py

Cursor rule "py" from zenbase-ai/llml, covering python coding rules for llml project, project structure and management, coding style and formatting, typing and type safety and function and api design.

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/zenbase-ai/llml/py
Clone the repo
git clone --depth 1 https://github.com/zenbase-ai/llml

Made for: Cursor.

Per session 0 Nothing until a file matches its globs; then the whole rule loads.
When invoked 843 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin unknown 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 $0.00843
Opus 5 $0.00000 $0.00421
Sonnet 5 $0.00000 $0.00169
Haiku 4.5 $0.00000 $0.00084

Measured today against content hash 85d7dad04b95, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

py 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 today.

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/py.mdc · 65 lines

How it starts

The opening of the file, as written. The whole thing — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Python Coding Rules for LLML Project

Project Structure and Management

  • Configuration: All project metadata, dependencies, and tool configurations must be managed in pyproject.toml.
  • Dependencies:
    • Runtime dependencies should be added to the [project.dependencies] section.
    • Development dependencies must be added to the [project.optional-dependencies] section under the dev group.
    • Use uv for managing the virtual environment and installing dependencies.
  • Source Code: All main application logic must reside in the py/src/ directory.
  • Tests: All tests must be placed in the py/tests/ directory and follow the naming convention test_*.py.

Coding Style and Formatting

  • Linter/Formatter: ruff is the designated tool for all linting and formatting. All code must be compliant with the rules defined in pyproject.toml.
  • Line Length: The maximum line length is 88 characters.
  • Quotes: Use double quotes (") for all strings. Single quotes are not permitted.
  • Indentation: Use 4 spaces for indentation. Tabs are not allowed.
  • Docstrings:
    • All public modules, functions, and methods must have a docstring.
    • Use triple-double quotes ("""Docstring goes here""") for docstrings.
    • Function docstrings should clearly describe the function's purpose, arguments, and return value.

Typing and Type Safety

  • Type Hinting: All function signatures, including arguments and return values, must have type hints from Python's typing module.
  • Runtime Type Checking: The @beartype decorator must be applied to all functions to enforce runtime type safety.
  • Type Aliases: Use the t alias for the typing module (e.g., import typing as t).

Function and API Design

  • Core Logic: The main logic is centered around the llml function, which is designed to be recursive. When adding new features, maintain this recursive pattern.
  • Immutability: The llml function should be pure and not modify its inputs. It should return a new string as the result.
  • Keyword Arguments: When calling functions, prefer keyword arguments for clarity, especially for functions with multiple parameters.

Read the full file on GitHub · 65 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. today First seen · 65 lines · 0 tokens per session scan A 85d7dad04b95

Subscribe to this mod's changes

py is a cursor rule published in the GitHub repository zenbase-ai/llml (72 stars, last pushed 1y ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 843 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 2026-09-01.