bg-remover
v1.0.0Remove, replace, or blur image backgrounds using AI-powered segmentation (rembg/U2-Net). Use when the user asks to: (1) remove image background / make transp...
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byJianghua@jxhgzs
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/description (background removal, replacement, blur) match the provided files: a Python CLI (scripts/bg_remove.py) that implements remove/replace/blur and an install script to install rembg and its dependencies. Required resources (rembg, Pillow, numpy, onnxruntime) are appropriate for the task.
Instruction Scope
SKILL.md instructs running the included install.sh and the bg_remove.py commands; the script only reads input image files and writes PNG outputs. The SKILL.md and script explicitly note a one-time model download (~170MB). There are no instructions to read unrelated files, environment variables, or send processed data to external endpoints.
Install Mechanism
Installation is via the included scripts/install.sh which runs pip install rembg Pillow numpy onnxruntime (PyPI). This is a standard package install from PyPI; there are no downloads from arbitrary URLs, URL shorteners, or archive extraction from unknown hosts in the repo.
Credentials
The skill declares no required environment variables, no credentials, and the code does not read any environment secrets. The requested dependencies are proportionate to the image-processing functionality.
Persistence & Privilege
The skill does not request permanent/always-on presence, does not modify other skills or system-wide configs, and is user-invocable only. It performs no privileged operations.
Assessment
This skill appears coherent and implements local image background removal. Before installing: (1) be aware the first run will download a ~170MB U2‑Net model and pip will fetch packages from PyPI (onnxruntime can be large); (2) run the install script inside a Python virtualenv to avoid polluting system packages; (3) review/approve network access if you are on a metered connection; (4) do not feed sensitive images unless you are comfortable with local processing and the rembg package’s behavior; and (5) if you need stricter isolation, consider running the script in a container or sandbox. Overall there are no signs of credential access or hidden exfiltration in the provided files.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.