Skill UI

Placeholder skill entry for risk modeling workflows that outlines basic analytics and scenario planning used to quantify potential losses and guide mitigation decisions.

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 workflow for extracting, parsing, and analyzing Windows Event Logs (EVTX). It guides security professionals in using advanced tools like Chainsaw and Hayabusa, combined with Sigma rules, to detect critical security incidents such as lateral movement, privilege escalation, and persistence mechanisms during incident response and digital forensic investigations.

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.

Automates the generation of high-quality, publication-ready figures and comparison tables from raw experimental data (JSON, CSV). This skill is essential for academic writing workflows, handling various plot types including line graphs, bar charts, scatter plots, and heatmaps, ensuring consistency with professional style guides like LaTeX and specific DPI requirements.

This guide demonstrates how to build comprehensive forensic super-timelines using Plaso (log2timeline) to correlate events across diverse sources. It covers the entire workflow, from processing disk images and various logs (MFT, registry, browser history) to generating standardized CSV, JSONL exports, and visualizing the results in tools like Timesketch for deep investigative analysis.

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.

This comprehensive guide provides a detailed workflow for parsing Windows LNK shortcut files in a digital forensics context. It extracts critical artifacts including target paths, timestamps (creation, access, modification), volume serial numbers, and machine identifiers. It is essential for reconstructing user file access history, tracking accessed files on network shares, and building robust forensic timelines.

A comprehensive tool for generating publication-quality figures essential for Machine Learning and AI academic papers. It supports two distinct workflows: generating complex system architecture and workflow diagrams using AI (Gemini), and creating precise, data-driven visualizations (line charts, heatmaps, etc.) using standard libraries like matplotlib and seaborn. Use it when preparing any figure for a conference submission.

ADHX provides a dedicated API to fetch X/Twitter posts as clean, structured JSON. It extracts comprehensive data, including author profiles, engagement metrics (likes, retweets), and full article content for long-form posts. This method is vastly superior to traditional scraping techniques, making it ideal for LLM analysis, summarization, and structured data workflows.

Trackio is an essential library for logging, visualizing, and analyzing machine learning training experiments. It provides multi-interface support: a Python API for real-time metric logging and alert firing during training; a Command Line Interface (CLI) for retrieving historical metrics and alerts; and synchronization capabilities to Hugging Face Spaces for persistent dashboards. This tool is crucial for debugging, diagnosing training issues (like loss spikes), and automating ML workflow iteration.

This skill automates the process of renting, managing, and terminating cloud GPU instances on vast.ai. It analyzes your AI/ML task—determining VRAM, required GPU count, and estimated runtime—and then searches for the most cost-effective and reliable GPU offers, presenting optimized options for your training workflow.