Download bundle/README.md from earakely-scale/MousepadComputerEnv-REA: direct link, hf CLI and curl.
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https://huggingface.co/datasets/earakely-scale/MousepadComputerEnv-REA/resolve/main/bundle/README.md
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hf download hf://datasets/earakely-scale/MousepadComputerEnv-REA/bundle/README.md
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curl -L -o README.md https://huggingface.co/datasets/earakely-scale/MousepadComputerEnv-REA/resolve/main/bundle/README.md
Mousepad computer app
Exported by REA's export-desktop-application from a capture of the mousepad package
(capture Evidence ev_22dd18d1ecd38dbb96e8ab160a67bdc00a95aa24a6af4da49f87db3f31bb3669). It was checked against agentenv-framework
0.9.1431.
envs/mousepad/is a computer app on theubuntu-24.04base with thereferencedriver, foramd64andarm64. It installsmousepadfrom the distribution archive, launchessh -c exec 'mousepad' "$HOME"/'Documents/todo.txt'at boot and is ready when a window of classMousepadappears.- Owned paths:
~/.config/Mousepad,~/Documents.data/getreturns their files and a typed JSON view of each;data/resetrestores the seed. - Seed: 1 file(s) from the user's home before the capture, with the captured home path respelled in text files.
tasks/smoke.jsondeploys the app.
Run the task with agent-env run <this folder>, or only the deploy with --task smoke.
The seed is synthetic test data made for this showcase; it holds no one's real files.
Task add-milk
tasks/add-milk.json deploys the app, has the agent work through the computer from the prompt below, and grades
the outcome with artifacts/add-milk-check/verify.py, which reads the app's data/get views and needs
no model. evals/mousepad.toml names it, so a run of the bundle runs it.
Add buy milk as a new line at the end of the to-do list open in Mousepad, then save it.
Checks, each of which must pass:
milk:{"kind":"lines_present","file":"Documents/todo.txt","lines":["buy milk"]}bread:{"kind":"lines_present","file":"Documents/todo.txt","lines":["buy bread"]}one-milk:{"kind":"line_count","file":"Documents/todo.txt","exact":"buy milk","equals":1}
REA kept the task because its checks fail on the seed (milk, one-milk) and all
pass on the captured end state.
To measure a solve rate, run the task k times (3 by default) with the agent and model to measure, and count the runs
whose summary line says passed; a run that says failed did not finish and is run again rather than counted:
agent-env run <this folder> --task add-milk --agent claude-code --model MODEL