spillwavesolutions/agent_rulez · Archived

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", "Pydant…

Installation

$ npx skills add spillwavesolutions/agent_rulez --skill mastering-python-skill

Summary

  • 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".

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

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Repository health

Stars 47
Default branch main
Open issues 1
Status Archived

Skill metadata

Parsed from SKILL.md frontmatter.

Version2.1.0
Allowed toolsRead, Write, Bash, Edit
More metadata
version
2.1.0
domains
["python","testing","packaging","web-development","async","security"]

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 5,640 B
  • docs SUMMARY.md 589 B

History

  1. First recorded snapshot · 1 installs

SKILL.md

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+ ``

  1. Create and activate virtual environment

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

  1. Install dependencies

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

Phase 2: Develop

  1. 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)

  1. Follow project structure from [project-structure.md](references/foundations/project-structure.md)

Phase 3: Validate

  1. Run quality checks

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

  1. 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/* ``

  1. 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
[asyncfetcher.py](sample-cli/asyncfetcher.py) Async HTTP, rate limiting, error handling [async-programming.md](references/patterns/async-programming.md)
[configloader.py](sample-cli/configloader.py) Pydantic settings, .env files, validation [pydantic-validation.md](references/web-apis/pydantic-validation.md)
[dbcli.py](sample-cli/dbcli.py) SQLAlchemy async CRUD, repository pattern [database-access.md](references/web-apis/database-access.md)
[codevalidator.py](sample-cli/codevalidator.py) Run→check→fix with ruff and mypy [code-quality.md](references/foundations/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+