Skill UI

Provides a Python client for querying the Data Commons knowledge graph so teams can resolve entities, fetch statistical observations, and explore public demographic, economic, and health datasets with optional Pandas support.

Access HMDB to search over 220K human metabolites by name, ID, structure, disease or specimen, retrieve chemical/clinical/pathway data, and download spectra for metabolomics workflows.

NeuroKit2 is a Python biosignal processing toolkit for ECG, EEG, EDA, RSP, EMG, and EOG data that automates cleaning, feature extraction, and HRV/complexity analysis for psychophysiology, clinical, and HCI studies.

Use the OpenAlex client to query 240M+ scholarly works for literature searches, citation tracking, open access discovery, and bibliometric studies across papers, authors, and institutions.

Query the Reactome Content and Analysis Services (or the reactome2py client) to fetch pathway hierarchies, map genes, run overrepresentation/expression analyses, and explore disease interactions for systems biology research.

Access multi-dimensional cancer genomics data via cBioPortal REST API for mutation, copy-number, expression, and survival insights across TCGA/GENIE studies, enabling target validation and clinical association analyses programmatically with Python clients.

Trackio is a comprehensive library for logging, visualizing, and managing ML experiment metrics and alerts. It allows developers to use a Python API to log metrics and set diagnostic alerts (INFO/WARN/ERROR) during training. Furthermore, a dedicated CLI enables metric retrieval and dashboard viewing, syncing data to Hugging Face Spaces for persistent, remote monitoring, making it ideal for autonomous ML agent workflows.

Generates a single formula-driven Excel dashboard from CSV/tabular data, complete with KPI cards, charts, sparklines, and conditional formatting so analysts can instantly review metrics and share an executive-ready .xlsx output.

This skill provides comprehensive command-line interface (CLI) functionality for managing the entire lifecycle of experiments within the Arize platform. Users can list, retrieve metadata, create new experiments from various data files (CSV, JSON, Parquet), and export detailed run results for deep analysis. It supports linking experiments to specific dataset versions, making it essential for structured ML model evaluation and performance comparison workflows.

This skill provides comprehensive command-line capabilities for interacting with Arize datasets. Use it to perform dataset CRUD operations (Create, Read, Update), list available datasets, retrieve detailed metadata, and export large volumes of example data. It is essential for managing the lifecycle of versioned datasets used in model evaluation and machine learning experimentation. Supports file formats like CSV, JSON, and Parquet.

Guides agents through validation, dry-run MCP tool usage, and monitors-as-code YAML generation so Monte Carlo monitors can be established via CLI or CI/CD without directly creating assets.

Generates publication-ready clinical decision support documents for pharma and clinical research, covering biomarker-stratified cohort analysis, evidence-graded treatment recommendations, statistical comparisons, and regulatory-ready LaTeX/PDF outputs.