Skills
skills/a-green-hand-jack/ml-research-skills/init-python-project

init-python-project

SKILL.md

Initialize Python Project (New or Fork)

You are helping the user create a production-ready Python project structure or enhance an existing project with best practices.

Project Types

This command supports two scenarios:

  1. New Project: Create a new Python project from scratch
  2. Fork/Enhancement: Clone an existing project and enhance it with missing components

Standard Project Structure

<project-name>/
├── src/
│   └── <package_name>/
│       ├── __init__.py
│       ├── models/
│       ├── data/
│       ├── utils/
│       └── ...
├── tests/
│   ├── __init__.py
│   ├── data/              # Test-specific data (isolated)
│   ├── outputs/           # Test outputs (isolated)
│   ├── conftest.py        # pytest configuration
│   ├── models/
│   │   └── test_layers.py
│   └── ...                # Mirror src/ structure
├── docs/
│   ├── README.md
│   ├── outlines/          # Project roadmap and progress
│   │   ├── project_plan.md
│   │   ├── milestones.md
│   │   └── progress.md
│   ├── dev/               # Feature development tracking
│   │   ├── feature_template.md
│   │   └── features/
│   └── src/               # Module documentation (mirrors src/)
│       ├── models.md
│       ├── data.md
│       └── dependencies.md
├── data/                  # Project data
│   ├── raw/
│   ├── processed/
│   └── README.md
├── outputs/               # Model outputs, results
│   ├── models/
│   ├── predictions/
│   └── logs/
├── scripts/               # Executable scripts
│   ├── train.py
│   ├── predict.py
│   ├── download_data.py
│   └── download_weights.py
├── experiments/           # Experiment tracking
│   └── README.md
├── .vscode/              # VSCode settings
│   └── settings.json
├── .cursor/              # Cursor settings
│   └── settings.json
├── .claude/              # Claude Code settings
│   └── commands/
├── .env                  # Environment variables (not committed)
├── .env.example          # Example environment variables
├── .gitignore
├── pyproject.toml        # UV project configuration
├── README.md
└── CLAUDE.md             # Instructions for Claude Code

Workflow

Step 1: Ask User for Project Information

Ask the user:

  1. Project type:

    • new - Create new project from scratch
    • fork - Clone and enhance existing project

  2. If new project:

    • Project name (e.g., my-ml-project)
    • Package name (e.g., my_ml_project, defaults to snake_case of project name)
    • Short description
    • Python version (default: 3.11)
    • Project type: ml (machine learning), web (web app), lib (library), general

  3. If fork project:

    • GitHub repository URL (SSH format)
    • Do you want to create a fork on GitHub or just clone?

  4. For both:

    • GitHub repository URL for this project (can skip and add later)
    • Author name and email

Step 2A: Create New Project

2A.1: Initialize UV Project

# Create project directory
mkdir <project-name>
cd <project-name>

# Initialize uv project
uv init --name <package_name> --python <version>

# Create src structure
mkdir -p src/<package_name>/{models,data,utils}
touch src/<package_name>/__init__.py
touch src/<package_name>/models/__init__.py
touch src/<package_name>/data/__init__.py
touch src/<package_name>/utils/__init__.py

2A.2: Create Directory Structure

# Tests with isolated data/outputs
mkdir -p tests/{data,outputs,models,data_tests,utils}
touch tests/__init__.py
touch tests/conftest.py

# Documentation structure
mkdir -p docs/{outlines,dev/features,src}

# Data directories
mkdir -p data/{raw,processed}

# Output directories
mkdir -p outputs/{models,predictions,logs}

# Scripts
mkdir scripts

# Experiments
mkdir experiments

# IDE configurations
mkdir -p .vscode .cursor .claude/commands

# Environment file
touch .env .env.example

2A.3: Configure pyproject.toml for Editable Install

Edit pyproject.toml to add:

[project]
name = "<package_name>"
version = "0.1.0"
description = "<description>"
authors = [
    {name = "<author_name>", email = "<author_email>"}
]
readme = "README.md"
requires-python = ">= <version>"
dependencies = [
    # Add based on project type
]

