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".
Best use case
mastering-python-skill is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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".
Teams using mastering-python-skill should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/mastering-python-skill/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How mastering-python-skill Compares
| Feature / Agent | mastering-python-skill | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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".
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
# Mastering Python Skill Production-ready Python patterns with runnable code examples. ## Contents - [Workflow](#workflow) - [Reference Files](#reference-files) - [Sample CLI Tools](#sample-cli-tools) - [When NOT to Use](#when-not-to-use) - [Full Table of Contents](TOC.md) --- ## 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 4. Reference appropriate patterns: - Types → [type-systems.md](references/foundations/type-systems.md) - Async → [async-programming.md](references/patterns/async-programming.md) - APIs → [fastapi-patterns.md](references/web-apis/fastapi-patterns.md) - DB → [database-access.md](references/web-apis/database-access.md) 5. Follow project structure from [project-structure.md](references/foundations/project-structure.md) ### Phase 3: Validate 6. Run quality checks ```bash ruff check . && ruff format --check . mypy src/ ``` 7. Run tests with coverage ```bash pytest -v --cov=src --cov-report=term-missing ``` ### Phase 4: Deploy 8. Build and verify package ```bash python -m build && twine check dist/* ``` 9. Deploy per [docker-deployment.md](references/packaging/docker-deployment.md) or [ci-cd-pipelines.md](references/production/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](references/foundations/syntax-essentials.md), [type-systems](references/foundations/type-systems.md), [project-structure](references/foundations/project-structure.md), [code-quality](references/foundations/code-quality.md) | Variables, type hints, generics, src layout, ruff, mypy | | **Patterns** | [async-programming](references/patterns/async-programming.md), [error-handling](references/patterns/error-handling.md), [decorators](references/patterns/decorators.md), [context-managers](references/patterns/context-managers.md), [generators](references/patterns/generators.md) | async/await, exceptions, Result type, with statements, yield | | **Testing** | [pytest-essentials](references/testing/pytest-essentials.md), [mocking-strategies](references/testing/mocking-strategies.md), [property-testing](references/testing/property-testing.md) | Fixtures, parametrize, unittest.mock, Hypothesis | | **Web APIs** | [fastapi-patterns](references/web-apis/fastapi-patterns.md), [pydantic-validation](references/web-apis/pydantic-validation.md), [database-access](references/web-apis/database-access.md) | Dependencies, middleware, validators, SQLAlchemy async | | **Packaging** | [poetry-workflow](references/packaging/poetry-workflow.md), [pyproject-config](references/packaging/pyproject-config.md), [docker-deployment](references/packaging/docker-deployment.md) | Lock files, PEP 621, multi-stage builds | | **Production** | [ci-cd-pipelines](references/production/ci-cd-pipelines.md), [monitoring](references/production/monitoring.md), [security](references/production/security.md) | GitHub Actions, OpenTelemetry, OWASP, JWT | See [TOC.md](TOC.md) for detailed topic lookup. --- ## Sample CLI Tools Runnable examples demonstrating production patterns: | Tool | Demonstrates | Reference | |------|-------------|-----------| | [async_fetcher.py](sample-cli/async_fetcher.py) | Async HTTP, rate limiting, error handling | [async-programming.md](references/patterns/async-programming.md) | | [config_loader.py](sample-cli/config_loader.py) | Pydantic settings, .env files, validation | [pydantic-validation.md](references/web-apis/pydantic-validation.md) | | [db_cli.py](sample-cli/db_cli.py) | SQLAlchemy async CRUD, repository pattern | [database-access.md](references/web-apis/database-access.md) | | [code_validator.py](sample-cli/code_validator.py) | Run→check→fix with ruff and mypy | [code-quality.md](references/foundations/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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