python-best-practices

A set of Python coding patterns that uses type hints and structured data models to make function inputs, outputs, and valid states explicit. It covers dataclasses, tagged alternatives, custom types, and protocols.

In plain words
What is it for?
Use it when reading or writing Python files, especially when defining data models, function signatures, validation at system boundaries, or type-safe interfaces.
Why use it?
It reduces ambiguity and helps catch invalid data and missing cases earlier, before they cause runtime problems.

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/noesisvision/nasde-toolkit/python-best-practices
Any agent
npx skills add NoesisVision/nasde-toolkit --skill python-best-practices
Clone the repo
git clone --depth 1 https://github.com/NoesisVision/nasde-toolkit

Made for: Claude Code, Codex.

Per session 36 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,715 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 $0.00036 $0.01715
Opus 5 $0.00018 $0.00857
Sonnet 5 $0.00007 $0.00343
Haiku 4.5 $0.00004 $0.00171

Measured 2d ago against content hash a856509d228b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

python-best-practices 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 2d ago.

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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

examples/nasde-dev-skill/variants/claude-nasde-dev-full-stack/skills/python-best-practices/SKILL.md · 271 lines

How it starts

The opening of the file, as written. The whole thing — 271 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Python Best Practices

Type-First Development

Types define the contract before implementation. Follow this workflow:

  1. Define data models - dataclasses, Pydantic models, or TypedDict first
  2. Define function signatures - parameter and return type hints
  3. Implement to satisfy types - let the type checker guide completeness
  4. Validate at boundaries - runtime checks where data enters the system

Make Illegal States Unrepresentable

Use Python's type system to prevent invalid states at type-check time.

Dataclasses for structured data:

from dataclasses import dataclass
from datetime import datetime

@dataclass(frozen=True)
class User:
    id: str
    email: str
    name: str
    created_at: datetime

@dataclass(frozen=True)
class CreateUser:
    email: str
    name: str

# Frozen dataclasses are immutable - no accidental mutation

Discriminated unions with Literal:

from dataclasses import dataclass
from typing import Literal

@dataclass
class Idle:
    status: Literal["idle"] = "idle"

@dataclass
class Loading:
    status: Literal["loading"] = "loading"

@dataclass
class Success:
    status: Literal["success"] = "success"
    data: str

@dataclass
class Failure:
    status: Literal["error"] = "error"
    error: Exception

RequestState = Idle | Loading | Success | Failure

def handle_state(state: RequestState) -> None:
    match state:
        case Idle():
            pass
        case Loading():
            show_spinner()
        case Success(data=data):
            render(data)
        case Failure(error=err):
            show_error(err)

NewType for domain primitives:

from typing import NewType

UserId = NewType("UserId", str)
OrderId = NewType("OrderId", str)

def get_user(user_id: UserId) -> User:
    # Type checker prevents passing OrderId here
    ...

def create_user_id(raw: str) -> UserId:
    return UserId(raw)

Enums for constrained values:

from enum import Enum, auto

class Role(Enum):
    ADMIN = auto()
    USER = auto()
    GUEST = auto()

def check_permission(role: Role) -> bool:
    match role:
        case Role.ADMIN:
            return True
        case Role.USER:
            return limited_check()
        case Role.GUEST:
            return False
    # Type checker warns if case is missing

Read the full file on GitHub · 271 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. 2d ago First seen · 271 lines · 36 tokens per session scan A a856509d228b

Subscribe to this mod's changes

python-best-practices is a skill published in the GitHub repository NoesisVision/nasde-toolkit (12 stars, last pushed 8d ago), licensed MIT. It adds 36 tokens to every session and 1,715 once invoked, about $0.0002 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 2026-08-30.