Skill UI

BioServices is a comprehensive Python library providing programmatic access to over 40 major bioinformatics web services and databases (e.g., UniProt, KEGG, ChEMBL). It is designed for complex biological data workflows, enabling cross-database querying, identifier mapping across diverse biological resources, and advanced sequence/pathway analysis in a single, consistent API structure.

This skill provides programmatic access to the CZ CELLxGENE Census, a comprehensive, versioned repository containing over 61 million single-cell genomics records from human and mouse. It is essential for conducting large-scale cross-dataset analyses, querying gene expression data across diverse tissues, cell types, and diseases, and training machine learning models in bioinformatic research. It supports both in-memory and out-of-core computation using advanced libraries like scanpy.

A specialized skill for generating comprehensive medical and scientific documentation, including case reports (CARE guidelines), diagnostic reports, clinical trial reports (CSR), and patient records (SOAP, H&P). It ensures strict adherence to global regulatory standards such as HIPAA, FDA, and ICH-GCP, and integrates mandatory AI-generated scientific schematics for enhanced clarity and publication quality. Ideal for medical research and healthcare compliance.

Dask is a powerful Python library designed for scalable, parallel, and distributed computing. It enables workflows for datasets that exceed available RAM or require processing across multiple machines. Users can scale familiar pandas and NumPy operations (DataFrames, Arrays) and process unstructured data (Bags) efficiently, making it ideal for large-scale ETL, analytics, and distributed machine learning tasks.

A comprehensive cloud platform for biomedical data analysis and genomics research. This skill provides tools for building, deploying, and managing complex bioinformatics pipelines (apps/applets). Users can manage diverse data types (FASTQ, BAM, VCF), execute advanced workflows, and interact with the platform programmatically using the dxpy Python SDK.

Perform deep exploratory data analysis (EDA) across a vast array of scientific formats, including chemistry, bioinformatics, microscopy, and spectroscopy. This skill automatically detects the file type, assesses data quality and integrity, extracts format-specific metadata, and generates detailed markdown reports. It is ideal for understanding the structure, content, and potential analysis paths for complex scientific datasets.

FluidSim is a high-performance, object-oriented Python framework designed for computational fluid dynamics (CFD) simulations. It implements solvers for key physical equations, including 2D/3D Navier-Stokes, shallow water, and stratified flows. Utilizing pseudospectral methods with FFT and MPI parallelization, it delivers professional-grade simulation capabilities for analyzing turbulence, geophysical, and general fluid dynamics phenomena, coupled with comprehensive post-processing and visualization tools.

Geniml is a comprehensive Python package designed for applying machine learning techniques to genomic interval data (BED files). It provides advanced, unsupervised methods for learning high-dimensional embeddings of genomic regions, single cells (scATAC-seq), and metadata. Core functionalities include Region2Vec for region embeddings, BEDspace for joint region-label analysis, scEmbed for single-cell data, and utilities for building consensus peak universes, enabling robust clustering and feature extraction in genomics research.

A powerful skill covering geospatial science, including GIS, remote sensing, spatial statistics, and machine learning for Earth observation. It supports processing of various data types (raster, vector, point cloud) from sources like Sentinel and Landsat, and integrates modern cloud-native workflows (STAC, COG). Ideal for environmental monitoring, urban planning, and advanced spatial modeling tasks.

This skill automatically detects all available computational resources, including CPU core counts, GPU support (NVIDIA, AMD, Apple Silicon), memory capacity, and disk space. It generates a detailed JSON report that goes beyond simple metrics by providing strategic, context-aware recommendations. These recommendations guide users on the optimal computational approach—such as choosing between Dask for out-of-core processing, selecting the right GPU backend, or determining the ideal number of workers for parallel processing—before running any intensive scientific or data analysis task.

gget is a comprehensive command-line interface and Python library providing unified access to over 20 major bioinformatics databases. It allows researchers to perform complex tasks such as gene information retrieval, sequence analysis (BLAST, BLAT), structural modeling, and enrichment analysis using a consistent API. Ideal for interactive data exploration, quick lookups, and integrating bioinformatics workflows into Python scripts.

This skill guides systematic scientific inquiry by helping users develop testable, evidence-based hypotheses from observations and preliminary data. It strictly follows the scientific method, covering literature review, synthesizing evidence, generating multiple competing mechanisms, evaluating hypothesis quality (testability, falsifiability), and designing detailed experimental protocols. Comprehensive reports mandate the use of scientific schematics for visualization.