python-typing

A set of rules for writing explicit Python type hints. It requires specific types for values, collections, function parameters, return values, and callbacks.

In plain words
What is it for?
Use it when writing or reviewing Python functions, dictionaries, lists, callbacks, Pydantic models, and modern Python 3.10+ type annotations.
Why use it?
It catches unclear data shapes and missing annotations early, making code easier to check and maintain. It also avoids accidental Any types and outdated typing syntax.

Cursor rule for Cursor

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 rules/techskies11/datadog-mcp/python-typing
Clone the repo
git clone --depth 1 https://github.com/techskies11/datadog-mcp

Made for: Cursor.

Per session 0 Nothing until a file matches its globs; then the whole rule loads.
When invoked 2,171 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 $0.00000 $0.02171
Opus 5 $0.00000 $0.01086
Sonnet 5 $0.00000 $0.00434
Haiku 4.5 $0.00000 $0.00217

Measured yesterday against content hash 238db93f4fe6, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

python-typing 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.

.cursor/rules/python-typing.mdc · 296 lines

How it starts

The opening of the file, as written. The whole thing — 296 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Python Typing Standards

This project maintains STRICT typing standards. NO Any, NO untyped dict/list, NO object unless absolutely necessary.

Core Principles

❌ NEVER Use These

# ❌ BAD - No Any types
def process(data: Any) -> Any:
    pass

# ❌ BAD - No untyped collections
def get_data() -> dict:
    pass

def process_items(items: list) -> None:
    pass

# ❌ BAD - No implicit Any
def transform(callback):  # callback is implicitly Any
    pass

✅ ALWAYS Use These

# ✅ GOOD - Specific types
from typing import Literal
from collections.abc import Callable, Sequence, Mapping
from pydantic import BaseModel

class UserData(BaseModel):
    id: str
    name: str
    age: int

def process(data: UserData) -> UserData:
    pass

# ✅ GOOD - Typed collections (Python 3.10+ syntax)
def get_data() -> dict[str, int]:
    pass

def process_items(items: list[str]) -> None:
    pass

# ✅ GOOD - Typed callbacks
def transform(callback: Callable[[str], int]) -> int:
    pass

Modern Python Syntax (3.10+)

Use Built-in Generics

# ❌ OLD - Don't import from typing
from typing import List, Dict, Tuple, Optional, Union

def process(data: List[str]) -> Dict[str, int]:
    pass

# ✅ NEW - Use built-in types directly
def process(data: list[str]) -> dict[str, int]:
    pass

Use Union Shorthand

# ❌ OLD
from typing import Optional, Union

def get_user(user_id: str) -> Optional[User]:
    pass

def parse(value: Union[str, int, float]) -> str:
    pass

# ✅ NEW
def get_user(user_id: str) -> User | None:
    pass

def parse(value: str | int | float) -> str:
    pass

# ✅ IMPORTANT - Always put None last
result: str | int | None  # ✅ Correct
result: None | str | int  # ❌ Wrong order

Pydantic Models, Not TypedDict, for Structured Data

This project uses Pydantic BaseModel for every tool input/output - not TypedDict. TypedDict gives static-only checking with zero runtime validation; Pydantic validates at the API boundary, where a shape mismatch actually matters. See pydantic-models.mdc for the full inbound/outbound convention (DatadogModel vs ToolResponse/PaginatedListResponse).

Read the full file on GitHub · 296 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 · 296 lines · 0 tokens per session scan A 238db93f4fe6

Subscribe to this mod's changes

python-typing is a cursor rule published in the GitHub repository techskies11/datadog-mcp (0 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,171 tokens. 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.