ClawHub

Image Cropper

v1.0.0

Crop objects from images using bounding box annotations in COCO, YOLO, VOC, or LabelMe formats with optional padding and batch processing.

⭐ 0· 249·4 current·4 all-time
byMingo_318@mingo-318
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
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Benign
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OpenClawOpenClaw
Benign
high confidence
✓
Purpose & Capability
Name/description match the included script and SKILL.md. The tool only requires Pillow (documented in SKILL.md) and operates on local image and annotation files (COCO/YOLO/VOC/LabelMe).
✓
Instruction Scope
SKILL.md instructs running the included script with local paths and options (padding, objects, format). The instructions do not ask the agent to read unrelated system files, environment variables, or to transmit data externally. Note: the printed code excerpt in the manifest appears truncated in the prompt display, but the file manifest indicates a full script is present — verify the full file if you plan to install.
✓
Install Mechanism
No install spec; SKILL.md recommends 'pip install pillow' which is appropriate for the script. No downloads from arbitrary URLs or archive extraction are present.
✓
Credentials
No environment variables, credentials, or config paths are required. The script does not access secrets or external services in the provided code.
✓
Persistence & Privilege
Skill does not request always:true or any elevated/system-wide privileges; it is user-invocable only and does not modify other skill configurations.
Assessment
This skill appears to be a straightforward local image-cropping utility. Before installing or running it: (1) verify the full scripts/cropper.py file in the package (the preview in the prompt looked truncated); (2) run it in an environment where input images/labels are from trusted sources (it reads and writes local files only); (3) install Pillow from PyPI in a virtual environment (pip install pillow); (4) if you need stronger assurance, quickly grep the script for network/socket/requests/imports to confirm there are no external endpoints or hidden code paths; and (5) consider running initial tests on a small dataset to confirm behavior and outputs.

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

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