[project.optional-dependencies]
dev = [
    "pytest>=7.4.0",
    "pytest-cov>=4.1.0",
    "black>=23.0.0",
    "ruff>=0.1.0",
    "mypy>=1.5.0",
]

# For ML projects, add:
ml = [
    "torch>=2.0.0",
    "numpy>=1.24.0",
    "pandas>=2.0.0",
    "scikit-learn>=1.3.0",
    "matplotlib>=3.7.0",
    "seaborn>=0.12.0",
    "tqdm>=4.65.0",
]

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

[tool.pytest.ini_options]
testpaths = ["tests"]
python_files = "test_*.py"
python_functions = "test_*"
addopts = "-v --cov=src/<package_name> --cov-report=html --cov-report=term"

[tool.black]
line-length = 100
target-version = ['py311']

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

[tool.mypy]
python_version = "<version>"
warn_return_any = true
warn_unused_configs = true
disallow_untyped_defs = true

2A.4: Install Package in Editable Mode

# Install package in editable mode with dev dependencies
uv pip install -e ".[dev]"

# For ML projects:
uv pip install -e ".[dev,ml]"

Step 2B: Fork and Enhance Existing Project

2B.1: Clone Repository

# Clone the repository
git clone <github-ssh-url> <project-name>
cd <project-name>

# If forking, set up upstream
git remote add upstream <original-repo-url>

2B.2: Analyze Existing Structure

Check what already exists:

# List current structure
ls -la

# Check for pyproject.toml, setup.py, requirements.txt
# Check existing directories

2B.3: Identify Missing Components

Compare existing structure with standard structure and identify missing:

  • Documentation directories (docs/outlines, docs/dev, docs/src)
  • Test structure (tests/data, tests/outputs)
  • Scripts directory
  • Configuration files (.env.example, CLAUDE.md)
  • IDE configurations

2B.4: Add Missing Components

Create only the missing directories and files:

# Example: If docs/outlines is missing
mkdir -p docs/outlines

# If tests/data is missing
mkdir -p tests/{data,outputs}

# If .env.example is missing
touch .env.example

2B.5: Enhance pyproject.toml

If project uses requirements.txt, offer to migrate to pyproject.toml:

# Install uv if not present
# Create pyproject.toml from requirements.txt
uv init

# Install existing requirements
uv pip install -r requirements.txt

# Add to pyproject.toml

If pyproject.toml exists but lacks editable install config, add it.

Step 3: Create Template Files

3.1: .gitignore

# Python
__pycache__/
*.py[cod]
*$py.class
*.so
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST

# Virtual Environment
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
.uv/

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

# Testing
.pytest_cache/
.coverage
htmlcov/
.tox/
.hypothesis/

# Data and outputs (optional - adjust based on needs)
data/raw/*
!data/raw/.gitkeep
data/processed/*
!data/processed/.gitkeep
outputs/*
!outputs/.gitkeep

# Experiments
experiments/*
!experiments/README.md

# Logs
*.log
logs/

# OS
.DS_Store
Thumbs.db

# Environment variables
.env

# Model checkpoints (optional)
*.pth
*.ckpt
*.pt
outputs/models/*

3.2: .env.example

# HuggingFace
HF_HOME=/path/to/huggingface/cache
HF_TOKEN=your_hf_token_here

# OpenAI (if needed)
OPENAI_API_KEY=your_openai_key_here

# Weights & Biases (if needed)
WANDB_API_KEY=your_wandb_key_here

# Project specific
PROJECT_ROOT=/path/to/project
DATA_DIR=${PROJECT_ROOT}/data
OUTPUTS_DIR=${PROJECT_ROOT}/outputs

# Model paths
PRETRAINED_MODEL_PATH=/path/to/pretrained/models

3.3: .env (create empty, user will fill)

