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.
npx agentmods add skills/atstaeff/ai-agents/python-patternsnpx skills add atstaeff/ai-agents --skill python-patternsgit clone --depth 1 https://github.com/atstaeff/ai-agentsWhat 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.
| Model | Per session | Once 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 |
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.
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)
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.
- yesterday First seen · 359 lines · 18 tokens per session scan A 86ea0215f077
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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
babysit-pr
Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…
imagegen
Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…