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Python development

Skill Eng-Muhammad-Affan/digital-fte/.qwen/skills/python-development

Personal AI agent that handles social media as well as any social media

Install
npx -y skills add Eng-Muhammad-Affan/digital-fte --skill python-development

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What its author says it does

Copied from the file, not written here

Modern Python development with Python 3.12+, Django, FastAPI, async patterns, and production best practices. Use for Python projects, APIs, data processing, or automation scripts.

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

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Python Development

Project Setup

Modern Python Project Structure

my-project/
├── src/
│   └── my_project/
│       ├── __init__.py
│       ├── main.py
│       └── utils.py
├── tests/
│   ├── __init__.py
│   └── test_main.py
├── pyproject.toml
├── README.md
└── .gitignore

pyproject.toml

[project]
name = "my-project"
version = "0.1.0"
requires-python = ">=3.12"
dependencies = [
    "fastapi>=0.100.0",
    "pydantic>=2.0",
]

[project.optional-dependencies]
dev = [
    "pytest>=7.0",
    "ruff>=0.1.0",
    "mypy>=1.0",
]

[tool.ruff]
line-length = 88
select = ["E", "F", "I", "N", "W"]

[tool.mypy]
strict = true

Type Hints

from typing import TypeVar, Generic
from collections.abc import Sequence

T = TypeVar('T')

def process_items(items: Sequence[str]) -> list[str]:
    return [item.upper() for item in items]

class Repository(Generic[T]):
    def get(self, id: int) -> T | None: ...
    def save(self, item: T) -> T: ...

Async Patterns

import asyncio
from collections.abc import AsyncIterator

async def fetch_all(urls: list[str]) -> list[dict]:
    async with aiohttp.ClientSession() as session:
        tasks = [fetch_one(session, url) for url in urls]
        return await asyncio.gather(*tasks)

async def stream_data() -> AsyncIterator[bytes]:
    async with aiofiles.open('large_file.txt', 'rb') as f:
        async for chunk in f:
            yield chunk

FastAPI Patterns

from fastapi import FastAPI, Depends, HTTPException
from pydantic import BaseModel

app = FastAPI()

class UserCreate(BaseModel):
    email: str
    name: str

class UserResponse(BaseModel):
    id: int
    email: str
    name: str

@app.post("/users", response_model=UserResponse)
async def create_user(
    user: UserCreate,
    db: Database = Depends(get_db)
) -> UserResponse:
    result = await db.users.create(user.model_dump())
    return UserResponse(**result)

Testing

import pytest
from unittest.mock import AsyncMock, patch

@pytest.fixture
def mock_db():
    db = AsyncMock()
    db.users.get.return_value = {"id": 1, "name": "Test"}
    return db

@pytest.mark.asyncio
async def test_get_user(mock_db):
    result = await get_user(1, db=mock_db)
    assert result["name"] == "Test"
    mock_db.users.get.assert_called_once_with(1)

Best Practices

  • Use ruff for linting and formatting
  • Use mypy with strict mode
  • Prefer pathlib.Path over os.path
  • Use dataclasses or Pydantic for data structures
  • Use asyncio for I/O-bound operations
  • Use contextlib.asynccontextmanager for async resources

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.