Skill UI

Query InterPro for protein family, domain, and GO annotations via a REST API with Python helpers, enabling function prediction, domain architecture mapping, and evolutionary classification insights for proteomic workflows.

Query the JASPAR REST API and Python helpers to retrieve TF binding profiles, convert PFMs to PWMs, and scan DNA sequences for high-confidence motifs, enabling regulatory variant interpretation, ChIP/ATAC analysis, and TF network construction.

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.

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.

This comprehensive skill analyzes sleep data to identify patterns, assess quality, and detect potential sleep disorders. It tracks trends in sleep duration, efficiency, and consistency, and performs advanced correlation analysis with other health metrics like exercise, diet, and mood. The output provides actionable, personalized recommendations based on scientific data, helping users improve their overall sleep health.

This skill integrates multiple digital health sources (such as Apple Health, Fitbit, Oura, and CSV/JSON files) into a unified system. It processes, standardizes, and validates diverse health data formats, and connects this data to a dedicated knowledge base (WellAlly.tech). It provides intelligent recommendations and article retrieval based on the user's current health context and needs.

Hindsight is an open-source digital forensics tool designed to parse and correlate artifacts from Chromium-based browsers (Chrome, Edge, Brave, Opera). It extracts comprehensive data, including browsing history, cookies, download records, autofill data, and saved passwords, generating unified, chronological timelines essential for incident response, insider threat investigation, and digital forensics cases.

Detects abnormal access patterns across major cloud storage platforms, including AWS S3, GCS, and Azure Blob Storage. By analyzing cloud audit logs (CloudTrail, etc.), this tool identifies critical security threats such as after-hours bulk data downloads, sudden spikes in API calls (GetObject spikes), access from new geographic IPs, and potential data exfiltration. It utilizes statistical baselines and time-series anomaly detection for proactive threat hunting and security incident investigation.

A comprehensive tool for interacting with the arXiv database. It allows users to search for academic papers by topic or ID, retrieve detailed metadata (abstracts, authors, categories), and download the PDF files directly. Ideal for researchers and students needing to efficiently find, save, and summarize scientific literature.