Skill UI

This is an advanced, structured workflow designed to conduct deep, multi-source investigations. It synthesizes current public evidence with user-provided context to generate highly structured reports. The workflow explicitly differentiates between verifiable facts, user-supplied evidence, logical inferences, and actionable recommendations, making it ideal for complex comparative analysis or continuous monitoring tasks.

Astropy is the foundational Python package for astronomical research and astrophysical data analysis. It provides essential APIs for handling coordinates, physical units, time systems, FITS file I/O, table manipulation, and complex cosmological calculations. Use it for any workflow involving celestial data processing.

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.

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.

This tool provides access to the Cancer Dependency Map (DepMap) data, derived from genome-wide CRISPR screens, RNAi, and compound sensitivity assays. It allows users to query gene dependency scores, identify cancer-specific vulnerabilities, predict synthetic lethal interactions, and validate potential oncology drug targets across various cancer cell lines.

ETE is a comprehensive toolkit for phylogenomic and hierarchical tree analysis. It enables users to load and manipulate diverse tree formats (Newick, NHX), detect key evolutionary events like speciation and duplication, identify orthologs and paralogs, and integrate with NCBI taxonomy databases. The toolkit supports professional visualization in PDF/SVG for high-quality scientific publications.

gget is a comprehensive command-line and Python package providing unified access to over 20 bioinformatics databases. It allows users to perform quick lookups and analyses, including gene searching, retrieving reference genomes (GTF, cDNA), fetching sequences, and running BLAST searches against major biological data repositories like Ensembl, UniProt, and NCBI. It is ideal for interactive exploration and initial data acquisition in genomics research.

Gtars is a comprehensive, high-performance toolkit written in Rust, featuring Python bindings for advanced genomic interval analysis. It is designed for computational biology and machine learning applications, providing specialized modules for overlap detection (IGD), coverage track generation (WIG/BigWig), genomic tokenization, and single-cell fragment analysis. Use it for processing BED files, validating reference sequences, and building robust bioinformatic pipelines.

A comprehensive toolkit for running and analyzing molecular dynamics (MD) simulations. This skill covers the entire workflow: system setup, force field definition, energy minimization, and equilibration using industry-standard tools like OpenMM and MDAnalysis. Essential for structural biology, drug binding studies, and biophysics research.

This toolkit provides comprehensive processing and analysis capabilities for various physiological biosignals, including ECG, EEG, EDA, PPG, and EMG. It is designed for advanced psychophysiology research, clinical diagnosis, and human-computer interaction studies, supporting complex analyses like HRV, microstate detection, and autonomic assessment.

An end-to-end toolkit for analyzing Neuropixels high-density neural recordings, covering the entire electrophysiology workflow. Features include loading diverse formats (SpikeGLX, NWB), preprocessing, motion/drift correction, advanced spike sorting (Kilosort4, etc.), quality metric calculation, and unit curation (AI-assisted and model-based). Ideal for researchers working with multi-shank, high-channel count neural data.

PathML is a comprehensive Python toolkit designed for advanced computational pathology workflows. It facilitates the end-to-end analysis of whole-slide pathology images (WSI) from over 160 proprietary formats. Key functionalities include image preprocessing (stain normalization, tissue detection), constructing spatial cell/tissue graphs, and training deep learning models (e.g., HoVer-Net). It is highly specialized for analyzing multiparametric data from technologies like CODEX and Vectra.