Skill UI

This skill utilizes AI/LLM reasoning capabilities combined with industry-standard OSINT tools (Sherlock, theHarvester, SpiderFoot). It correlates fragmented data—including usernames, emails, domain records, and breach data—from multiple sources into unified, confidence-scored intelligence profiles. It is ideal for deep target profiling, identity resolution, and mapping complex threat landscapes.

Assesses AI/ML systems for prompt injection, jailbreak, model inversion, data poisoning, and agent tool abuse using MITRE ATLAS mapping, signature detection, and scoring metrics before deployment.

PyHealth is a modular Python toolkit designed for developing deep learning pipelines in clinical and healthcare settings. It provides a standardized 5-stage framework (Dataset → Task → Model → Trainer → Metrics) to handle complex, multi-modal data, including EHR records, physiological signals, and medical imaging. Use cases include predicting mortality, readmission risks, recommending drugs, or performing medical code mapping (ICD/ATC).

A comprehensive suite for running advanced sensing applications using RuView hardware. It supports multi-modal data fusion from CSI, Wi-Fi, and mmWave sensors to perform critical tasks such as occupancy detection, vital sign monitoring (heart rate, breathing), pose estimation, environment mapping, and mass casualty assessment. Ideal for prototyping sophisticated IoT and smart building solutions.

A comprehensive guide for academic authors detailing how to establish and maintain rigorous reproducibility in Machine Learning research papers (e.g., ICLR). It instructs on mapping every central claim to verifiable evidence, including recording seeds, variance, specific compute budgets (training vs. inference), data preprocessing scripts, and ethical considerations. The statement must function as a public contract for the scientific community.