python-expert
Python gotchas and decision criteria. Covers async pitfalls, FastAPI/Django patterns, and type hint traps.
Best use case
python-expert is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Python gotchas and decision criteria. Covers async pitfalls, FastAPI/Django patterns, and type hint traps.
Teams using python-expert 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-expert/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How python-expert Compares
| Feature / Agent | python-expert | 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 gotchas and decision criteria. Covers async pitfalls, FastAPI/Django patterns, and type hint traps.
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.
SKILL.md Source
> **AI-consumed reference.** Optimized for Claude to read during execution.
> Human-readable explanation: see [docs/architecture/HIERARCHICAL_PLANNING.md](../../../docs/architecture/HIERARCHICAL_PLANNING.md)
> or [docs/getting-started/](../../../docs/getting-started/) depending on topic.
# Python Expert — Gotchas & Decisions
Use Context7 for FastAPI/Django/Flask docs.
## Key Decisions
```toon
decisions[4]{choice,use_when}:
FastAPI vs Django vs Flask,"FastAPI: async APIs + auto-docs. Django: full-featured + ORM + admin. Flask: minimal/micro"
Pydantic vs dataclass,"Pydantic for validation/serialization (API boundaries). dataclass for internal structs"
SQLAlchemy vs Django ORM,"SQLAlchemy: standalone/FastAPI. Django ORM: Django projects only"
sync vs async,"async for I/O-bound (HTTP/DB). sync for CPU-bound. Don't mix blocking calls in async"
```
## Gotchas
- Mutable default args: `def f(items=[])` shares list across calls — use `def f(items=None): items = items or []`
- `asyncio.run()` creates new event loop — can't nest. Use `await` inside existing async context
- FastAPI `Depends()`: new instance per request by default. Use `@lru_cache` for singletons
- Django N+1: use `select_related` (FK/OneToOne) and `prefetch_related` (M2M/reverse FK)
- `isinstance(x, int)` catches `bool` too — `bool` is subclass of `int`. Check `type(x) is int` if needed
- Type hints are NOT enforced at runtime — use Pydantic or `beartype` for runtime validation
- `dict.get('key')` returns `None` silently — use `dict['key']` when key must exist
- `requirements.txt` vs `pyproject.toml`: prefer pyproject.toml (PEP 621) for modern projects
- `with` statement for resource cleanup (files, DB connections) — never rely on `__del__`Related Skills
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