Skill UI

Tetragon, a CNCF project under Cilium, provides advanced, kernel-level runtime security observability and policy enforcement in Kubernetes clusters. By utilizing eBPF, it monitors process execution, file access, and network connections with minimal overhead, enabling detection and prevention of container escapes, unauthorized access, and suspicious activity.

This guide details comprehensive physical security penetration testing techniques, essential for red teaming engagements. It covers methods such as tailgating, badge cloning (RFID), lock bypassing, and deploying rogue network devices (e.g., Pineapple, LAN Turtle). It provides practical toolkits and MITRE ATT&CK mapping to evaluate an organization's physical and digital control weaknesses.

A comprehensive guide and workflow demonstrating how to use YARA rules for proactive threat hunting. This method helps detect unknown malware, suspicious patterns, and Indicators of Compromise (IOCs) across filesystems, memory dumps, and network artifacts, making it essential for incident response and security analysis.

This skill provides guidance on implementing common architectural patterns and best practices within the PyTorch framework. It is designed for developers building sophisticated deep learning models, helping to improve structure, efficiency, and code maintainability when working with neural networks.

PennyLane is a versatile, hardware-agnostic library for quantum computing. It enables the training of quantum circuits, including quantum neural networks, via automatic differentiation. It supports building hybrid quantum-classical models and integrates seamlessly with major classical ML frameworks like PyTorch and TensorFlow, making it ideal for variational algorithms (VQE, QAOA) and quantum chemistry simulations.

PyTorch Lightning is an advanced deep learning framework built on PyTorch. It simplifies the entire ML workflow, allowing users to organize models into LightningModules, automate training loops using the Trainer, and manage complex data pipelines with DataModules. It supports advanced features like multi-GPU/TPU scaling (DDP, FSDP), integrated logging (W&B, MLflow), and custom callbacks, making large-scale neural network development efficient and reproducible.

An AI-powered tool designed to transform complex scientific concepts into publication-quality visual diagrams. It uses advanced AI models for generation, specializing in neural network architectures, biological pathways, system flowcharts, and complex schematics. Features smart iterative refinement and quality review based on the target document type (e.g., journal, thesis, poster), ensuring the highest academic standard.

PyG is the standard, powerful library for building Graph Neural Networks (GNNs) atop PyTorch. It provides specialized data structures for nodes, edges, and graphs, supporting various tasks like node classification, graph classification, and link prediction. Users can implement advanced models using built-in layers (GCN, GAT, GraphSAGE) and handle complex structures like heterogeneous graphs efficiently. It is essential for deep learning research and industrial applications involving graph data.

TorchDrug is a comprehensive PyTorch-based machine learning toolbox specializing in computational chemistry and drug discovery. It enables users to apply sophisticated Graph Neural Networks (GNNs) to tackle complex tasks, including molecular property prediction, protein structure modeling, knowledge graph reasoning, molecular generation, and retrosynthesis planning. It is highly valuable for researchers working with SMILES, PDB, and biomedical datasets.