mastering-python-skill

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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".

1installs

Sourcespillwavesolutions/agent-brain

Added on2026-02-21

NPX Install

npx skill4agent add spillwavesolutions/agent-brain mastering-python-skill

SKILL.md Content

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Mastering Python Skill

Production-ready Python patterns with runnable code examples.

Workflow

Phase 1: Setup

Verify Python version

bash

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

Create and activate virtual environment

bash

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

Install dependencies

bash

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

Phase 2: Develop

Reference appropriate patterns:
- Types → type-systems.md
- Async → async-programming.md
- APIs → fastapi-patterns.md
- DB → database-access.md
Follow project structure from project-structure.md

Phase 3: Validate

Run quality checks

bash

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

Run tests with coverage

bash

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

Phase 4: Deploy

Build and verify package
bash
```
python -m build && twine check dist/*
```
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+

mastering-python-skill

NPX Install

Tags

SKILL.md Content

Mastering Python Skill

Contents

Workflow

Phase 1: Setup

Phase 2: Develop

Phase 3: Validate

Phase 4: Deploy

Reference Files

Sample CLI Tools

When NOT to Use