Skill UI

Query gnomAD via GraphQL to retrieve allele frequencies, constraint scores, and loss-of-function annotations for population-informed variant interpretation and rare disease gene prioritization.

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.

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.

Analyzes comprehensive fitness logs to track quantitative trends in volume, intensity, and frequency. It tracks progress across multiple disciplines (running, strength) and correlates exercise metrics with vital health indicators like blood pressure and weight, providing personalized, science-backed training and recovery advice.

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.

This skill provides comprehensive read-only access to explore, query, and analyze datasets hosted on Hugging Face. It utilizes the Dataset Viewer API to list splits, preview rows, perform text searches, filter records, and retrieve crucial metadata and Parquet links. Furthermore, it supports advanced data extraction by enabling complex SQL querying (via parquetlens) directly against dataset shards, allowing export into CSV, JSON, or Parquet formats.

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.

PyMC is a powerful Python library for probabilistic programming and advanced Bayesian inference. This comprehensive guide details the full workflow for building, fitting, and validating complex Bayesian models. Learn to implement hierarchical structures, perform MCMC sampling (NUTS), conduct posterior predictive checks (PPC), and effectively quantify uncertainty using industry best practices.

Analyzes TCM constitution data to identify primary and mixed constitution types, assess traits and risks, and generate personalized wellness advice with links to nutrition, exercise, sleep, and trend tracking.

A comprehensive tool designed to search, download, and summarize academic papers from the arXiv repository. Users can search by topic or specific ID, manage result limits, and save PDFs locally. The system extracts detailed metadata, provides abstract summaries, and identifies key research contributions.

This skill systematically verifies the novelty of a proposed research method or idea against vast global literature. It identifies core technical claims, performs multi-source searches across arXiv, Google Scholar, and major conferences (ICLR, NeurIPS, ICML), and utilizes advanced LLMs for cross-model verification. The output is a structured report, providing a novelty score, identifying the closest prior work, and offering a strategic recommendation to guide the research direction.