Agent skill

mastering-python-skill

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".

View SKILL.md on GitHub Repository
Stars 163
Forks 31

Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/mastering-python-skill

Metadata

Additional technical details for this skill

domains 
[
    "python",
    "testing",
    "packaging",
    "web-development",
    "async",
    "security"
]
version
2.1.0

SKILL.md

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

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

  2. Create and activate virtual environment

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

  3. Install dependencies

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

Phase 2: Develop

  1. Reference appropriate patterns:

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

  2. Follow project structure from project-structure.md

Phase 3: Validate

  1. Run quality checks

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

  2. Run tests with coverage

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

Phase 4: Deploy

  1. Build and verify package

    bash
    python -m build && twine check dist/*

  2. 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, database-access Dependencies, middleware, validators, SQLAlchemy async
Packaging poetry-workflow, pyproject-config, docker-deployment Lock files, PEP 621, multi-stage builds
Production ci-cd-pipelines, monitoring, security GitHub Actions, OpenTelemetry, OWASP, JWT

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
bash
# 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+

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