Skill UI

Qiskit is the world's leading open-source framework for building, optimizing, and executing quantum circuits. It provides comprehensive capabilities for quantum error mitigation, running algorithms on diverse hardware backends (including IBM Quantum and simulators), and supporting applications in chemistry and machine learning. Essential for researchers entering the quantum computing field.

GeoMaster is a comprehensive geospatial science skill that guides remote sensing, GIS, spatial analysis, and machine learning workflows for Earth observation and planetary data projects, covering satellite imagery, vector/raster math, point clouds, terrain, hydrology, and cloud-native data stacks.

Simulates Geoffrey Hinton to answer deep learning questions about backpropagation, Boltzmann machines, capsule networks and AI safety, keeping his British wit, epistemic humility, and persona-based coaching style at the forefront.

Processes machine learning experiment outputs to build comparison tables, compute statistics, and surface insights with recommendations for follow-up trials whenever users ask to analyze results or make comparisons.

Deploys machine-learning anomaly detection for OT environments by modeling SCADA polling behaviors, profiling Modbus/DNP3/OPC UA traffic, and correlating network anomalies with historian process data to spot rogue devices and unusual process deviations.

Detects anomalous authentication patterns across identity logs by combining UEBA, statistical baselines, geo intelligence, and machine learning to flag impossible travel, brute-force, credential stuffing, and other compromised account behaviors for SOC or IAM teams.

Detects unauthorized DNP3 control commands, protocol violations, and baseline deviations via deep packet inspection and machine learning, fitting SCADA/energy IDS deployments monitoring RTUs and substations.

A comprehensive tool for generating publication-quality figures essential for Machine Learning and AI academic papers. It supports two distinct workflows: generating complex system architecture and workflow diagrams using AI (Gemini), and creating precise, data-driven visualizations (line charts, heatmaps, etc.) using standard libraries like matplotlib and seaborn. Use it when preparing any figure for a conference submission.

Trackio is a robust library designed for comprehensive monitoring and management of Machine Learning experiments. It allows users to log key training metrics, set structured alerts (e.g., loss divergence, NaN gradients), and visualize results over time. The system supports integration via Python API for logging/alerting, and a powerful CLI for programmatic metric retrieval and dashboard synchronization to Hugging Face Spaces. Ideal for autonomous AI agent workflows and detailed research tracking.

This skill provides comprehensive command-line capabilities for interacting with Arize datasets. Use it to perform dataset CRUD operations (Create, Read, Update), list available datasets, retrieve detailed metadata, and export large volumes of example data. It is essential for managing the lifecycle of versioned datasets used in model evaluation and machine learning experimentation. Supports file formats like CSV, JSON, and Parquet.