1.0.0

이 스킬 정보

Django LTS, DRF 및 django-allauth를 사용하여 고급 백엔드 개발 기능이 필요한 Python 기반 AI 에이전트에 적합합니다. Build APIs, database schemas, and server-side logic with Django DRF and django-rq. Use after frontend is built.

MarioWinter MarioWinter
[0]
[0]
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 operators, 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

Django LTS, DRF 및 django-allauth를 사용하여 고급 백엔드 개발 기능이 필요한 Python 기반 AI 에이전트에 적합합니다. Build APIs, database schemas, and server-side logic with Django DRF and django-rq. Use after frontend is built.

이 스킬을 사용하는 이유

Django LTS, DRF 및 django-allauth를 사용하여 API, 데이터베이스 모델, 시리얼라이저 및 백그라운드 작업을 구현할 수 있는 에이전트의 능력을 부여하여 인쇄 온デ맨드 판매자를 위한 강력한 백엔드 인프라를 촉진합니다. django-rq를 사용하여 효율적인 작업 처리를 수행하며 기존 Django 애플리케이션과 무결점으로 통합됩니다. 이 스킬을 사용함으로써 에이전트는 백엔드 개발을 간소화하여 전체 시스템의 성능과 신뢰성을 향상시킬 수 있습니다.

최적의 용도

Django LTS, DRF 및 django-allauth를 사용하여 고급 백엔드 개발 기능이 필요한 Python 기반 AI 에이전트에 적합합니다.

실행 가능한 사용 사례 for backend

Django Rest Framework를 사용하여 RESTful API를 구현하는 것
전자상거래 애플리케이션을 위한 데이터베이스 모델을 디자인하는 것
django-allauth를 사용하여 인증 시스템을 통합하는 것

! 보안 및 제한 사항

  • Django LTS 및 DRF 전문 지식이 필요함
  • Python 기반 AI 에이전트로 제한됨
  • django-allauth 및 django-rq 라이브러리에 의존함

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 supporting source material from the upstream repository. Use the Killer-Skills review above as the primary decision layer.

Labs Demo

Browser Sandbox Environment

⚡️ Ready to unleash?

Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

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?

Django LTS, DRF 및 django-allauth를 사용하여 고급 백엔드 개발 기능이 필요한 Python 기반 AI 에이전트에 적합합니다. 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: Django Rest Framework를 사용하여 RESTful API를 구현하는 것, 전자상거래 애플리케이션을 위한 데이터베이스 모델을 디자인하는 것, django-allauth를 사용하여 인증 시스템을 통합하는 것.

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?

Django LTS 및 DRF 전문 지식이 필요함. Python 기반 AI 에이전트로 제한됨. django-allauth 및 django-rq 라이브러리에 의존함.

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.

Imported Repository Instructions

The section below is supporting source material from the upstream repository. Use the Killer-Skills review above as the primary decision layer.

Supporting Evidence

backend

Install backend, an AI agent skill for AI agent workflows and automation. Works with Claude Code, Cursor, and Windsurf with one-command setup.

SKILL.md
Readonly
Imported Repository Instructions
The section below is supporting source material from the upstream repository. Use the Killer-Skills review above as the primary decision layer.
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]

관련 스킬

Looking for an alternative to backend or another community skill for your workflow? Explore these related open-source skills.

모두 보기

openclaw-release-maintainer

Logo of openclaw
openclaw

Your own personal AI assistant. Any OS. Any Platform. The lobster way. 🦞

333.8k
0
인공지능

widget-generator

Logo of f
f

prompts.chat 피드 시스템을 위한 사용자 지정 가능한 위젯 플러그인을 생성합니다

149.6k
0
인공지능

flags

Logo of vercel
vercel

리액트 프레임워크

138.4k
0
브라우저

pr-review

Logo of pytorch
pytorch

파이썬에서 텐서와 동적 신경망 구현 및 강력한 GPU 가속 지원

98.6k
0
개발자