mastering-python-skill

name	mastering-python-skill
description	Modern Python coaching covering language foundations through advanced production patterns. Use when asked to "write Python code", "explain Python concepts", "set up a Python project", "configure Poetry or PDM", "write pytest tests", "create a FastAPI endpoint", "run uvicorn server", "configure alembic migrations", "set up logging", "process data with pandas", or "debug Python errors". Triggers on "Python best practices", "type hints", "async Python", "packaging", "virtual environments", "Pydantic validation", "dependency injection", "SQLAlchemy models".
context	fork
agent	senior-python-engineer
allowed-tools	["Read","Write","Bash","Edit","Glob","Grep"]
metadata	{"version":"2.2.0","domains":["python","testing","packaging","web-development","async","security"]}

Mastering Python Skill

Production-ready Python patterns with runnable code examples.

Contents

• Workflow
• Reference Files
• Sample CLI Tools
• When NOT to Use
• Full Table of Contents

---

Workflow

Phase 1: Setup

1. Verify Python version

   python --version  # Require 3.10+, prefer 3.12+
   

2. Create and activate virtual environment

   python -m venv .venv && source .venv/bin/activate
   

3. Install dependencies

   poetry install  # or: pip install -r requirements.txt
   

Phase 2: Develop

4. Reference appropriate patterns:

   • Types → type-systems.md
   • Async → async-programming.md
   • APIs → fastapi-patterns.md
   • DB → database-access.md

5. Follow project structure from project-structure.md

Phase 3: Validate

6. Run quality checks

   ruff check . && ruff format --check .
   mypy src/
   

7. Run tests with coverage

   pytest -v --cov=src --cov-report=term-missing
   

Phase 4: Deploy

8. Build and verify package

   python -m build && twine check dist/*
   

9. Deploy per docker-deployment.md or ci-cd-pipelines.md

Pre-Completion Checklist:

- [ ] All tests pass
- [ ] mypy reports no errors
- [ ] ruff check clean
- [ ] Coverage ≥80%
- [ ] No security warnings in dependencies

---

Reference Files

Category	Files	Key Topics
Foundations	syntax-essentials, type-systems, project-structure, code-quality	Variables, type hints, generics, src layout, ruff, mypy
Patterns	async-programming, error-handling, decorators, context-managers, generators	async/await, exceptions, Result type, with statements, yield
Testing	pytest-essentials, mocking-strategies, property-testing	Fixtures, parametrize, unittest.mock, Hypothesis
Web APIs	fastapi-patterns, pydantic-validation,

See TOC.md for detailed topic lookup.

---

Sample CLI Tools

Runnable examples demonstrating production patterns:

Tool	Demonstrates	Reference
async_fetcher.py	Async HTTP, rate limiting, error handling	async-programming.md
config_loader.py	Pydantic settings, .env files, validation	pydantic-validation.md
db_cli.py	SQLAlchemy async CRUD, repository pattern	database-access.md
code_validator.py	Run→check→fix with ruff and mypy	code-quality.md

# Test examples
python sample-cli/async_fetcher.py https://httpbin.org/get
python sample-cli/config_loader.py --show-env
python sample-cli/db_cli.py init --sample-data && python sample-cli/db_cli.py list
python sample-cli/code_validator.py src/

---

When NOT to Use

• Non-Python languages: Use language-specific skills
• ML/AI model internals: Use PyTorch/TensorFlow skills
• Cloud infrastructure: Use AWS/GCP skills for infra (this covers code)
• Legacy Python 2: Focus is Python 3.10+