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alinaqi
python

Python development with ruff, mypy, pytest - TDD and type safety

Overview

Publisheralinaqi
Repositorymaggy
Skill namepython
Stars
707
Forks
57
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by alinaqi on GitHub. Read the source before you install it.

Installation

Install the Python AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/alinaqi/maggy/tree/main/skills/python
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/alinaqi/maggy.git /tmp/maggy
mkdir -p .claude/skills
cp -r /tmp/maggy/skills/python .claude/skills/python
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Python in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Python on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Python is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Python Skill


Type Hints

  • Use type hints on all function signatures
  • Use typing module for complex types
  • Run mypy --strict in CI
python
def process_user(user_id: int, options: dict[str, Any] | None = None) -> User:
    ...

Project Structure

project/
├── src/
│   └── package_name/
│       ├── __init__.py
│       ├── core/           # Pure business logic
│       │   ├── __init__.py
│       │   ├── models.py   # Pydantic models / dataclasses
│       │   └── services.py # Pure functions
│       ├── infra/          # Side effects
│       │   ├── __init__.py
│       │   ├── api.py      # FastAPI routes
│       │   └── db.py       # Database operations
│       └── utils/          # Shared utilities
├── tests/
│   ├── unit/
│   └── integration/
├── pyproject.toml
└── CLAUDE.md

Tooling (Required)

toml
# pyproject.toml
[tool.ruff]
line-length = 100
select = ["E", "F", "I", "N", "W", "UP"]

[tool.mypy]
strict = true

[tool.pytest.ini_options]
testpaths = ["tests"]
addopts = "--cov=src --cov-report=term-missing --cov-fail-under=80"

Testing with Pytest

python
# tests/unit/test_services.py
import pytest
from package_name.core.services import calculate_total

class TestCalculateTotal:
    def test_returns_sum_of_items(self):
        # Arrange
        items = [{"price": 10}, {"price": 20}]
        
        # Act
        result = calculate_total(items)
        
        # Assert
        assert result == 30

    def test_returns_zero_for_empty_list(self):
        assert calculate_total([]) == 0

    def test_raises_on_invalid_item(self):
        with pytest.raises(ValueError):
            calculate_total([{"invalid": "item"}])

GitHub Actions

yaml
name: Python Quality Gate

on: [push, pull_request]

jobs:
  quality:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      
      - name: Setup Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.12'
          
      - name: Install dependencies
        run: |
          pip install -e ".[dev]"
          
      - name: Lint (Ruff)
        run: ruff check .
        
      - name: Format Check (Ruff)
        run: ruff format --check .
        
      - name: Type Check (mypy)
        run: mypy src/
        
      - name: Test with Coverage
        run: pytest

Pre-Commit Hooks

yaml
# .pre-commit-config.yaml
repos:
  - repo: https://github.com/astral-sh/ruff-pre-commit
    rev: v0.8.0
    hooks:
      - id: ruff
        args: [--fix]
      - id: ruff-format

  - repo: https://github.com/pre-commit/mirrors-mypy
    rev: v1.13.0
    hooks:
      - id: mypy
        additional_dependencies: [pydantic]
        args: [--strict]

  - repo: local
    hooks:
      - id: pytest
        name: pytest
        entry: pytest tests/unit -x --tb=short
        language: system
        pass_filenames: false
        always_run: true

Install and setup:

bash
pip install pre-commit
pre-commit install

Patterns

Pydantic for Data Validation

python
from pydantic import BaseModel, Field

class CreateUserRequest(BaseModel):
    email: str = Field(..., min_length=5)
    name: str = Field(..., max_length=100)

Dependency Injection

python
# Don't import dependencies directly in business logic
# Pass them in

# Bad
from .db import database
def get_user(user_id: int) -> User:
    return database.fetch(user_id)

# Good
def get_user(user_id: int, db: Database) -> User:
    return db.fetch(user_id)

Result Pattern (No Exceptions in Core)

python
from dataclasses import dataclass

@dataclass
class Result[T]:
    value: T | None
    error: str | None
    
    @property
    def is_ok(self) -> bool:
        return self.error is None

Python Anti-Patterns

  • ❌ from module import *
  • ❌ Mutable default arguments
  • ❌ Bare except: clauses
  • ❌ Using type: ignore without explanation
  • ❌ Global variables for state
  • ❌ Classes when functions suffice

Frequently asked questions

What does the Python AI skill do?

Python development with ruff, mypy, pytest - TDD and type safety

Why use Python on TypingMind?

Because you install it once and use it with any model. Python is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Python in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/alinaqi/maggy/tree/main/skills/python. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Python?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Python?

As many as you like. As long as a model supports skills, you can use Python with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Python AI skill free?

Yes. It is published on GitHub by alinaqi under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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