Skill UI

Run OpenMM-powered molecular dynamics simulations and MDAnalysis trajectory workflows to build protein or small-molecule systems, minimize energy, equilibrate conditions, and characterize RMSD/RMSF, contacts, and free-energy surfaces for structural biology or drug studies.

Query the Monarch Initiative API to explore disease-gene-phenotype associations across species, enabling rare disease gene discovery, phenotype-to-gene mapping, and cross-species modeling via integrated HPO and model organism data.

An AI-driven comprehensive health analysis system that integrates multi-dimensional datasets, including biometrics, lifestyle, and medical history. It excels at anomaly detection, predicting various health risks (e.g., CVD, Diabetes), providing personalized, evidence-based recommendations, and generating detailed, interactive health reports.

This skill provides deep analytical insights into aggregated family health data. It assesses genetic predisposition, identifies patterns of familial diseases, calculates weighted risk scores based on historical data, and generates personalized, actionable prevention recommendations. Ideal for generating comprehensive family health reports and trend analysis.

This skill provides comprehensive functionality for querying, comparing, and calculating nutritional data from a structured food database. Users can perform precise searches by food name or nutritional criteria (e.g., high protein, low GI). It supports calculating total nutrient intake based on specific portions, suggesting foods for various health conditions (like diabetes or high blood pressure), and performing detailed comparative analysis between multiple food items.

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.

Use this skill to perform read-only API calls for exploring, previewing, searching, and filtering structured datasets hosted on Hugging Face. It allows users to validate dataset availability, list splits, paginate large datasets, and retrieve comprehensive metadata and statistics, making it ideal for data validation and content extraction workflows.

A comprehensive skill for managing the entire lifecycle of datasets on the Hugging Face Hub. It allows users to initialize, configure, and stream updates for datasets of various types (Chat, Text Classification, Tabular). Crucially, it extends data handling capabilities by enabling powerful SQL-based querying and transformation directly on Hub datasets using the DuckDB engine, facilitating advanced data exploration and pipeline creation.

Literature Review Tool helps researchers gather and synthesize key insights from scholarly texts, highlighting patterns and critical arguments to accelerate literature surveying workflows even when structured content is initially unavailable.

Analyze dietary and nutrient data, identify nutrition patterns, assess nutritional status, and generate personalized improvement advice. Also supports correlation analysis with exercise, sleep, weight, blood pressure, and blood sugar data.

Analyzes occupational health data to assess work-related risks, ergonomic issues, symptoms, and trends. Use it to generate personalized workplace health advice, screen for occupational hazards, and relate work factors to sleep, exercise, and mental health.

Analyzes oral health records to detect trends, assess caries and periodontal risks, and generate personalized care advice. Supports linked analysis with nutrition, chronic disease, medication, and eye health data for comprehensive oral care insights.