ABOUTME: Comprehensive skill for idiomatic Python best practices

ABOUTME: Covers loops, dicts, comprehensions, typing, Pydantic v2, error handling

Idiomatic Python Best Practices

Target: Python 3.11+ with modern tooling (uv, Pydantic v2, type hints).

Detailed patterns: See references/pydantic-patterns.md and references/advanced-patterns.md

Quick Reference

🛑 FILE OPERATION CHECKPOINT (BLOCKING)

Before EVERY Write or Edit tool call on a .py file:

╔══════════════════════════════════════════════════════════════════╗
║  🛑 STOP - PYTHON SKILL CHECK                                    ║
║                                                                  ║
║  You are about to modify a .py file.                             ║
║                                                                  ║
║  QUESTION: Is /python skill currently active?                    ║
║                                                                  ║
║  If YES → Proceed with the edit                                  ║
║  If NO  → STOP! Invoke /python FIRST, then edit                  ║
║                                                                  ║
║  This check applies to:                                          ║
║  ✗ Write tool with file_path ending in .py                       ║
║  ✗ Edit tool with file_path ending in .py                        ║
║  ✗ ANY Python file, regardless of conversation topic             ║
║                                                                  ║
║  Examples that REQUIRE this skill:                               ║
║  - "update the schemas" (edits schemas.py)                       ║
║  - "fix the import" (edits any .py file)                         ║
║  - "add logging" (edits Python code)                             ║
╚══════════════════════════════════════════════════════════════════╝

Why this matters: In session 1ea73ffd, Claude edited 3+ Python files without invoking the Python skill, leading to potential style/pattern inconsistencies.

🔄 RESUMED SESSION CHECKPOINT

┌─────────────────────────────────────────────────────────────┐
│  SESSION RESUMED - PYTHON SKILL VERIFICATION                │
│                                                             │
│  Before continuing:                                         │
│  1. Type hints on all functions?                            │
│  2. Pydantic v2 for API validation?                         │
│  3. ABOUTME headers on new files?                           │
│  4. Run: ruff check <file>.py && mypy <file>.py             │
│  5. Re-invoke /python if skill context was lost             │
└─────────────────────────────────────────────────────────────┘

Core Patterns

Iteration

# Direct iteration
for item in items:
    process(item)

# With index
for i, item in enumerate(items):
    print(f"{i}: {item}")

# Parallel iteration
for name, score in zip(names, scores, strict=True):
    process(name, score)

# Reversed/sorted (no copy)
for item in reversed(items):
    process(item)

Dictionaries

# Safe access with default
port = config.get("port", 8080)

# Initialize-if-missing
groups: dict[str, list[str]] = {}
groups.setdefault(category, []).append(item)

# Merge dicts (Python 3.9+)
merged = defaults | overrides

# Dict comprehension
squares = {n: n**2 for n in range(10)}

Comprehensions

# List comprehension
squared = [x**2 for x in numbers]
evens = [x for x in numbers if x % 2 == 0]

# Generator expression (memory efficient)
total = sum(x**2 for x in range(1_000_000))
any_match = any(item.is_valid for item in items)

# Set comprehension
unique_domains = {email.split("@")[1] for email in emails}

Unpacking

# Basic
x, y, z = coordinates
first, *rest = items
a, b = b, a  # Swap

# Dict unpacking
combined = {**defaults, **overrides}
connect(**config)  # Pass as kwargs

Type Hints

Basic Types

def greet(name: str, times: int = 1) -> str:
    return f"Hello, {name}! " * times

def process(items: list[str]) -> dict[str, int]:
    return {item: len(item) for item in items}

Optional and Union

def find_user(user_id: int) -> User | None:
    return db.get(user_id)

def process(value: int | str | None) -> str:
    if value is None:
        return "none"
    return str(value)

Generic Collections

from collections.abc import Sequence, Mapping, Iterable, Callable

def process_items(items: Sequence[str]) -> list[str]:
    return [item.upper() for item in items]

def apply_func(data: Iterable[int], func: Callable[[int], int]) -> list[int]:
    return [func(x) for x in data]

Pydantic v2 (Quick Reference)

Use Pydantic for API validation and serialization. Full patterns: references/pydantic-patterns.md

from pydantic import BaseModel, Field, EmailStr

class User(BaseModel):
    id: int
    email: EmailStr
    name: str = Field(min_length=1, max_length=100)
    is_active: bool = True

# Parse and validate
user = User.model_validate({"id": 1, "email": "test@example.com", "name": "Alice"})

# Serialize
user.model_dump()       # To dict
user.model_dump_json()  # To JSON

When to Use What

Error Handling

Catch Specific Exceptions

try:
    value = int(user_input)
except ValueError:
    print("Invalid number")
except TypeError:
    print("Wrong type")

# DON'T: bare except
try:
    value = int(user_input)
except:  # Catches KeyboardInterrupt too!
    pass

EAFP (Pythonic)

# EAFP: Easier to Ask Forgiveness than Permission
try:
    value = mapping[key]
except KeyError:
    value = default

# vs LBYL: Look Before You Leap
if key in mapping:
    value = mapping[key]
else:
    value = default

Custom Exceptions

class ValidationError(Exception):
    def __init__(self, field: str, message: str):
        self.field = field
        self.message = message
        super().__init__(f"{field}: {message}")

def validate_email(email: str) -> str:
    if "@" not in email:
        raise ValidationError("email", "Must contain @")
    return email.lower()

Context Managers

# File handling (always use with)
with open("data.txt", "r", encoding="utf-8") as f:
    content = f.read()

# Multiple files
with open("in.txt") as infile, open("out.txt", "w") as outfile:
    outfile.write(infile.read().upper())

String Handling

# f-strings
name = "Alice"
score = 95.678
print(f"Score: {score:.2f}")  # 95.68
print(f"{1000000:,}")  # 1,000,000
print(f"{x=}")  # Debug: x=42

# String building (use join, not +=)
result = " ".join(parts)

Best Practices

DO

• Use meaningful variable names
• Prefer composition over inheritance
• Keep functions small and focused
• Use type hints consistently
• Use pathlib.Path for file paths
• Use logging instead of print

DON’T

• Use mutable default arguments: def f(items=[]):
• Modify lists while iterating
• Use from module import *
• Catch bare except:
• Use eval() with untrusted input

Quality Tools

# Linting
ruff check .
ruff check --fix .

# Type checking
mypy src/

# Formatting
ruff format .

Advanced Patterns

See references/advanced-patterns.md for:

• itertools (chain, islice, groupby, product)
• functools (partial, lru_cache, cached_property)
• collections (defaultdict, Counter, deque)
• Custom context managers
• Generator functions
• Dataclasses
• Enumerations
• TypedDict for data contracts

References

• PEP 8 – Style Guide
• Pydantic v2 Documentation
• Real Python – Idiomatic Python