# Copy from .env.example and fill in actual values
# This file is not committed to git

3.4: README.md

# <Project Name>

<Short description>

## Installation

### Prerequisites
- Python <version>
- uv (recommended) or pip

### Setup

1. Clone the repository:
```bash
git clone <repo-url>
cd <project-name>
  1. Install dependencies:
# Using uv (recommended)
uv sync

# Install in editable mode
uv pip install -e ".[dev]"

# For ML projects
uv pip install -e ".[dev,ml]"
  1. Set up environment variables:
cp .env.example .env
# Edit .env with your actual values

Project Structure

  • src/<package_name>/ - Main source code
    • models/ - Model definitions
    • data/ - Data loading and processing
    • utils/ - Utility functions
  • tests/ - Unit tests (mirrors src/ structure)
  • docs/ - Documentation
    • outlines/ - Project roadmap and progress
    • dev/ - Feature development tracking
    • src/ - Module documentation
  • data/ - Project data
  • outputs/ - Model outputs and results
  • scripts/ - Executable scripts
  • experiments/ - Experiment tracking

Usage

Training

python scripts/train.py

Testing

pytest tests/

Development

Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=src/<package_name>

# Run specific test file
pytest tests/models/test_layers.py

Code Quality

# Format code
black src/ tests/

# Lint code
ruff check src/ tests/

# Type checking
mypy src/

Documentation

See docs/ for detailed documentation:

  • docs/outlines/ - Project planning and milestones
  • docs/dev/ - Feature development tracking
  • docs/src/ - Module documentation and dependencies

Contributing

  1. Create a new branch for your feature
  2. Write tests for your changes
  3. Update documentation in docs/
  4. Submit a pull request

License

[Specify license]

Authors

  • <>

#### 3.5: CLAUDE.md

```markdown
# Claude Code Instructions for <Project Name>

## Project Overview

<Brief description of the project>

## Development Principles

1. **Test-Driven Development**
   - Every module in `src/` must have corresponding tests in `tests/`
   - Tests use isolated `tests/data/` and `tests/outputs/` directories
   - Run tests before committing: `pytest`

2. **Documentation-First**
   - Document new features in `docs/dev/features/`
   - Update module docs in `docs/src/` when changing code
   - Track dependencies between modules

3. **Code Quality**
   - Format code with `black` before committing
   - Check with `ruff` for linting
   - Use type hints and check with `mypy`

4. **Environment Management**
   - All dependencies via `pyproject.toml`
   - Package installed in editable mode (`uv pip install -e .`)
   - Use absolute imports (e.g., `from <package_name>.models import ...`)

## Project Structure Rules

### Source Code (`src/`)
- Organized by functionality (models, data, utils)
- Each module has clear responsibility
- Document in corresponding `docs/src/<module>.md`

### Tests (`tests/`)
- **IMPORTANT**: Mirror `src/` structure exactly
- Example: `src/<package_name>/models/layers.py` → `tests/models/test_layers.py`
- Use isolated `tests/data/` and `tests/outputs/`
- Never share data/outputs with main project

### Documentation (`docs/`)

#### `docs/outlines/`
- Project planning and roadmap
- Milestones and progress tracking
- High-level architecture decisions

#### `docs/dev/`
- Feature development tracking
- Each feature has its own markdown file
- Track: design, implementation, testing, status

#### `docs/src/`
- **IMPORTANT**: Mirror `src/` structure
- Document each module's:
  - Purpose and functionality
  - Dependencies on other modules
  - Public API
  - Usage examples

### Scripts (`scripts/`)
- Executable scripts for common tasks
- Use absolute imports from installed package
- Load environment variables from `.env`

## When Adding New Features

1. **Create feature doc**: `docs/dev/features/<feature_name>.md`
2. **Implement in src**: `src/<package_name>/<module>/<feature>.py`
3. **Write tests**: `tests/<module>/test_<feature>.py`
4. **Update module doc**: `docs/src/<module>.md`
5. **Update dependencies**: Document in `docs/src/dependencies.md`

## Common Tasks

### Adding a New Model

1. Create: `src/<package_name>/models/<model_name>.py`
2. Test: `tests/models/test_<model_name>.py`
3. Document: `docs/src/models.md`
4. Feature track: `docs/dev/features/<model_name>.md`

### Adding a New Data Loader

1. Create: `src/<package_name>/data/<loader_name>.py`
2. Test: `tests/data_tests/test_<loader_name>.py`
   - Use `tests/data/` for test datasets
3. Document: `docs/src/data.md`

