python-patterns

A guide to idiomatic Python: common ways to write clear, maintainable Python code. It covers design patterns such as repositories for data access and service layers for business logic, with practical examples.

In plain words
What is it for?
Writing, reviewing, or refactoring Python code using modern practices, repository and service-layer patterns, injected dependencies, and production-oriented standards.
Why use it?
It helps keep Python code consistent and easier to test, replace, and maintain. Separating data access from business rules can also make changes safer.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/atstaeff/ai-agents/python-patterns
Any agent
npx skills add atstaeff/ai-agents --skill python-patterns
Clone the repo
git clone --depth 1 https://github.com/atstaeff/ai-agents

Made for: Claude Code, Codex.

Per session 18 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,479 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00018 $0.02479
Opus 5 $0.00009 $0.01239
Sonnet 5 $0.00004 $0.00496
Haiku 4.5 $0.00002 $0.00248

Measured yesterday against content hash 86ea0215f077, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

python-patterns scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/python-patterns/SKILL.md · 359 lines

How it starts

The opening of the file, as written. The whole thing — 359 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Python Patterns Skill

Instructions for AI

Apply idiomatic Python patterns, modern best practices, and production-grade code standards. Use this skill when writing, reviewing, or refactoring Python code.

Reference: atstaeff/better-python for concrete before/after examples of all patterns below.

Core Patterns

1. Repository Pattern

Abstraction over data access, enabling testability and swappable backends.

from typing import Protocol
from uuid import UUID

class Repository[T](Protocol):
    async def get(self, id: UUID) -> T | None: ...
    async def save(self, entity: T) -> None: ...
    async def delete(self, id: UUID) -> None: ...
    async def list_all(self) -> list[T]: ...

2. Service Layer Pattern

Business logic encapsulated in services with injected dependencies.

class OrderService:
    def __init__(self, repo: OrderRepository, events: EventBus) -> None:
        self._repo = repo
        self._events = events

    async def place_order(self, command: PlaceOrderCommand) -> Order:
        order = Order.create(command)
        await self._repo.save(order)
        await self._events.publish(OrderPlaced(order_id=order.id))
        return order

3. Domain Events

Decouple side effects from business logic.

from dataclasses import dataclass
from datetime import datetime
from uuid import UUID

@dataclass(frozen=True)
class DomainEvent:
    occurred_at: datetime = field(default_factory=lambda: datetime.now(UTC))

@dataclass(frozen=True)
class OrderPlaced(DomainEvent):
    order_id: UUID
    customer_id: UUID
    total_amount: float

4. Result Pattern (Error Handling)

Explicit success/failure without exceptions for expected cases.

from dataclasses import dataclass
from typing import Generic, TypeVar

T = TypeVar("T")
E = TypeVar("E")

@dataclass(frozen=True)
class Ok(Generic[T]):
    value: T

@dataclass(frozen=True)
class Err(Generic[E]):
    error: E

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

# Usage
def divide(a: float, b: float) -> Result[float, str]:
    if b == 0:
        return Err("Division by zero")
    return Ok(a / b)

Read the full file on GitHub · 359 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 359 lines · 18 tokens per session scan A 86ea0215f077

Subscribe to this mod's changes

python-patterns is a skill published in the GitHub repository atstaeff/ai-agents (2 stars, last pushed 5mo ago), licensed MIT. It adds 18 tokens to every session and 2,479 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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