project

Project rules for humanproof, a system that identifies patterns in how players control games to help detect AI use in competitive gaming.

In plain words
What is it for?
Guiding feature work, exports, tests, command-line tests, and release-note updates in the humanproof project.
Why use it?
They keep new code consistent with the project’s Python, typing, linting, documentation, testing, and change-log requirements.

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/sandeep-alluru/humanproof/project
Clone the repo
git clone --depth 1 https://github.com/sandeep-alluru/humanproof

Made for: Cursor.

Per session 223 This file is loaded in full into every session.
When invoked 223 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin 83% copy Near-identical to another mod 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 $0.00223 $0.00223
Opus 5 $0.00112 $0.00112
Sonnet 5 $0.00045 $0.00045
Haiku 4.5 $0.00022 $0.00022

Measured yesterday against content hash d909520806ea, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

project 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.

Origin

This is a copy

83% identical to project — 8 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.cursor/rules/project.mdc · 33 lines

What it actually says

humanproof — Cursor Rules

What this project does

Motor-noise fingerprinting for AI detection in competitive games

Module map

src/humanproof/
├── # TODO: fill in after implementation

Invariants — never break these

  • TODO: list invariants

Code style

  • Python 3.10+, fully type-annotated, mypy strict mode
  • Ruff lint rules: E W F I UP B S N SIM RUF PT
  • No print() in library code — use rich.console.Console
  • All public functions and classes must have docstrings
  • Tests: pytest, CliRunner for CLI tests

When adding a new feature

  1. Implement in the appropriate module
  2. Export from init.py, add to all alphabetically
  3. Add tests in tests/test_.py
  4. Update CHANGELOG.md under [Unreleased]
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 · 33 lines · 223 tokens per session scan A d909520806ea

Subscribe to this mod's changes

project is a cursor rule published in the GitHub repository sandeep-alluru/humanproof (0 stars, last pushed 14d ago), licensed MIT. It adds 223 tokens to every session, about $0.0011 per session on Opus 5. A static security scan graded it A with 0 findings. It is 83% identical to project, differing in 8 lines, and is treated as a copy.