### Adding a New Script

1. Create: `scripts/<script_name>.py`
2. Use environment variables from `.env`
3. Import from installed package: `from <package_name> import ...`
4. Document usage in `README.md`

## Environment Variables

Always use `python-dotenv`:

```python
from dotenv import load_dotenv
import os

load_dotenv()

HF_HOME = os.getenv('HF_HOME')
HF_TOKEN = os.getenv('HF_TOKEN')

Testing Guidelines

  • Use pytest fixtures in tests/conftest.py
  • Isolated test data in tests/data/
  • Test outputs go to tests/outputs/
  • Aim for >80% code coverage
  • Run before committing: pytest --cov=src/<package_name>

Important Reminders

  • ✓ Always create tests for new code
  • ✓ Always update documentation
  • ✓ Use editable install (no relative imports)
  • ✓ Keep tests isolated from main data/outputs
  • ✓ Track feature development in docs/dev/
  • ✓ Document module dependencies

#### 3.6: tests/conftest.py

```python
"""Pytest configuration and fixtures."""
import pytest
from pathlib import Path
import shutil

# Test directories
TEST_DATA_DIR = Path(__file__).parent / "data"
TEST_OUTPUTS_DIR = Path(__file__).parent / "outputs"


@pytest.fixture(scope="session", autouse=True)
def setup_test_dirs():
    """Create test directories if they don't exist."""
    TEST_DATA_DIR.mkdir(exist_ok=True)
    TEST_OUTPUTS_DIR.mkdir(exist_ok=True)
    yield
    # Optional: Clean up test outputs after all tests
    # shutil.rmtree(TEST_OUTPUTS_DIR, ignore_errors=True)
    # TEST_OUTPUTS_DIR.mkdir(exist_ok=True)


@pytest.fixture
def test_data_dir():
    """Provide path to test data directory."""
    return TEST_DATA_DIR


@pytest.fixture
def test_outputs_dir():
    """Provide path to test outputs directory."""
    return TEST_OUTPUTS_DIR


@pytest.fixture(autouse=True)
def reset_test_outputs():
    """Clean test outputs before each test."""
    if TEST_OUTPUTS_DIR.exists():
        for item in TEST_OUTPUTS_DIR.iterdir():
            if item.is_file():
                item.unlink()
            elif item.is_dir():
                shutil.rmtree(item)
    yield

3.7: docs/outlines/project_plan.md

# Project Plan: <Project Name>

## Overview
<Brief project description and goals>

## Objectives
1. [Objective 1]
2. [Objective 2]
3. [Objective 3]

## Timeline

### Phase 1: Foundation (Weeks 1-2)
- [ ] Set up project structure
- [ ] Implement core data loading
- [ ] Set up testing framework

### Phase 2: Core Development (Weeks 3-6)
- [ ] Implement main models
- [ ] Add training pipeline
- [ ] Create evaluation metrics

### Phase 3: Refinement (Weeks 7-8)
- [ ] Optimize performance
- [ ] Add documentation
- [ ] Write examples

### Phase 4: Release (Week 9+)
- [ ] Final testing
- [ ] Prepare release
- [ ] Write publication/blog

## Architecture

### Core Components
1. **Data Module**: Data loading and preprocessing
2. **Models Module**: Model definitions and architectures
3. **Training Module**: Training loops and optimization
4. **Evaluation Module**: Metrics and evaluation

### Dependencies
See `docs/src/dependencies.md` for detailed module dependencies.

## Success Criteria
- [ ] All tests passing
- [ ] Code coverage > 80%
- [ ] Documentation complete
- [ ] Performance benchmarks met

3.8: docs/outlines/progress.md

# Project Progress

Last updated: [Date]

