Skill UI

Analyzes, plans, and executes upgrades for the Hex SDK, providing detailed detection of breaking changes, deprecations, and API version updates. Use this skill when migrating data pipelines or backend code to ensure compatibility with the latest Hex API standards.

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 guide provides a comprehensive strategy for configuring Klaviyo across development, staging, and production environments. It covers best practices for using separate API keys, implementing robust environment detection logic (like reading NODE_ENV), and utilizing secret management solutions (AWS/GCP/Vault). Crucially, it teaches how to implement 'guards' to prevent accidental real-world actions, such as sending live emails or deleting data, in non-production environments, ensuring data integrity and smooth CI/CD deployment.

This comprehensive skill manages the entire computer vision pipeline, from custom dataset annotation to model deployment. Users can fine-tune YOLO models on proprietary COCO-formatted datasets using an agent-driven workflow. Key features include hardware-aware training (supporting CUDA, MPS, ROCm), automatic export to optimized formats (TensorRT, CoreML, OpenVINO, ONNX), and seamless, one-click deployment as an active detection skill.

A guide to building resilient software integrations when dealing with external APIs that lack formal versioning, changelogs, or guaranteed backward compatibility. Learn defensive coding patterns, implementing schema drift detection via CLI tools (curl, jq), using fallback field names, and executing gradual feature flag rollouts to maintain production stability.

This comprehensive checklist provides a deep dive into Endpoint Detection and Response (EDR) architecture and monitoring mechanisms. It details advanced evasion techniques, such as direct syscalls, AMSI bypass, kernel patching, and memory manipulation. This skill is essential for red team engagements, penetration testing, and advanced malware research to thoroughly assess and bypass modern security defenses.

This comprehensive guide serves as a reference for offensive security professionals developing custom x86/x64 shellcode. It details advanced concepts including Position-Independent Code (PIC), PEB traversal for API resolution, safe memory allocation (avoiding RWX), and sophisticated process injection methods (e.g., APC injection, threadless techniques). Learn how to bypass modern AV/EDR detection using advanced evasion techniques like API hashing, encryption, direct syscalls, and abusing PE structures.

This utility addresses the lack of webhooks for OneNote resources. It implements an efficient change detection mechanism by performing timestamp-based polling and delta query comparisons against the Microsoft Graph API. It is essential for building real-time synchronization, change monitoring, or event-driven integrations for OneNote, while carefully managing API rate limits.

This skill provides the OWASP Top 10 for Agentic Applications 2026 (ASI) framework for classifying security risks in AI agent systems. It maps detection findings to standardized risk categories like goal hijacking, tool misuse, and identity abuse, enabling consistent security assessment and remediation prioritization for agentic workflows.

This skill manages comprehensive identity verification using the Persona API. It covers government ID checks (passport, driver's license), selfie liveness detection, and database lookups (SSN, watchlist). Use this when implementing Know Your Customer (KYC) workflows, reviewing detailed verification results, or building custom approval logic based on multi-source identity data.

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.

This skill provides a comprehensive framework for security auditing LLM agents that possess external tools (file system access, command execution, network calls). It employs advanced dialogue probing techniques—such as command injection, path traversal, and SSRF attempts—to identify vulnerabilities where the agent might be induced to execute unexpected or malicious code, or access sensitive resources. Essential for red teaming and securing agent functionalities.