Skill UI

AnnData is a core Python framework designed for managing and analyzing annotated matrices, essential in single-cell genomics and high-dimensional data science. It provides a standardized structure (X, obs, var) to store experimental measurements alongside rich metadata. Users can read/write various formats (h5ad, zarr), perform subsetting, concatenation, and complex data manipulation, making it indispensable for modern bioinformatics workflows.

Arboreto is a powerful Python library designed for inferring Gene Regulatory Networks (GRNs) from various gene expression datasets, including bulk and single-cell RNA-seq data. It leverages advanced machine learning algorithms like GRNBoost2 and GENIE3. The library supports scalable, distributed computation using Dask, making it ideal for analyzing large-scale, complex transcriptomics datasets to identify critical transcription factor-target gene relationships.

A comprehensive Python library for professional astronomy and astrophysics data analysis. It provides essential tools for handling celestial coordinates, physical units, FITS file I/O, time systems, and cosmological calculations, making it vital for scientific research in space science.

A powerful tool designed for systematic literature reviews and meta-analyses. It searches scientific papers and retrieves highly structured experimental data directly from the full text, going beyond mere abstracts. It extracts over 25 fields, including detailed methods, quantitative results, sample sizes, quality scores, and conclusions, enabling comprehensive evidence synthesis for academic research.

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 collection of public single-cell and spatial transcriptomics data. It allows users to query massive, standardized datasets—including millions of cells, gene expression matrices, embeddings, and metadata—for specific cell types, tissues, or diseases without downloading the entire dataset. Ideal for large-scale analysis, ML training, and cross-dataset comparisons using tools like Scanpy and AnnData.

Specialized skill for generating professional, evidence-based Clinical Decision Support (CDS) documents for pharmaceutical and clinical research. It performs biomarker-stratified patient cohort analyses, generates evidence-based treatment guidelines (including GRADE grading), and produces publication-ready LaTeX/PDF reports optimized for regulatory submissions and drug development.

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.

COBRApy is a comprehensive Python library for Constraint-Based Reconstruction and Analysis (COBRA) of metabolic models. It enables users to simulate complex cellular metabolism, performing crucial analyses such as Flux Balance Analysis (FBA), Flux Variability Analysis (FVA), and virtual gene/reaction knockouts. It is an essential tool for systems biology research and metabolic engineering design.

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.

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.

DeepChem is a comprehensive Python library designed for applying machine learning to chemical, biological, and materials science datasets. It enables molecular property prediction (e.g., ADMET, toxicity), drug discovery, and materials design. The skill supports diverse featurization methods (fingerprints, descriptors, graph representations), handling various data formats like SMILES and SDF, and implementing advanced GNN models for complex scientific research.