Skill UI

Provides direct REST access to UniProt for searching proteins, retrieving sequences/annotations, mapping IDs, and streaming large datasets in workflows that demand precise protein data.

A comprehensive solution for systematically auditing dataset health. It profiles data distributions, detects missing values (MCAR/MAR/MNAR), identifies outliers using multiple methods (IQR, Z-score), and calculates a Data Quality Score (DQS). It provides actionable remediation plans and supports monitoring setup for live pipelines.

This skill generates highly precise deep links to various resources within the Arize platform, including traces, spans, sessions, datasets, and evaluators. It requires collecting specific resource IDs (e.g., project_id, dataset_id) and managing time ranges (startA/endA) to ensure the user is taken to the exact view, optimizing the user experience for ML practitioners and data scientists.

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.

Phoenix CLI streamlines debugging Phoenix-based LLM observability: it fetches traces, spans, sessions, datasets, experiments, prompts, and GraphQL data while piping JSON to tools like jq so you can triage errors, inspect performance, and verify deployments quickly.

Astropy is a Python library for astronomical research that handles unit-aware quantities, celestial coordinates, FITS files, cosmological models, precise time systems, and tabular data, letting you transform, convert, and analyze observational or theoretical datasets.

DeepChem is a Python library for molecular machine learning, providing featurizers, dataset loaders, splitters, and models spanning traditional ML to GNNs for property prediction, drug discovery, and materials design.

Geniml builds machine learning workflows around BED and scATAC datasets to learn embeddings for regions, metadata, and cells, enabling similarity searches, clustering, consensus peaks, and downstream analysis of genomic intervals.

High-performance Rust toolkit with Python bindings for genomic interval analysis, covering overlap detection, coverage track generation, ML tokenization, reference sequence handling, fragment processing, and scoring workflows for sequencing datasets.

Histolab streamlines whole-slide microscopy by automating tissue detection, tile extraction, and stain normalization for H&E scans, making it ideal for lightweight dataset prep and quick tile-based analysis.

Hypogenic automates LLM-driven hypothesis generation and testing over tabular datasets, blending literature insights with empirical analysis to accelerate discovery in domains like deception detection or AI content evaluation.