Skill UI

This tool provides comprehensive integration for Reolink security cameras, supporting both RTSP streaming and HTTP API access. It allows users to discover cameras via ONVIF, capture live streams, take snapshots, and even capture motion-triggered video clips. It is ideal for developers building robust, networked surveillance or monitoring systems.

This skill provides comprehensive integration for TP-Link Tapo cameras (models like C200, C310, etc.). It supports streaming via RTSP and ONVIF protocols, allowing users to discover cameras on the network, capture motion-triggered video clips, and maintain stable live video feeds. Setup requires basic network credentials and Python environment setup.

This skill provides real-time monocular depth estimation using advanced models like Depth Anything v2. It transforms live camera feeds by generating colorized depth maps, enabling depth overlays and detailed scene understanding. Crucially, it supports a 'privacy mode' that anonymizes visual identities by using depth-only transformation, preserving only the spatial layout and movement patterns for secure monitoring purposes. Optimized for both CoreML on macOS and PyTorch on other platforms.

This utility enables multi-camera streaming by converting standard camera protocols (RTSP, RTMP, HTTP) into low-latency WebRTC streams using the go2rtc sidecar. It is essential for building real-time surveillance dashboards, live video monitoring systems, or integrating diverse IP cameras into a single web interface.

This bridge provides bidirectional integration between Home Assistant (HA) and advanced AI pipelines. It streams HA camera feeds into the AI system for sophisticated analysis (detection, VLM, REID). Conversely, the analysis results—such as detected objects, descriptions, and person identities—are sent back to HA as image_processing entities, enabling powerful automation and smart notifications.

This benchmark evaluates Vision-Language Models (VLMs) on detecting potential safety hazards within indoor environments. It uses static camera frames, simulating real-world fixed security camera monitoring. The test covers 40 scenarios across 5 critical categories, including fire/smoke, electrical risks, trips/falls, child safety, and falling objects, providing a rigorous measure of VLM's practical safety inspection capability.

This skill provides high-performance, real-time object detection utilizing the Google Coral Edge TPU accelerator. It processes live camera frames to identify up to 80 COCO classes (e.g., person, car, dog). Optimized for native macOS and Linux deployment, it achieves extremely low latency (e.g., ~4ms at 320x320), making it ideal for embedded vision systems, surveillance, and low-latency monitoring applications.

This skill provides real-time object detection capabilities by leveraging the Google Coral Edge TPU accelerator. It operates natively within Windows Subsystem for Linux (WSL), allowing high-speed, low-latency inference on live camera frames. By utilizing hardware acceleration, it achieves impressive performance (e.g., ~4ms inference at 320x320), making it ideal for edge computing projects requiring rapid visual analysis and resource efficiency.

A powerful, provider-agnostic skill dedicated to generating high-quality, single-image assets such as posters, key art, and editorial illustrations. This tool utilizes a structured, multi-step prompt composition workflow (Subject, Lighting, Palette, Camera) to ensure cinematic and artistic consistency, regardless of the underlying AI model (Flux, Imagen, Midjourney). Ideal for designers needing professional-grade visual concepts.

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.

A comprehensive guide for preparing accepted research papers for final submission to major conferences (e.g., AAAI Press). This process covers strict template adherence, deanonymization of author details, managing copyright transfer, optimizing page counts (including purchasing extra technical pages), and ensuring all source files (LaTeX/Word, figures, bibliography) are correctly formatted for professional publication.

This guide provides a comprehensive project management framework for submitting research to the AAAI conference. It maps out every critical milestone, from initial topic selection and abstract submission, through the two-phase peer review, writing the rebuttal, and preparing the final camera-ready version and public artifacts. It emphasizes backward planning from hard deadlines and maintaining evidence integrity throughout the rigorous academic lifecycle.