Skill UI

A comprehensive skill for ingesting, analyzing, and manipulating multi-modal video and audio content. Supports real-time desktop capture, live stream monitoring (RTSP), and file uploads (URL/local). Capabilities include advanced indexing (visual, semantic, temporal), precise moment searching, professional timeline editing (subtitles, overlays, dubbing), transcoding, and generating actionable alerts. Ideal for content creators, monitoring systems, and complex media workflows.

A comprehensive skill for generating multimodal media content using fal.ai models. It supports text-to-image (Nano Banana), text/image-to-video (Veo, Kling, Seedance), and text/video-to-audio (CSM-1B, ThinkSound). Use this tool for any creative project requiring high-quality generated images, videos, or sound effects.

This guide details advanced methods for detecting and preventing QR code phishing (quishing) attacks. It covers how malicious URLs embedded in images bypass traditional email security filters, requiring multimodal AI, OCR, and robust mobile threat defense solutions. Essential for SOC analysts and security teams.

Provides a detailed, multi-platform workflow to monitor long-running computational experiments. It checks the status of jobs across various environments (SSH, vast.ai, Modal), collects screen logs, fetches structured JSON results, and pulls detailed training metrics (loss curves, accuracy) from Weights & Biases (W&B). Essential for MLOps and automated result review.

This comprehensive tool manages the complete lifecycle of ML experiments, providing robust deployment capabilities across diverse environments. It supports running training jobs on local GPUs, dedicated remote servers, Vast.ai, and serverless platforms like Modal. The workflow handles environment detection, code synchronization (rsync/git), dependency pinning, and integrates advanced logging tools (e.g., W&B), ensuring reliable, scalable, and reproducible ML training runs.

Transformers.js allows developers to run state-of-the-art machine learning models directly within JavaScript/TypeScript environments (browser, Node.js). It supports a comprehensive range of tasks including NLP (text generation, summarization, translation), Computer Vision (image classification, object detection), and Audio processing. This capability enables building fully client-side, multimodal AI applications without requiring a dedicated Python backend or server infrastructure.

Utilize Modal, a serverless GPU cloud, for running complex machine learning workloads, including training, fine-tuning, and inference. This tool provides a zero-config environment, eliminating the need for SSH setup or Docker management. It features automatic scale-to-zero billing and local-first execution, making high-powered GPU compute accessible for debugging and small-scale experiments.

Presents modal dialogs for asking clarifying questions, preferences, or confirmations when the user cannot see CLI output, ensuring explicit consent before sensitive or ambiguous actions.

Guides configuring Firebase AI in Flutter, generating text/chat with Gemini, streaming responses, multimodal prompts, and handling errors with security considerations.

A comprehensive guide to optimizing website content for modern AI search systems, including ChatGPT, Perplexity, and Google AI Overviews. It covers structural improvements, maximizing citability scores, incorporating multi-modal elements, building brand authority, and implementing technical standards like llms.txt and proper server-side rendering (SSR). Essential for maximizing organic reach in the LLM era.

Modal is a serverless cloud platform for running Python code on demand, including GPU-accelerated AI/ML workloads. Use for deploying models, serving web APIs, batch processing, and scaling Python applications to cloud containers with automatic scaling from zero to thousands of instances. Supports custom container images, persistent storage, and sub-second cold starts for low-latency inference.

Build or audit reproducible research workflows with NeuroKit2 for physiological time-series: preprocessing, event/interval analysis, multimodal alignment, variability and complexity on ECG, RSP and EDA signals.