GitHub - puffinsoft/mousecrack: Human mouse imitation with deep learning. · GitHub
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puffinsoft
mousecrack
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.claude-plugin
.claude-plugin
.codex-plugin
.codex-plugin
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assets
inference
inference
skills/move-mouse
skills/move-mouse
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train
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.gitignore
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Synthesize organically varied, human-like mouse movement.
This project aims to test the abilities of deep-learning for mouse imitation.
See it in Action
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 :).
Installation
npm i -g mousecrack
Usage
Warning<br>This project is still experimental! For educational purposes only.
Available as an SDK (for developers) and CLI (for agents).
SDK
import { move, steps } from 'mousecrack';
await move(200, 400);
// or alternatively...<br>const from = { x: 100, y: 200 }<br>const to = { x: 200, y: 400 }<br>await steps(from, to);
// [<br>// { x: 100, y: 200, t: 0 },<br>// { x: 95, y: 202, t: 10.528131778472712 },<br>// { x: 90, y: 210, t: 21.040190062833986 },<br>// { x: 81, y: 223, t: 31.892832399406224 },<br>// ...
CLI
mousecrack move 200 400 # (x, y)<br>mousecrack steps 100 200 200 400 # from (x, y), to (x, y)
Install the Skill<br>For Claude Code:
/plugin marketplace add puffinsoft/mousecrack<br>/plugin install move-mouse@mousecrack
For Codex:
codex plugin marketplace add puffinsoft/mousecrack<br>codex plugin add move-mouse@mousecrack
How does it work?
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 problem, Mousecrack uses a Mixture Density Network to model several trajectories as a probability distribution.
Mousecrack is open source software, licensed under the MIT license.
About<br>Human mouse imitation with deep learning.<br>Resources<br>Readme<br>MIT license<br>Activity<br>Custom properties<br>Stars<br>1 star<br>Watchers<br>0 watching<br>Forks<br>0 forks<br>Report repository
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