Skill UI

A comprehensive framework for advanced sensing utilizing WiFi signals. It implements multistatic radar sensing, cross-viewpoint data fusion, RF tomography for volumetric imaging, and persistent field modeling. Features include pre-movement intention detection, biomechanics drift tracking, and robust mesh security hardening for research-grade deployments.

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 set of programmatic interfaces for RuView's Wi-Fi sensing capabilities, including the CLI binary, REST API, and WebAssembly builds. This toolkit allows developers to integrate sophisticated disaster survivor detection and signal analysis into various environments, such as scripting, web browsers, and edge devices (ESP32-WASM3). Ideal for systems requiring cross-platform, scalable sensing data streams.

A comprehensive guide for configuring and deploying the RuView wireless sensing system. It covers firmware build-time settings (sdkconfig), runtime parameters (NVS provisioning), multi-node mesh setup, and integrating advanced edge intelligence modules (ADR-041). Essential for tuning system behavior and capabilities without modifying core code.

A comprehensive guide for bringing RuView CSI sensing nodes (ESP32-S3/C6) online. This skill covers the full lifecycle: building the embedded firmware, flashing the device, provisioning WiFi credentials, and confirming the Continuous Sensing Image (CSI) data stream. Essential for physical hardware deployment and debugging of complex sensing systems.

This tool provides the framework for setting up and running mmWave/FMCW radar (60 GHz, 24 GHz) alongside WiFi CSI. It performs advanced sensor fusion, reconciling radar-derived vitals (heart rate, breathing rate) with CSI data to generate unified 48-byte fused vital records. It is suitable for developing high-precision health monitoring systems and volumetric spatial models.

A comprehensive guide detailing the entire model lifecycle for RuView. It supports multiple advanced tracking methods including camera-free pose estimation (WiFlow), camera-supervised methods using MediaPipe, robust contrastive embeddings (RuVector), and domain generalization (MERIDIAN). Covers training, evaluation, and deployment across local GPU and cloud platforms.

This guide provides a comprehensive onboarding process for RuView (WiFi-DensePose). It details three deployment tiers: Docker simulation for quick testing and UI exploration, source code build verification (Rust/Python), and live sensing setup utilizing ESP32 hardware. Ideal for new users or rapid prototyping.

A comprehensive skill for integrating agentic operational flows into the RuView sensing ecosystem (RVF). It allows external agents to read, reason about, and respond to real-time cross-component sensing data, including BFLD events, vitals, and pose estimation. Use cases include automated caregiving responses, complex event negotiation, and maintaining auditable agent state within the sensing data stream.

This comprehensive tool provides a multi-stage verification pipeline for the RuView system. It executes full Rust workspace tests, runs a deterministic Python proof (Trust Kill Switch) to detect behavioral drift, and generates a detailed ADR-028 witness bundle. Use this before merging pull requests or when creating an official attestation package for recipients.

A comprehensive guide for preparing academic manuscripts in Criminology, focusing on data reproducibility and transparency. It details how to create data-availability statements, build master scripts, and handle restricted criminal-justice datasets (such as juvenile records) to meet modern open-science standards.

This tool converts various academic citation formats (e.g., APA, Harvard) into the specific Vancouver/ICMJE numbered style required by the New England Journal of Medicine (NEJM). It ensures citations are numbered sequentially by appearance, handles limited author lists (first 6 + et al.), uses NLM abbreviations for journals, and performs a comprehensive evidence audit, ensuring the manuscript adheres to the strict standards of high-impact medical publishing.