python-pro

name

python-pro

description

Modern Python with uv, ruff, pyright, strict typing, and clean module design. Use when implementing, debugging, refactoring, or reviewing Python code; designing module boundaries; setting up Python projects; resolving type errors; tightening pyproject.toml; choosing between dataclasses and Pydantic; or working with async code, pytest, or packaging. Applies to any Python work unless a more specific role overrides.

Python Pro

Senior-level Python expertise for production projects. Focuses on modern tooling, strict type checking, and Pythonic idioms.

When Invoked

1. Review pyproject.toml for project conventions and tooling config
2. For build system setup, invoke the just-pro skill
3. Apply Python idioms and established project patterns

Core Standards

Required:

• All public functions/classes have docstrings
• All functions have type annotations (params + return)
• ruff passes with project configuration
• pyright passes in strict mode
• Meaningful tests with pytest

Foundational Principles:

• Single Responsibility: One module = one purpose, one function = one job
• No God Classes: Split large classes; if it has 10+ methods, decompose
• Dependency Injection: Pass dependencies, don't create them internally
• Explicit over Implicit: Clear is better than clever

---

Project Setup (Python 3.12+)

Version Management

Pin Python version with mise: mise use python@3.12 (creates .mise.toml — commit it). Team members run mise install. See mise skill for setup.

New Project Quick Start with uv

uv is the modern Python package manager (10-100x faster than pip).

# Initialize project
uv init project-name
cd project-name

# Or in existing directory
uv init

# Add dependencies
uv add httpx pydantic

# Add dev dependencies
uv add --dev pytest pytest-cov pytest-asyncio ruff pyright

# Set up src layout (required for imports to work)
mkdir -p src/projectname tests
mv *.py src/projectname/ 2>/dev/null || true
touch src/projectname/__init__.py tests/__init__.py tests/conftest.py

# Copy configs from this skill's references/ directory:
#   references/gitignore               -> .gitignore
#   references/pyproject-template.toml -> use as pyproject.toml base
#   references/pyrightconfig.json      -> pyrightconfig.json
# For build system, invoke just-pro skill

# Verify
just check   # Or: uv run ruff check . && uv run pyright

pyproject.toml Requirements

Note: uv init creates a minimal pyproject.toml. For src layout to work, add these sections:

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

[tool.hatch.build.targets.wheel]
packages = ["src/projectname"]

[tool.pytest.ini_options]
asyncio_mode = "auto"
testpaths = ["tests"]

Without [build-system] and [tool.hatch.build.targets.wheel], tests cannot import your package.

Developer Onboarding

git clone <repo> && cd <repo>
just setup         # Runs mise trust/install + uv sync
just check         # Verify everything works

Or manually:

mise trust && mise install  # Get pinned Python version
uv sync                     # Install dependencies from lockfile

Why uv? Lockfile-based reproducibility, automatic venv management, 10-100x faster than pip.

---

Build System

Invoke the just-pro skill for build system setup. It covers:

• Simple repos vs monorepos
• Hierarchical justfile modules
• Python-specific templates

Why just? Consistent toolchain frontend between agents and humans. Instead of remembering uv run ruff check --fix ., use just fix.

---

Quality Assurance

Auto-Fix First - Always try auto-fix before manual fixes:

just fix             # Or: uv run ruff check --fix . && uv run ruff format .

Verification:

just check           # Or: uv run ruff check . && uv run pyright && uv run pytest

---

Handling Strict Pyright

Untyped Libraries

Some libraries lack type stubs. In pyright strict mode, use Any:

from typing import Any

import structlog  # No complete type stubs

# Annotate as Any to silence pyright
logger: Any = structlog.get_logger()

When to use Any:

• Library has no stubs and you can't create them
• Return types are dynamic/unpredictable
• Interfacing with weakly-typed external systems

Avoid # type: ignore - it silences all errors. Explicit Any is clearer.