## Current Status
**Phase**: [Current phase]
**Progress**: [X]%

## Completed Milestones
- ✓ [Milestone 1] - [Date]
- ✓ [Milestone 2] - [Date]

## In Progress
- [ ] [Current task 1]
- [ ] [Current task 2]

## Upcoming
- [ ] [Next task 1]
- [ ] [Next task 2]

## Blockers
- [None / List blockers]

## Notes
[Any important notes or decisions]

3.9: docs/dev/feature_template.md

# Feature: [Feature Name]

**Status**: [Planning / In Progress / Testing / Complete]
**Started**: [Date]
**Completed**: [Date]

## Overview
Brief description of the feature and its purpose.

## Design
### Architecture
[How this feature fits into the overall architecture]

### Dependencies
- Depends on: [List modules this depends on]
- Used by: [List modules that will use this]

### API Design
```python
# Example usage
from <package_name>.module import FeatureName

feature = FeatureName(param1, param2)
result = feature.process(data)

Implementation

Files Created/Modified

  • src/<package_name>/module/feature.py - Main implementation
  • tests/module/test_feature.py - Unit tests
  • docs/src/module.md - Documentation updated

Key Components

Testing

Test Cases

  • Test basic functionality
  • Test edge cases
  • Test error handling
  • Test integration with other modules

Test Results

pytest tests/module/test_feature.py -v
# Results:

Coverage

pytest tests/module/test_feature.py --cov
# Coverage: XX%

Documentation

  • API documentation in docstrings
  • Module documentation updated (docs/src/module.md)
  • Dependencies documented (docs/src/dependencies.md)
  • Usage examples added

Performance

[Any performance considerations or benchmarks]

Future Improvements

  • [Improvement 1]
  • [Improvement 2]

Notes

[Any additional notes, decisions, or learnings]


#### 3.10: docs/src/dependencies.md

```markdown
# Module Dependencies

Visual representation of how modules depend on each other.

## Dependency Graph

utils (no dependencies) ↑ data (depends on: utils) ↑ models (depends on: utils) ↑ training (depends on: models, data, utils) ↑ evaluation (depends on: models, data, utils)


## Detailed Dependencies

### utils
- **Purpose**: Utility functions and helpers
- **Dependencies**: None (base module)
- **Used by**: data, models, training, evaluation
- **Documentation**: `docs/src/utils.md`

### data
- **Purpose**: Data loading and preprocessing
- **Dependencies**: utils
- **Used by**: models, training, evaluation
- **Documentation**: `docs/src/data.md`

### models
- **Purpose**: Model definitions and architectures
- **Dependencies**: utils
- **Used by**: training, evaluation
- **Documentation**: `docs/src/models.md`

### training
- **Purpose**: Training loops and optimization
- **Dependencies**: models, data, utils
- **Used by**: scripts
- **Documentation**: `docs/src/training.md`

### evaluation
- **Purpose**: Evaluation metrics and validation
- **Dependencies**: models, data, utils
- **Used by**: training, scripts
- **Documentation**: `docs/src/evaluation.md`

## Circular Dependencies

**IMPORTANT**: Avoid circular dependencies!

Current circular dependencies: [None / List if any]

## External Dependencies

See `pyproject.toml` for third-party package dependencies.

3.11: scripts/train.py

#!/usr/bin/env python3
"""Training script."""
from dotenv import load_dotenv
import os
from pathlib import Path

# Load environment variables
load_dotenv()

# Import from installed package (editable mode)
from <package_name>.data import load_data
from <package_name>.models import create_model
from <package_name>.utils import setup_logging

def main():
    """Main training function."""
    # Setup
    logger = setup_logging("train")

    # Get paths from environment
    data_dir = Path(os.getenv("DATA_DIR", "data"))
    outputs_dir = Path(os.getenv("OUTPUTS_DIR", "outputs"))

    logger.info("Starting training...")

    # Load data
    train_data = load_data(data_dir / "processed" / "train.csv")

    # Create model
    model = create_model()

    # Train
    # ... training code ...

    # Save model
    model_path = outputs_dir / "models" / "model.pth"
    model_path.parent.mkdir(parents=True, exist_ok=True)
    # torch.save(model.state_dict(), model_path)

    logger.info(f"Model saved to {model_path}")

if __name__ == "__main__":
    main()

3.12: scripts/download_data.py

#!/usr/bin/env python3
"""Download dataset."""
from dotenv import load_dotenv
import os
from pathlib import Path

load_dotenv()

def main():
    """Download dataset to data/raw/."""
    data_dir = Path(os.getenv("DATA_DIR", "data"))
    raw_dir = data_dir / "raw"
    raw_dir.mkdir(parents=True, exist_ok=True)

    print(f"Downloading data to {raw_dir}...")

