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GPU Keepalive with KeepGPU
v1.0.0Install and operate KeepGPU for GPU keep-alive with both blocking CLI and non-blocking service workflows. Use when users ask for keep-gpu command constructio...
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MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
Name and description match the instructions: the SKILL.md explains installing KeepGPU, checking for GPUs, and running blocking or service modes. Required resources (CUDA/ROCm, PyTorch) are appropriate for a GPU keep-alive tool.
Instruction Scope
Runtime instructions are narrowly scoped to installing, starting, inspecting, and stopping KeepGPU; referenced commands and files (torch.cuda.device_count(), nvidia-smi, nohup/tmux, keepgpu.log, keepgpu.pid) are relevant to the stated task. There are no instructions to read unrelated user files or exfiltrate data.
Install Mechanism
The skill recommends pip installs from PyTorch's wheel index and either PyPI or a GitHub repo. These are expected for Python tooling, but pip installing directly from a Git URL will execute the package's install scripts on the machine — the user should trust the repository or prefer an official PyPI release or review the source before installing.
Credentials
No environment variables, credentials, or config paths are requested. The instructions only require local GPU drivers/runtimes and typical command-line tools, which are proportional to the functionality.
Persistence & Privilege
The skill does not request elevated platform privileges and 'always' is false. However, service/non-blocking usage will create background processes and may open a local dashboard port (127.0.0.1:8765); users should be aware these processes persist until stopped and may conflict with cluster policies.
Assessment
This skill appears to do what it says: install and run KeepGPU. Before installing, consider: (1) prefer the published PyPI release if available; (2) if you use pip install from the GitHub URL, review the repository (setup scripts and entry points) because pip install from a remote repo runs code on your machine; (3) run installs in a virtualenv or container if you are unsure; (4) be aware that service mode spawns persistent background processes and exposes a local dashboard on port 8765 — ensure this fits your environment and cluster policies; (5) verify the repository owner/maintainer and check for recent activity or issues if you will install on a production node.Like a lobster shell, security has layers — review code before you run it.
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License
MIT-0
Free to use, modify, and redistribute. No attribution required.