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Segment Anything

v1.0.0

Use SAM (Segment Anything Model) to remove image backgrounds and extract foreground subjects as transparent PNGs. Use when users want to remove backgrounds,...

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by@scikkk
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
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Benign
high confidence
✓
Purpose & Capability
The name/description (SAM background removal) matches the code and declared dependencies: python3, pillow, numpy, torch, torchvision, and the segment_anything package. The script implements segmentation and saving transparent PNGs as advertised.
✓
Instruction Scope
SKILL.md simply instructs running scripts/segment.py and documents parameters. The runtime behavior (auto-installing segment_anything via pip and auto-downloading model checkpoints to ~/.cache/sam) is clearly described. The instructions do not read unrelated files, environment variables, or transmit data to unexpected endpoints.
ℹ
Install Mechanism
Install spec lists pillow, numpy, torch, torchvision (appropriate for SAM). The script may auto-run pip install git+https://github.com/facebookresearch/segment-anything.git if needed and downloads large model checkpoints from dl.fbaipublicfiles.com (Meta's public hosting). This is expected but involves dynamic code download and large network transfers (~375MB–2.5GB).
✓
Credentials
No environment variables, credentials, or unrelated config paths are requested. The script writes checkpoints to ~/.cache/sam and saves outputs where the user specifies; those are proportionate to the function.
✓
Persistence & Privilege
always is false and the skill does not modify other skills or system-wide settings. It stores model checkpoints in the user's cache directory only, which is reasonable for repeated use.
Assessment
This skill appears to be what it claims, but it will: (1) auto-install the segment_anything package from GitHub at runtime, and (2) download large model checkpoints (~375MB–2.5GB) to ~/.cache/sam. Before installing, ensure you have sufficient disk space and bandwidth and that you trust pulling code from the segment-anything GitHub repo. If you prefer tighter control, pre-install the dependencies and provide a local checkpoint via --checkpoint to avoid runtime pip installs and downloads. Run in an environment where large native packages (torch) are supported (and consider GPU/CUDA compatibility) or in an isolated sandbox if you want to limit risk.

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.
Termshttps://spdx.org/licenses/MIT-0.html

Runtime requirements

Binspython3

Install

uvuv tool install pillow
uvuv tool install numpy
uvuv tool install torch
uvuv tool install torchvision

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