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/noesisvision/nasde-toolkit/python-best-practicesnpx skills add NoesisVision/nasde-toolkit --skill python-best-practicesgit clone --depth 1 https://github.com/NoesisVision/nasde-toolkitWhat 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 | $0.00036 | $0.01715 |
| Opus 5 | $0.00018 | $0.00857 |
| Sonnet 5 | $0.00007 | $0.00343 |
| Haiku 4.5 | $0.00004 | $0.00171 |
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- python-best-practices — 100% identical, 0 lines differ
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:
- Define data models - dataclasses, Pydantic models, or TypedDict first
- Define function signatures - parameter and return type hints
- Implement to satisfy types - let the type checker guide completeness
- 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
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.
- 2d ago First seen · 271 lines · 36 tokens per session scan A a856509d228b
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.
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