Skill UI

Achieve complete integration with Amazon Alexa to build sophisticated, intelligent voice skills. This system transforms a standard Alexa device into an ultra-intelligent assistant by utilizing Claude as the core LLM brain, while deeply integrating with the entire AWS ecosystem (Lambda, DynamoDB, Polly, Smart Home). Ideal for complex, specialized voice interaction applications.

This skill integrates Aegis detection events directly into Home Assistant. By firing specific `aegis_detection` events, it allows users to automate actions in their smart home setup. Users can configure automations to automatically turn on lights, send notifications, or execute complex routines based on detected security activity. Requires HA credentials for secure connection.

A comprehensive evaluation suite designed to benchmark the performance of Large Language Models (LLM) and Vision-Language Models (VLM) specifically for home security applications. It covers 143 diverse tests across 16 critical domains, including tool use, prompt injection resistance, contextual reasoning, alert triage, and detailed scene analysis, ensuring robustness for smart home AI systems.

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 utility publishes various Aegis-related events (such as person detection, alerts, and clip completion) to an MQTT broker. It is designed for seamless integration with smart home and automation platforms like Home Assistant or Node-RED, enabling real-time, event-driven monitoring and system linkage.

This comprehensive benchmark suite evaluates the performance of Vision-Language Models (VLM) on complex video anomaly detection tasks using the SmartHome-Bench dataset. It covers a wide range of real-world smart home scenarios, including security breaches, elderly fall detection, and unusual pet behavior. Unlike single-frame analysis, this benchmark requires deep multi-frame video understanding, making it a robust tool for testing the advanced AI capabilities of video monitoring systems.

Covers offensive workflows for Zigbee, Thread, and Matter mesh networks, including 802.15.4 sniffing, commissioning abuse, key capture, replay/injection, and control-command exploitation in smart-home deployments.

A comprehensive guide detailing attack methodologies for Z-Wave smart home networks. Techniques covered include sniffing during inclusion (S0/S2), exploiting key derivation flaws, performing replay attacks on unauthenticated nodes, and crucial hub compromise (pivoting) to gain full control over the entire mesh network. Essential reading for IoT penetration testing.