Dataset Splitter
v1.0.0Split image datasets into train, validation, and test sets with options for random or stratified splits, custom ratios, and annotation support.
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byMingo_318@mingo-318
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 match the included script: splitter.py implements random and stratified splits, annotation handling, YOLO output structure, and stats. Required dependencies (Pillow referenced in SKILL.md) align with the script's 'stats' feature.
Instruction Scope
SKILL.md usage matches the script, but the script's default behavior is to move files (shutil.move) unless --copy is provided — this is potentially destructive and should be highlighted to users. Stratified splitting infers the class from the first token of a .txt annotation file (YOLO-style); that assumption isn't fully documented in SKILL.md and may not match all annotation formats.
Install Mechanism
No install spec (instruction-only skill) and SKILL.md recommends 'pip install pillow' which is a standard package; nothing is downloaded from arbitrary URLs or written to disk by an installer.
Credentials
The skill requests no environment variables, no credentials, and touches only paths the user supplies. There are no attempts to read unrelated config or secrets.
Persistence & Privilege
always is false and the skill does not request elevated or persistent platform privileges. It does not modify other skills or global agent settings.
Assessment
This skill appears to do exactly what it says: split image datasets into train/val/test sets. Before running it, back up your dataset or use --copy to avoid losing original files (the script moves files by default). Ensure your annotations are YOLO-style .txt files (the script reads the first token as the class id for stratified splits). Install Pillow only if you need the 'stats' command. No network calls or credential access were found, but review and test on a small dataset first if you're unsure.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.