Mouse YOLO Factory

Generate simulated scratch defects, run YOLO model inference with auto-labeling, and merge mouse product defect image datasets with version control.

What to check before installing or running: - The code implements the stated features, but the SKILL.md commands and some internal paths are hard-coded to D:/... — make sure you understand and relocate those paths to directories you control before running. - The scripts expect heavy Python dependencies (torch, ultralytics, torchvision, opencv, pandas, numpy). The skill metadata does not declare these — install them in a controlled environment (preferably a virtualenv or container). - The inference script writes detection logs to D:/aiagent/rag_database/detection_logs.jsonl and writes output images/labels into dataset folders. Inspect the logs if they may contain sensitive filenames or metadata. - Run the code first in an isolated environment (container or VM) and review/modify the hard-coded paths and any file-write locations. Search the code for any other absolute paths before trusting it with production data. - If you need tighter guarantees, ask the publisher to: remove hard-coded paths, declare required dependencies, and document exactly what files will be created/modified and where. - Confidence is medium: nothing in the code indicates network exfiltration or obfuscated/malicious behavior, but the path assumptions and missing dependency declarations are implementation issues that could lead to accidental data exposure or file overwrites.

Type: OpenClaw Skill Name: mouse-yolo-factory Version: 1.0.0 The skill bundle is a specialized toolset for YOLO-based defect detection on computer mice, including synthetic scratch generation, automated labeling, and dataset management. The code utilizes standard libraries such as OpenCV, NumPy, and Ultralytics for image processing and model inference. While it contains hardcoded file paths (e.g., D:/aiagent/...) and performs file deletions (shutil.rmtree) as part of its dataset merging logic, these actions are consistent with the stated purpose and show no signs of malicious intent, data exfiltration, or unauthorized remote access.