Skill UI

Automates adapting pretrained models via transfer learning by analyzing requirements, generating PyTorch or TensorFlow fine-tuning code, validating data, tracking KPIs, and saving artifacts so you can quickly reuse existing models for new datasets and tasks.

This skill provides a comprehensive guide to configuring event-driven workflows and robust notification systems for Databricks jobs. It covers setting up various notification destinations (Slack, Teams, Email, PagerDuty) and triggering alerts based on job lifecycle events (success, failure, start). Furthermore, it demonstrates advanced monitoring using system audit tables and creating automated SQL alerts for critical data pipeline monitoring.

Track experiments, catalog models, compare versions, and deploy trained artifacts with MLflow’s framework-agnostic lifecycle tools across PyTorch, TensorFlow, and other stacks.

Automates the end-to-end training of machine learning models by analyzing datasets, selecting suitable algorithms, tuning parameters, performing cross-validated training, and emitting performance metrics plus a saved model artifact.

A comprehensive guide to optimizing deep learning workflows and maximizing efficiency on cloud GPU platforms like Vast.ai. Learn techniques for selecting optimal instances by performance-per-dollar, reducing startup latency, tuning data pipelines (DataLoader, pin_memory), and scaling multi-GPU training using PyTorch and Bash scripting.

Conversational research ideation assistant that helps scientists generate novel hypotheses, explore interdisciplinary analogies, challenge assumptions, and outline experiment pathways during early-stage planning.

GeoMaster is a comprehensive geospatial science skill that guides remote sensing, GIS, spatial analysis, and machine learning workflows for Earth observation and planetary data projects, covering satellite imagery, vector/raster math, point clouds, terrain, hydrology, and cloud-native data stacks.

This comprehensive tool helps users set, track, and optimize personal health goals using deep data analysis. It validates goals against the SMART criteria (Specific, Measurable, Achievable, Relevant, Time-bound) and tracks progress by correlating diverse health data, including nutrition, exercise, and sleep patterns. Key features include habit formation analysis, motivation assessment, identifying roadblocks, and generating interactive reports with ECharts visualizations, providing a holistic view of wellness journeys.

A specialized tool for digital forensics investigators that parses the Windows Amcache.hve registry hive. It extracts crucial evidence regarding program execution history, application installation metadata, and loaded driver binaries. By correlating SHA-1 hashes and reconstructing timelines, users can determine which executables ran on a system, aiding in incident response and threat detection.

Timesketch is a collaborative, open-source platform for security teams to analyze and reconstruct complex forensic timelines. It ingests and normalizes multi-source security data (logs, artifacts) using methods like Plaso, CSV, and JSONL. Users can visualize event chronology, track attack chains, and utilize advanced built-in analyzers (e.g., Sigma, Chain of Events) for comprehensive incident investigation and documentation.

This guide details how to detect WMI event subscription persistence, a common attacker technique (T1546.003). It focuses on analyzing suspicious Sysmon Event IDs 19, 20, and 21 to identify malicious EventFilters, EventConsumers, and Bindings. Essential for incident response and threat hunting in Windows environments.

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.