# Show HN: Mousecrack – Bypass agent mouse detection with deep learning

> Source: <https://github.com/puffinsoft/mousecrack>
> Published: 2026-07-26 06:46:21+00:00

Synthesize organically varied, human-like mouse movement.

This project aims to test the abilities of deep-learning for mouse imitation.

## demo.mp4

*Clearly, it's not perfect. But this is v0.1.0. Still a lot of fun stuff to try on the model side :).*

```
npm i -g mousecrack
```

Warning

This project is still experimental! For educational purposes only.

Available as an SDK (for developers) and CLI (for agents).

``` js
import { move, steps } from 'mousecrack';

await move(200, 400);

// or alternatively...
const from = { x: 100, y: 200 }
const to = { x: 200, y: 400 }
await steps(from, to);

// [
//   { x: 100, y: 200, t: 0 },
//   { x: 95, y: 202, t: 10.528131778472712 },
//   { x: 90, y: 210, t: 21.040190062833986 },
//   { x: 81, y: 223, t: 31.892832399406224 },
//   ...
mousecrack move 200 400 # (x, y)
mousecrack steps 100 200 200 400 # from (x, y), to (x, y)
```

## Install the Skill

For Claude Code:

```
/plugin marketplace add puffinsoft/mousecrack
/plugin install move-mouse@mousecrack
```

For Codex:

```
codex plugin marketplace add puffinsoft/mousecrack
codex plugin add move-mouse@mousecrack
```

Mousecrack treats mouse prediction like a time forecasting problem.

It models mouse movement as a change in position (`dx, dy`

) and time (`dt`

), and tries to predict the next step in this multivariate time series.

To avoid the [mode collapse](https://en.wikipedia.org/wiki/Mode_collapse) problem, Mousecrack uses a Mixture Density Network to model several trajectories as a probability distribution.

*Mousecrack* is open source software, licensed under the [MIT](/puffinsoft/mousecrack/blob/master/LICENSE) license.