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Python Project Creator

Community
haddock-development
python-project-creator

Creates Python projects with proper structure, virtual environments, and dependency management. Use when users request to create a new Python project, set up a Python development environment, or initialize a Python application with standard tooling.

Overview

Publisherhaddock-development
Repositoryclaude-reflect-system
Skill namepython-project-creator
Stars
383
Forks
44
Bundled files
3
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.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by haddock-development on GitHub. Read the source before you install it.

Installation

Install the Python Project Creator 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/haddock-development/claude-reflect-system/tree/master/python-project-creator
  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/haddock-development/claude-reflect-system.git /tmp/claude-reflect-system
mkdir -p .claude/skills
cp -r /tmp/claude-reflect-system/python-project-creator .claude/skills/python-project-creator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Python Project Creator 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 Project Creator 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 Project Creator 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 Project Creator

Critical Corrections

Use 'uv' instead of 'pip'

  • ✗ Don't: pip install, pip freeze
  • ✓ Do: uv pip install, uv pip freeze (uv is faster and more modern)

Always use pytest, never unittest

  • ✗ Don't: unittest framework
  • ✓ Do: pytest for all testing needs

Overview

This skill creates well-structured Python projects with best practices for dependency management, testing, and code organization. It sets up virtual environments, installs dependencies, and configures common development tools.

Project Creation Workflow

1. Understand Requirements

Ask the user about:

  • Project name and purpose
  • Type: CLI tool, web app, library, data science project
  • Dependencies: Required packages
  • Testing: Testing framework preference (pytest, unittest)

2. Create Project Structure

Standard Python project structure:

project-name/
├── src/
│   └── project_name/
│       ├── __init__.py
│       └── main.py
├── tests/
│   ├── __init__.py
│   └── test_main.py
├── .gitignore
├── README.md
├── requirements.txt
└── setup.py (optional, for libraries)

3. Virtual Environment Setup

Create and activate virtual environment:

bash
# Create virtual environment
python3 -m venv venv

# Activate (instructions for user)
# macOS/Linux: source venv/bin/activate
# Windows: venv\Scripts\activate

4. Install Dependencies

Install packages using uv:

bash
uv pip install <package-name>
uv pip freeze > requirements.txt

For development dependencies:

bash
uv pip install pytest black flake8 mypy

5. Initialize Git

bash
git init
git add .
git commit -m "Initial commit: project setup"

Project Types

CLI Application

  • Use argparse or click for command-line arguments
  • Include main.py with proper entry point
  • Add if __name__ == "__main__": guard

Web Application

  • Flask: Lightweight, good for small APIs
  • FastAPI: Modern, async, auto-documentation
  • Django: Full-featured, batteries included

Library/Package

  • Include setup.py for packaging
  • Follow semantic versioning
  • Add comprehensive docstrings

Data Science

  • Include notebooks/ directory for Jupyter notebooks
  • Add data/ directory (with .gitignore)
  • Common packages: pandas, numpy, matplotlib, scikit-learn

Testing Setup

pytest (Required)

Always use pytest for testing:

bash
uv pip install pytest pytest-cov

Example test file:

python
# tests/test_main.py
import pytest
from src.project_name.main import my_function

def test_my_function():
    assert my_function(2, 3) == 5

Run tests:

bash
pytest
pytest --cov=src  # with coverage

Code Quality Tools

Black (Code Formatter)

bash
uv pip install black
black src/ tests/

Flake8 (Linter)

bash
uv pip install flake8
flake8 src/ tests/

mypy (Type Checker)

bash
uv pip install mypy
mypy src/

Common Patterns

Entry Point Pattern

python
# src/project_name/main.py

def main():
    """Main application entry point."""
    print("Hello, World!")

if __name__ == "__main__":
    main()

Configuration Pattern

python
# src/project_name/config.py

import os
from pathlib import Path

# Project root directory
PROJECT_ROOT = Path(__file__).parent.parent.parent

# Load environment variables
DEBUG = os.getenv("DEBUG", "False") == "True"

Error Handling Pattern

python
class ProjectError(Exception):
    """Base exception for this project."""
    pass

class ConfigError(ProjectError):
    """Configuration-related errors."""
    pass

.gitignore Template

# Virtual environment
venv/
env/
.venv/

# Python
__pycache__/
*.py[cod]
*$py.class
*.so
.Python
*.egg-info/
dist/
build/

# IDE
.vscode/
.idea/
*.swp
*.swo

# Environment
.env
.env.local

# Testing
.pytest_cache/
.coverage
htmlcov/

# OS
.DS_Store
Thumbs.db

Best Practices

Dependency Management

  • Pin exact versions in production: package==1.2.3
  • Use ranges for libraries: package>=1.2,<2.0
  • Separate dev dependencies from production
  • Keep requirements.txt minimal

Project Structure

  • Use src/ layout to avoid import issues
  • Keep tests separate from source code
  • One module per file, clear naming
  • Flat is better than nested (within reason)

Documentation

  • Write clear README.md with setup instructions
  • Add docstrings to all public functions/classes
  • Include usage examples in README
  • Document environment variables

Version Control

  • Initialize git from the start
  • Write meaningful commit messages
  • Create .gitignore before first commit
  • Never commit secrets or credentials

Quick Start Examples

Minimal CLI Tool

bash
mkdir my-cli-tool && cd my-cli-tool
python3 -m venv venv
source venv/bin/activate
uv pip install click
# Create main.py, tests, etc.

FastAPI Web Service

bash
mkdir my-api && cd my-api
python3 -m venv venv
source venv/bin/activate
uv pip install fastapi uvicorn
# Create app structure

Data Science Project

bash
mkdir my-analysis && cd my-analysis
python3 -m venv venv
source venv/bin/activate
uv pip install pandas numpy matplotlib jupyter
# Create notebooks/, data/, src/

Resources

This skill includes examples in the bundled directories:

scripts/

  • example.py - Template Python script with best practices

references/

  • api_reference.md - Common library documentation references

assets/

  • Project templates and boilerplate code

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Python Project Creator AI skill do?

Creates Python projects with proper structure, virtual environments, and dependency management. Use when users request to create a new Python project, set up a Python development environment, or initialize a Python application with standard tooling.

Why use Python Project Creator on TypingMind?

Because you install it once and use it with any model. Python Project Creator 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 Project Creator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/haddock-development/claude-reflect-system/tree/master/python-project-creator. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Python Project Creator?

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 Project Creator?

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

Is the Python Project Creator AI skill free?

It is published on GitHub by haddock-development. Check the repository for licensing terms. 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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