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 an essential library for logging, visualizing, and analyzing machine learning training experiments. It provides multi-interface support: a Python API for real-time metric logging and alert firing during training; a Command Line Interface (CLI) for retrieving historical metrics and alerts; and synchronization capabilities to Hugging Face Spaces for persistent dashboards. This tool is crucial for debugging, diagnosing training issues (like loss spikes), and automating ML workflow iteration.

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 commands for creating, managing, versioning, and querying structured datasets within the Arize platform. It covers dataset CRUD operations, appending examples, and exporting data in various formats (JSON, CSV, Parquet) using the `ax` CLI. Ideal for preparing test data, generating evaluation examples, or retrieving golden datasets for LLM development and model testing.

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.

Prepares and validates research-only clinical decision-support evaluation, evidence-profile, cohort, survival, biomarker/model, privacy, and governance artifacts using aggregate or synthetic data. Not for patient care or live clinical use.

An autonomous research agent that plans complex investigations using Directed Acyclic Graphs (DAGs). It executes sub-questions in parallel, performs gap analysis, and iteratively refines findings before synthesizing a comprehensive, structured report. Ideal for deep market analysis, academic literature reviews, or complex problem-solving requiring multiple data sources.