yolo-expert

PassAudited by VirusTotal on May 7, 2026.

Overview

Type: OpenClaw Skill Name: yolo-expert Version: 1.0.2 The 'yolo-expert' skill is a legitimate tool designed to provide technical support for the Ultralytics YOLO framework. It instructs the agent to clone the official GitHub repository (https://github.com/ultralytics/ultralytics.git) to a local directory (/root/ultralytics) and use the source code as a reference for answering user queries. The skill contains no evidence of malicious intent, data exfiltration, or harmful prompt injection, and its behavior is strictly aligned with its stated purpose of providing expert YOLO documentation.

Findings (0)

Artifact-based informational review of SKILL.md, metadata, install specs, static scan signals, and capability signals. ClawScan does not execute the skill or run runtime probes.

What this means

Answers or examples may depend on the latest upstream Ultralytics code, and running the setup writes a local repository checkout.

Why it was flagged

The skill documents cloning and updating an external GitHub repository without pinning a specific commit or version. This is purpose-aligned for source-code reference, but the reviewed source can change over time.

Skill content
git clone --depth 1 https://github.com/ultralytics/ultralytics.git /root/ultralytics

cd /root/ultralytics && git pull
Recommendation

Approve clone or pull commands before running them, and pin a trusted Ultralytics version or commit if reproducibility matters.

What this means

If these examples are run, they may create or replace local output files and use substantial CPU/GPU time.

Why it was flagged

The skill provides examples that can write files, overwrite existing experiment outputs, export model artifacts, and consume significant compute when executed. These operations are expected for YOLO workflows but should be user-directed.

Skill content
results = model.train(... project="runs/detect", name="train", exist_ok=True, ...)
results[0].save_txt("labels/")
results[0].save_crop("crops/")
model.export(format="onnx", imgsz=640, half=True, opset=12)
Recommendation

Run training, export, and save operations only on intended datasets and paths, and avoid overwrite options unless the user confirms.