gh-rstarkey-s5y-plugins/skills/python-expert/SKILL.md

name, description

name	description
python-expert	Use for Python development requiring async programming, type system expertise, testing patterns, or performance optimization.

Python Expert

Elite Python 3.13+ expertise for backend development, testing, async programming, and type systems.

When to Use

• Async/await, asyncio, concurrency patterns
• Type errors or complex annotations
• Writing/debugging tests (pytest, async, mocking)
• Performance optimization
• Security review
• Backend architecture (FastAPI, Django, SQLAlchemy)

Core Expertise

Python Mastery: Decorators, context managers, metaclasses, descriptors, generators, coroutines, data model, GIL internals

Backend: FastAPI/Django/Flask, PostgreSQL/Redis/MongoDB, SQLAlchemy/Django ORM, REST/GraphQL/WebSockets/gRPC, OAuth2/JWT, microservices

Code Standards

• Full type hints, Google/NumPy docstrings, 88-char lines
• PEP 8 naming, SOLID principles, secure by default
• Use f-strings for formatting, focused small functions

Testing

pytest: Use setup_method, pytest.raises, @patch for mocking Async: Use anyio for test fixtures, AsyncMock for mocking async functions Integration: In-memory SQLite fixtures with proper cleanup All network calls must be mocked

Async/Await

• asyncio.run() for entry, TaskGroup for structured concurrency (preferred over gather())
• asyncio.timeout() for timeouts, Semaphore for rate limiting
• Handle cancellation with try/finally, use ExceptionGroup for multiple errors
• Type: async def foo() -> T or Awaitable[T]

Type System

Modern syntax (Python 3.10+): list[str], dict[str, int], str | None Variance: dict invariant, Mapping covariant—use Mapping[K, V] when needed Advanced: Self for fluent methods, ParamSpec for decorator typing, TypedDict

Minimize Any:

• Use Protocol for structural typing instead of Any
• Use TypedDict for dicts with known structure instead of dict[str, Any]
• Document why Any is necessary when it must be used

Common fixes: Mixed type ops, SQLAlchemy column assignments, API response access Atomic processing: Fix ALL type errors in file with single edit

Patterns

# Dataclass with slots (memory efficient)
@dataclass(slots=True)
class User:
    name: str
    email: str
    tags: list[str] = field(default_factory=list)
    def __post_init__(self):
        if not self.name: raise ValueError("Name required")

# Pattern matching (3.10+)
match response.status:
    case 200: return response.json()
    case 404: raise NotFoundError()
    case _: raise APIError(response.status)

Prefer: Dependency injection over singletons, @cache for memoized instances

Security

• Validate/sanitize all inputs, parameterized SQL queries only
• Rate limiting, CORS/CSRF protection, secure sessions
• Avoid dynamic code evaluation and unsafe serialization with untrusted data

Cryptography:

• Forbidden: MD5, SHA-1, DES/3DES, RC4, custom crypto
• Required: SHA-256+ for hashing, AES-256-GCM for encryption, Argon2/scrypt for passwords
• Use secrets module for tokens, cryptography package for crypto operations

Performance

• Profile first (cProfile, timeit), optimize real bottlenecks
• Sets for O(1) lookup, deque for queues, Counter for counting
• Generators for large data, __slots__ for memory
• @cache (unbounded) or @lru_cache (bounded) for memoization
• Eager loading (N+1), connection pooling, async I/O

Pitfalls

# Mutable defaults: use None, then check identity
def f(items=None):
    if items is None:
        items = []  # Don't use `or []` - empty list is falsy!
    return items

# Late binding: capture with default arg
funcs = [lambda x=i: x for i in range(3)]

Avoid: God classes, spaghetti code, magic numbers, copy-paste, bare except:

Error Handling

Custom exception hierarchies, structured JSON logging, circuit breakers, retry with backoff, graceful degradation

Tooling

ruff check .    # lint
ruff format .   # format
pyright .       # typecheck

Stack: uv, httpx/aiohttp/anyio, pydantic

Cleanup

Remove before completion: debug-*.py, test-*.py, __pycache__/, *.pyc, *_REPORT.md