    # Download logic here
    # Example: use huggingface datasets, kaggle API, etc.

    print("Download complete!")

if __name__ == "__main__":
    main()

3.13: .vscode/settings.json

{
  "python.defaultInterpreterPath": "${workspaceFolder}/.venv/bin/python",
  "python.testing.pytestEnabled": true,
  "python.testing.pytestArgs": ["tests"],
  "python.formatting.provider": "black",
  "python.linting.enabled": true,
  "python.linting.ruffEnabled": true,
  "editor.formatOnSave": true,
  "editor.codeActionsOnSave": {
    "source.organizeImports": true
  },
  "[python]": {
    "editor.defaultFormatter": "ms-python.black-formatter",
    "editor.formatOnSave": true,
    "editor.rulers": [100]
  },
  "files.exclude": {
    "**/__pycache__": true,
    "**/*.pyc": true,
    "**/.pytest_cache": true,
    "**/.mypy_cache": true,
    "**/.ruff_cache": true
  },
  "python.analysis.typeCheckingMode": "basic"
}

Step 4: Initialize Git Repository

# Initialize git
git init

# Create .gitkeep files for empty directories
touch data/raw/.gitkeep
touch data/processed/.gitkeep
touch outputs/.gitkeep
touch experiments/.gitkeep

# Add all files
git add .

# Create initial commit
git commit -m "Initial Python project structure

- Set up src, tests, docs, scripts structure
- Configure uv and pyproject.toml
- Add development tooling (pytest, black, ruff, mypy)
- Create documentation templates
- Set up environment configuration"

Step 5: Link to GitHub Repository

If user provided GitHub URL:

# Add remote
git remote add origin <github-ssh-url>

# Verify remote
git remote -v

# Ask user if they want to push
# If yes:
git branch -M main
git push -u origin main

If forked project:

# Already has remote 'origin' pointing to fork
# Add upstream if provided
git remote add upstream <original-repo-url>

# Fetch upstream
git fetch upstream

# Create development branch
git checkout -b dev

Step 6: Final Setup

# Install package in editable mode
uv pip install -e ".[dev]"

# Run initial test to verify setup
pytest tests/ -v

# If no tests yet, create a placeholder
echo "def test_placeholder():\n    assert True" > tests/test_placeholder.py
pytest tests/

Step 7: Display Summary

Show a complete summary:

✓ Python project initialized: <project-name>
✓ Directory structure created
✓ UV environment configured
✓ Package installed in editable mode: <package_name>
✓ Development tools configured:
  - pytest (testing)
  - black (formatting)
  - ruff (linting)
  - mypy (type checking)
✓ Documentation structure created:
  - docs/outlines/ (project planning)
  - docs/dev/ (feature tracking)
  - docs/src/ (module documentation)
✓ Git repository initialized
✓ GitHub remote configured: <url>

Project location: <full-path>

Next steps:
1. cd <project-name>
2. Edit .env with your environment variables (copy from .env.example)
3. Start developing in src/<package_name>/
4. Write tests in tests/ (mirror src/ structure)
5. Document modules in docs/src/
6. Track features in docs/dev/

Quick commands:
  # Run tests
  pytest

  # Run tests with coverage
  pytest --cov=src/<package_name>

  # Format code
  black src/ tests/

  # Lint code
  ruff check src/ tests/

  # Type check
  mypy src/

  # Install new package
  uv add <package-name>

Documentation:
  - Project plan: docs/outlines/project_plan.md
  - Progress tracking: docs/outlines/progress.md
  - Feature template: docs/dev/feature_template.md
  - Module dependencies: docs/src/dependencies.md

Remember:
  ✓ Every src/ module needs corresponding tests in tests/
  ✓ Tests use isolated tests/data/ and tests/outputs/
  ✓ Document modules in docs/src/
  ✓ Track features in docs/dev/
  ✓ Use absolute imports (package installed in editable mode)

