examples

taskx Examples

This directory contains example projects demonstrating various use cases for taskx.

Examples

1. Simple Python Project (simple-python/)

Demonstrates basic taskx usage for a Python library or CLI tool.

Features:

• Code formatting with black
• Linting with ruff
• Type checking with mypy
• Testing with pytest
• Parallel quality checks
• Watch mode for TDD

Try it:

cd simple-python
taskx list           # See all available tasks
taskx check          # Run all checks in parallel
taskx dev            # Watch and run tests on file changes
taskx graph          # Visualize task dependencies

2. Flask Web Application (flask-app/)

Demonstrates taskx for web development workflows.

Features:

• Development server with auto-reload
• Database migration management
• Docker build and deployment
• Environment-based deployment targets
• Security scanning
• Parallel build checks

Try it:

cd flask-app
taskx dev                    # Start development server with watch mode
taskx db-init && taskx db-migrate  # Database setup
taskx docker-run            # Run in container
taskx deploy-staging        # Deploy to staging (after build checks)

3. Data Science Project (data-science/)

Demonstrates taskx for ML/data science workflows.

Features:

• Complete ML pipeline with dependencies
• Parallel hyperparameter tuning
• GPU training support
• Jupyter notebook integration
• MLflow tracking
• Data validation

Try it:

cd data-science
taskx pipeline              # Run complete ML pipeline (download → train → evaluate)
taskx tune                  # Run parallel hyperparameter tuning
taskx notebook              # Start Jupyter Lab
taskx graph --format mermaid > pipeline.mmd  # Export pipeline diagram

4. Cryptocurrency Price Tracker (crypto-tracker/)

Real-world example demonstrating a complete data processing pipeline with live API integration.

Features:

• Fetch live cryptocurrency prices from Coinbase API
• Data validation and quality checks
• Statistical analysis and reporting
• HTML report generation
• Parallel quality checks
• Watch mode for development
• Virtual environment setup

What it does:

1. Fetches real-time prices for BTC, ETH, SOL, ADA, DOT
2. Validates data integrity
3. Computes statistics (total value, average, min, max)
4. Generates beautiful HTML reports

Try it:

cd crypto-tracker

# Setup virtual environment
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

# Run complete pipeline
taskx run pipeline

# Run individual tasks
taskx run fetch           # Fetch live prices
taskx run validate        # Validate data quality
taskx run analyze         # Compute statistics
taskx run report          # Generate HTML report

# View the report
taskx run view            # Opens report in browser

# Watch mode for development
taskx watch dev

# See task dependencies
taskx graph --task view

Learning highlights:

• Real API integration with error handling
• Multi-stage data pipeline with dependencies
• Data validation patterns
• HTML report generation
• Production-ready project structure

Common Patterns

Parallel Execution

Run multiple commands concurrently:

[tool.taskx.tasks]
check = {
    parallel = ["ruff check .", "mypy .", "pytest -q"],
    description = "Run all checks in parallel"
}

Watch Mode

Auto-restart tasks on file changes:

[tool.taskx.tasks]
dev = {
    cmd = "python app.py",
    watch = ["*.py", "templates/**/*"],
    description = "Dev server with auto-reload"
}

Task Dependencies

Chain tasks with automatic ordering:

[tool.taskx.tasks]
test = { cmd = "pytest tests/" }
build = { cmd = "python -m build" }
deploy = {
    depends = ["test", "build"],  # Runs test → build → deploy
    cmd = "sh scripts/deploy.sh"
}

Environment Variables

Use environment variables for configuration:

[tool.taskx.env]
APP_NAME = "myapp"
PORT = "8000"

[tool.taskx.tasks]
dev = { cmd = "uvicorn ${APP_NAME}:app --port ${PORT} --reload" }

Learning Path

1. Start with simple-python to learn basic taskx concepts
2. Try crypto-tracker for a real-world data processing example
3. Move to flask-app to see how taskx handles web development
4. Explore data-science for complex ML dependency pipelines

Tips

• Use taskx graph to visualize dependencies
• Use taskx watch <task> for rapid development
• Set parallel for independent tasks to speed up workflows
• Use environment variables for configuration
• Add descriptions to make taskx list more helpful

Creating Your Own

To use taskx in your project:

# Initialize configuration
taskx init

# Edit pyproject.toml to add your tasks
# Then run with:
taskx <task-name>

See the main README for full documentation.