ClawHub Security Signals: A Coding Guide to End-to-End Security Signal Analysis and Verdict Classification on the AI Skills Dataset

In this tutorial, we use the ClawHub Security Signals dataset to examine how different security scanners assess AI skills and related files. We load the dataset directly from the Hugging Face Parquet conversion to avoid compatibility issues with newer dataset metadata, then inspect the main columns, verdict distribution, scanner outputs, and severity labels. After exploring scanner disagreement and overlap patterns, we build a practical machine learning pipeline that combines SKILL.md text with numerical scanner signals to predict the final ClawScan verdict. It gives us a complete workflow for loading, analyzing, visualizing, and modeling security signal data in a Colab-ready environment.

Setting Up the Colab Environment and Imports for Security Signal Analysis

!pip -q install -U "huggingface_hub>=0.23" pyarrow scikit-learn pandas numpy matplotlib seaborn

import warnings, numpy as np, pandas as pd

warnings.filterwarnings("ignore")