TYPE_CHECKING Imports

Ruff's TCH rules flag imports used only for type hints. Move them to a TYPE_CHECKING block:

from __future__ import annotations  # Required for forward refs

from typing import TYPE_CHECKING

from mypackage.service import run_service  # Runtime import

if TYPE_CHECKING:
    from mypackage.models import User  # Type-only import

def process(user: User) -> None:  # Works due to __future__ annotations
    run_service(user)

Rules:

• Imports used at runtime stay at top level
• Imports used only in type hints go in TYPE_CHECKING
• from __future__ import annotations enables string-based forward refs
• This also reduces import cycles

Optional Field Access

When a field might be None, assert before accessing:

# BAD - pyright error: "x" could be None
result.error_message.lower()

# GOOD - narrow the type first
assert result.error_message is not None
result.error_message.lower()

# OR use conditional
if result.error_message:
    result.error_message.lower()

---

Quick Reference

Type Annotations

from typing import TypeVar, Protocol
from collections.abc import Callable, Iterator, Sequence

# Basic annotations
def process(items: list[str], timeout: float = 30.0) -> dict[str, int]:
    ...

# Generic functions
T = TypeVar("T")

def first(items: Sequence[T]) -> T | None:
    return items[0] if items else None

# Protocols for structural typing (duck typing with types)
class Readable(Protocol):
    def read(self, n: int = -1) -> bytes: ...

def load_data(source: Readable) -> bytes:
    return source.read()

Error Handling

# Custom exceptions with context
class ValidationError(Exception):
    def __init__(self, field: str, message: str) -> None:
        self.field = field
        self.message = message
        super().__init__(f"{field}: {message}")

# Explicit error handling
def parse_config(path: str) -> Config:
    try:
        with open(path) as f:
            data = json.load(f)
    except FileNotFoundError:
        raise ConfigError(f"Config file not found: {path}") from None
    except json.JSONDecodeError as e:
        raise ConfigError(f"Invalid JSON in {path}: {e}") from e
    return Config.from_dict(data)

# Use Result pattern for expected failures (optional)
from dataclasses import dataclass

@dataclass
class Ok[T]:
    value: T

@dataclass
class Err[E]:
    error: E

type Result[T, E] = Ok[T] | Err[E]

Data Classes and Pydantic

from dataclasses import dataclass, field
from pydantic import BaseModel, Field

# Simple data containers
@dataclass(frozen=True, slots=True)
class Point:
    x: float
    y: float

# Pydantic for validation and serialization
class UserCreate(BaseModel):
    name: str = Field(min_length=1, max_length=100)
    email: str
    age: int = Field(ge=0, le=150)

    model_config = {"strict": True}

Async Patterns

import asyncio
from collections.abc import AsyncIterator

# Async context managers
async def fetch_with_timeout(url: str, timeout: float = 10.0) -> bytes:
    async with asyncio.timeout(timeout):
        async with httpx.AsyncClient() as client:
            response = await client.get(url)
            response.raise_for_status()
            return response.content

# Async generators
async def paginate(client: Client, url: str) -> AsyncIterator[Item]:
    while url:
        response = await client.get(url)
        for item in response.items:
            yield item
        url = response.next_url

# Gather with error handling
async def fetch_all(urls: list[str]) -> list[bytes | Exception]:
    tasks = [fetch_with_timeout(url) for url in urls]
    return await asyncio.gather(*tasks, return_exceptions=True)

Context Managers

from contextlib import contextmanager, asynccontextmanager
from collections.abc import Generator, AsyncGenerator

@contextmanager
def temporary_config(overrides: dict[str, str]) -> Generator[None, None, None]:
    original = config.copy()
    config.update(overrides)
    try:
        yield
    finally:
        config.clear()
        config.update(original)

@asynccontextmanager
async def database_transaction(db: Database) -> AsyncGenerator[Transaction, None]:
    tx = await db.begin()
    try:
        yield tx
        await tx.commit()
    except Exception:
        await tx.rollback()
        raise

Testing with pytest

import pytest
from unittest.mock import Mock, patch

# Parametrized tests
@pytest.mark.parametrize(
    "input_val,expected",
    [
        ("hello", "HELLO"),
        ("", ""),
        ("123", "123"),
    ],
    ids=["normal", "empty", "digits"],
)
def test_uppercase(input_val: str, expected: str) -> None:
    assert uppercase(input_val) == expected

# Fixtures
@pytest.fixture
def sample_user() -> User:
    return User(name="Test", email="[e-mail]")

@pytest.fixture
def mock_client() -> Mock:
    client = Mock(spec=APIClient)
    client.get.return_value = {"status": "ok"}
    return client