Enhancement Mode (Fork/Existing Project)

When enhancing existing project:

Analysis Checklist

  1. Directory Structure:

    • Check if tests/ mirrors src/
    • Check for tests/data and tests/outputs isolation
    • Check for docs/outlines, docs/dev, docs/src

  2. Configuration:

    • Check for pyproject.toml vs requirements.txt
    • Check if package is installable
    • Check for .env.example

  3. Documentation:

    • Check for CLAUDE.md
    • Check for module documentation
    • Check for feature tracking

  4. Testing:

    • Check for pytest configuration
    • Check for conftest.py
    • Check test coverage

Enhancement Actions

Based on analysis, offer to:

  1. Add missing directories (docs/outlines, docs/dev/features, docs/src, tests/data, tests/outputs)
  2. Create missing config files (.env.example, CLAUDE.md, .vscode/settings.json)
  3. Migrate to pyproject.toml (if using requirements.txt)
  4. Add development tools (pytest, black, ruff, mypy)
  5. Create documentation templates (feature_template, dependencies)
  6. Set up editable install
  7. Create missing tests (identify untested modules)

Show user a report:

Analysis of <project-name>:

Existing structure:
✓ src/ directory
✓ Basic tests/
✗ docs/outlines/ - MISSING
✗ docs/dev/ - MISSING
✗ docs/src/ - MISSING
✗ tests/data/ - MISSING (tests may share main data/)
✗ tests/outputs/ - MISSING
✓ README.md
✗ CLAUDE.md - MISSING

Configuration:
✓ pyproject.toml exists
✓ Package installable
✗ .env.example - MISSING
✗ Development dependencies incomplete

Testing:
✓ pytest configured
✗ conftest.py - MISSING
⚠ Test coverage: XX% (low)

Recommendations:
1. Add isolated test directories (tests/data, tests/outputs)
2. Create documentation structure (docs/outlines, docs/dev, docs/src)
3. Add CLAUDE.md with project instructions
4. Create .env.example for environment variables
5. Add missing tests for untested modules
6. Document module dependencies

Would you like me to:
1. Add all missing components
2. Select specific components to add
3. Generate a checklist for manual addition

Error Handling

  • Directory already exists:

    • For new project: Ask to choose different name or cancel
    • For fork: Confirm before cloning into existing directory

  • Git already initialized:

    • Skip git init
    • Check for existing remote
    • Offer to add new remote with different name

  • Package name conflict:

    • Check if package name is already taken on PyPI
    • Suggest alternative names

  • UV not installed:

    • Provide installation instructions
    • Offer to use pip as fallback

  • GitHub SSH authentication fails:

    • Check SSH key setup
    • Suggest /setup-git-github command
    • Offer to continue without remote

  • Existing remote with same name:

    • Offer to use different remote name
    • Or update existing remote

Best Practices

  1. Always use editable install: uv pip install -e .
  2. Isolate test data: Never share data/outputs between tests and main project
  3. Mirror structure: tests/ should mirror src/ exactly
  4. Document everything:
    • Modules in docs/src/
    • Features in docs/dev/
    • Dependencies in docs/src/dependencies.md
  5. Track progress: Update docs/outlines/progress.md regularly
  6. Use environment variables: Never hardcode paths or keys
  7. Type hints: Use type hints for better IDE support and type checking
  8. Code quality: Format, lint, type-check before committing

Important Notes

  • Editable install: Enables absolute imports from anywhere
  • Test isolation: Critical for reproducible tests
  • Documentation structure: Mirrors code structure for easy navigation
  • Feature tracking: Essential for larger projects
  • Environment variables: Keep secrets out of code
  • Development tools: Enforce code quality automatically

Security Considerations

  • Never commit .env file
  • Always provide .env.example as template
  • Validate all file paths before creation
  • Don't automatically push without confirmation
  • Check for existing sensitive files before initializing git
Weekly Installs
3
Repository
a-green-hand-ja…h-skills
First Seen
6 days ago
Security Audits
Gen Agent Trust HubPass
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Installed on
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