Skill UI

Automates building and evaluating classification models by analyzing datasets, selecting features, tuning hyperparameters, and reporting metrics via the classification-model-builder plugin for supervised learning requests.

Aeon is a scikit-learn compatible toolkit for time series machine learning, covering classification, regression, clustering, forecasting, anomaly detection, segmentation, similarity search, and feature extraction on temporal data.

Query InterPro for protein family, domain, and GO annotations via a REST API with Python helpers, enabling function prediction, domain architecture mapping, and evolutionary classification insights for proteomic workflows.

A comprehensive skill for managing the entire lifecycle of datasets on the Hugging Face Hub. It allows users to initialize, configure, and stream updates for datasets of various types (Chat, Text Classification, Tabular). Crucially, it extends data handling capabilities by enabling powerful SQL-based querying and transformation directly on Hub datasets using the DuckDB engine, facilitating advanced data exploration and pipeline creation.

A sophisticated skill for detecting Command and Control (C2) traffic tunneled through DNS protocols. It utilizes advanced techniques like Shannon entropy analysis of subdomains, Machine Learning for DGA classification, and correlation with network logs from Zeek/Suricata. Ideal for SOC analysts and threat hunters investigating data exfiltration, DNS beaconing, or suspicious DNS activity.

Aeon is a comprehensive, scikit-learn compatible Python toolkit designed for advanced time series machine learning. It supports a wide range of temporal tasks, including classification, regression, clustering, forecasting, anomaly detection, and pattern recognition. Ideal for analyzing sequential data and extracting features from time-indexed observations.

This skill provides comprehensive guidance for classical machine learning using scikit-learn, the industry-standard Python library. It covers the full ML lifecycle, including supervised tasks (classification, regression), unsupervised tasks (clustering, dimensionality reduction), data preprocessing, robust model evaluation, and building scalable, production-ready ML pipelines.

This internal agent skill processes raw X/Twitter posts into structured bug candidates. It systematically performs parsing, content deduplication, PII redaction, reliability scoring, and classification. Finally, it clusters related reports using deterministic signatures, providing a highly organized output for efficient bug triage and analysis from large-scale social media streams.