# Async tests
@pytest.mark.asyncio
async def test_fetch_data(mock_client: Mock) -> None:
    result = await fetch_data(mock_client, "test-id")
    assert result.status == "ok"

# Exception testing
 () -> :
     pytest.raises(ValueError, =):
        process_value(-)

Structured Logging

import logging
from typing import Any

import structlog

# Note: structlog lacks complete type stubs. Use Any for logger type.
logger: Any = structlog.get_logger()

# Configure structlog for console output
structlog.configure(
    processors=[
        structlog.stdlib.add_log_level,
        structlog.processors.TimeStamper(fmt="iso"),
        structlog.dev.ConsoleRenderer(),  # Or JSONRenderer() for prod
    ],
    wrapper_class=structlog.make_filtering_bound_logger(logging.INFO),
    context_class=dict,
    logger_factory=structlog.PrintLoggerFactory(),
)

# Structured logging with context
logger.info("request_processed", method="GET", path="/api/users", duration_ms=42)
logger.error("operation_failed", error=str(e), user_id=user_id)

# Bind context for a scope
bound_logger: Any = logger.bind(request_id=request_id, user_id=user_id)
bound_logger.info("starting_operation")

Package Organization

project/
├── src/
│   └── projectname/
│       ├── __init__.py
│       ├── api/              # HTTP handlers
│       ├── domain/           # Business logic
│       └── infra/            # External integrations
├── tests/
│   ├── conftest.py           # Shared fixtures
│   ├── test_api/
│   └── test_domain/
├── pyproject.toml
├── pyrightconfig.json
├── uv.lock
└── justfile

Rules: Use src/ layout for installable packages. One module = one purpose. Avoid utils, common, helpers modules.

---

DX Patterns

Doctor Recipe with Version Validation

# Validate toolchain versions meet requirements
doctor:
    #!/usr/bin/env bash
    set -euo pipefail
    echo "Checking toolchain..."

    # Validate Python version (requires 3.12+)
    PY_VERSION=$(python --version | grep -oE '[0-9]+\.[0-9]+')
    if [[ "$(printf '%s\n' "3.12" "$PY_VERSION" | sort -V | head -1)" != "3.12" ]]; then
        echo "FAIL: Python $PY_VERSION < 3.12 required"
        exit 1
    fi
    echo "OK: Python $PY_VERSION"

    # Check uv is available
    if ! command -v uv &> /dev/null; then
        echo "FAIL: uv not found. Install: curl -LsSf https://astral.sh/uv/install.sh | sh"
        exit 1
    fi
    echo "OK: uv $(uv --version | head -1)"

    echo "All checks passed"

First-Run Detection

# Setup with first-run detection
setup:
    #!/usr/bin/env bash
    if [[ -f .setup-complete ]]; then
        echo "Already set up. Run 'just setup-force' to reinstall."
        exit 0
    fi
    mise trust && mise install
    uv sync
    touch .setup-complete
    echo "Setup complete"

setup-force:
    rm -f .setup-complete
    @just setup

---

Anti-Patterns

• Bare except: clauses (always specify exception type)
• Mutable default arguments (def f(items=[]) - use None and create inside)
• Star imports (from module import *)
• God classes with 10+ methods (extract into companion modules, don't compress)
• Compressing code or removing comments to fit length limits (extract into well-named functions/modules instead)
• Missing type annotations on public APIs
• Any when specific types work
• Nested functions when a module-level function suffices
• print() for logging (use logging/structlog)
• Ignoring return values of functions with side effects

---

AI Agent Guidelines

Before writing code:

1. Read pyproject.toml for project structure and dependencies
2. Check ruff/pyright config for project-specific rules
3. Identify existing patterns in the codebase to follow

When writing code:

1. Add type annotations immediately - never leave functions untyped
2. Add docstrings to public functions/classes
3. Use existing project abstractions over creating new ones
4. Prefer explicit types; use generics only when pattern repeats 3+ times

Before committing:

1. Run just check (standard for projects using just)
2. Fallback: uv run ruff check --fix . && uv run ruff format .
3. Fallback: uv run pyright && uv run pytest