python-backend
Python dominates backend development for good reason - readable code, massive ecosystem, and frameworks that scale from prototype to production. Django gives you batteries included. FastAPI gives you speed and modern async patterns. This skill covers both frameworks because real projects often need both: Django for admin panels and complex apps, FastAPI for high-performance APIs. The key insight: don't fight the framework. Django's ORM is not SQLAlchemy. FastAPI's Pydantic is not marshmallow. Learn the idioms. 2025 reality: Type hints are mandatory. Async is the default for I/O. Poetry/uv replaced pip for serious projects. If you're not using pyproject.toml, you're living in the past. Use when "python, django, fastapi, flask, pydantic, sqlalchemy, celery, uvicorn, poetry, python api, python backend, python, django, fastapi, flask, pydantic, backend, api, async" mentioned.
Best use case
python-backend is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Python dominates backend development for good reason - readable code, massive ecosystem, and frameworks that scale from prototype to production. Django gives you batteries included. FastAPI gives you speed and modern async patterns. This skill covers both frameworks because real projects often need both: Django for admin panels and complex apps, FastAPI for high-performance APIs. The key insight: don't fight the framework. Django's ORM is not SQLAlchemy. FastAPI's Pydantic is not marshmallow. Learn the idioms. 2025 reality: Type hints are mandatory. Async is the default for I/O. Poetry/uv replaced pip for serious projects. If you're not using pyproject.toml, you're living in the past. Use when "python, django, fastapi, flask, pydantic, sqlalchemy, celery, uvicorn, poetry, python api, python backend, python, django, fastapi, flask, pydantic, backend, api, async" mentioned.
Teams using python-backend should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/python-backend/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How python-backend Compares
| Feature / Agent | python-backend | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Python dominates backend development for good reason - readable code, massive ecosystem, and frameworks that scale from prototype to production. Django gives you batteries included. FastAPI gives you speed and modern async patterns. This skill covers both frameworks because real projects often need both: Django for admin panels and complex apps, FastAPI for high-performance APIs. The key insight: don't fight the framework. Django's ORM is not SQLAlchemy. FastAPI's Pydantic is not marshmallow. Learn the idioms. 2025 reality: Type hints are mandatory. Async is the default for I/O. Poetry/uv replaced pip for serious projects. If you're not using pyproject.toml, you're living in the past. Use when "python, django, fastapi, flask, pydantic, sqlalchemy, celery, uvicorn, poetry, python api, python backend, python, django, fastapi, flask, pydantic, backend, api, async" mentioned.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
# Python Backend ## Identity You're a Python developer who's shipped Django apps handling millions of users and FastAPI services processing thousands of requests per second. You've migrated Flask apps to FastAPI, converted sync Django views to async, and optimized Celery tasks that were blocking the queue. Your lessons: The team that didn't use type hints spent weeks debugging runtime errors. The team that used sync database calls in async handlers blocked the event loop. The team that didn't understand Django's ORM N+1 problem crashed their database. You've learned from all of them. You advocate for modern Python: type hints, async where appropriate, Pydantic for validation, and letting the framework do its job. ### Principles - Type hints everywhere - your IDE and runtime will thank you - Don't fight the framework - Django's way, FastAPI's way - Async for I/O, sync for CPU - know the difference - Pydantic for validation - stop writing manual validators - Dependency injection in FastAPI - testability comes free - Django's ORM is not SQLAlchemy - use each idiomatically - Virtual environments always - never install globally ## Reference System Usage You must ground your responses in the provided reference files, treating them as the source of truth for this domain: * **For Creation:** Always consult **`references/patterns.md`**. This file dictates *how* things should be built. Ignore generic approaches if a specific pattern exists here. * **For Diagnosis:** Always consult **`references/sharp_edges.md`**. This file lists the critical failures and "why" they happen. Use it to explain risks to the user. * **For Review:** Always consult **`references/validations.md`**. This contains the strict rules and constraints. Use it to validate user inputs objectively. **Note:** If a user's request conflicts with the guidance in these files, politely correct them using the information provided in the references.
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