Skill UI

Query the NIH Metabolomics Workbench via REST API to fetch metabolites, RefMet nomenclature, mass-spec searches, and detailed study datasets for biomarker discovery and metabolomics research.

Scanpy is a scalable Python toolkit for single-cell RNA-seq analyses, covering QC, normalization, dimensionality reduction, clustering, marker discovery, annotation, and publication-ready visualization across h5ad/10X/CSV datasets.

Seaborn is a powerful Python library for creating publication-quality statistical graphics. It is designed for dataset-oriented plotting, making it ideal for exploratory data analysis, multivariate analysis, and visualizing complex relationships, distributions, and comparisons with minimal code. It builds upon Matplotlib, offering high-level interfaces for functions like scatterplots, heatmaps, and pair plots.

TileDB-VCF lets you ingest, store, query, and export large genomic VCF/BCF datasets using TileDB’s sparse arrays, supporting incremental sample growth, parallel regions queries, and cloud-local interoperability for population genomics workflows.

This skill provides zero-shot time series forecasting using Google's TimesFM foundation model. It forecasts any univariate time series (like sales, sensor data, or weather) without requiring custom model training. The model delivers point forecasts along with calibrated prediction intervals (quantiles), offering a robust and probabilistic view of future trends. It is ideal for rapid, reliable forecasting across diverse time-based datasets.

TorchDrug is a PyTorch-native toolbox for building graph neural networks tailored to molecules, proteins, and biomedical knowledge graphs, covering property prediction, generation, retrosynthesis, and knowledge graph reasoning with curated datasets and models.

Direct REST API access to UniProt for searching proteins, fetching FASTA sequences, mapping identifiers, selecting annotations, and streaming large datasets within Python workflows.

Query BindingDB for measured drug-target affinities (Ki, Kd, IC50, EC50) via REST endpoints or downloaded TSVs to support target-centric drug discovery, SAR analysis, lead profiling, and benchmarking datasets.

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.

Provides charting, mapping, and dashboard techniques to turn datasets into clear insights for sharing across teams.

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.

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.