Researchers need efficient access to gene expression datasets for analysis. This skill enables querying, downloading, and analyzing data from NCBI's GEO database containing millions of genomics samples.

Geniml transforms BED files into machine learning embeddings for genomic region analysis. Train models to find patterns in chromatin accessibility, build consensus peak sets, and analyze single-cell ATAC-seq data.

Access comprehensive gene data from NCBI including sequences, annotations, and functional information. Search by gene symbol, ID, or biological context to retrieve RefSeqs, Gene Ontology, chromosomal locations, and phenotypes for bioinformatics research.

FluidSim brings high-performance computational fluid dynamics to Python. Run Navier-Stokes simulations, analyze turbulence, and visualize results with simple Python commands. No complex Fortran or C++ setup required.

ETE toolkit streamlines phylogenetic analysis for evolutionary biology research. Process tree data, detect evolutionary events, and create publication-ready visualizations.

Query the Ensembl genome database REST API for vertebrate genomic data. Look up genes, retrieve sequences, analyze variants with VEP, and perform comparative genomics across 250+ species.

DNAnexus provides cloud-based genomics analysis but requires platform-specific knowledge. This skill offers complete documentation for building apps, managing data, and running bioinformatics workflows.

DiffDock uses advanced diffusion models to predict how small molecules bind to proteins in 3D space. Researchers can accelerate drug discovery by generating accurate binding poses with confidence scores for structure-based drug design.

Transform datasets into publication-ready research by automating hypothesis generation, methodology development, computational experiments, and LaTeX paper writing. Multiagent AI coordinates specialized agents for end-to-end research pipelines.

Process chemical data efficiently with a Pythonic interface to RDKit. Datamol simplifies complex cheminformatics operations while maintaining full compatibility with the RDKit ecosystem.

Cancer researchers need comprehensive mutation data for studies. This skill provides programmatic access to the COSMIC database containing millions of somatic mutations, curated cancer gene lists, and mutational signatures for precision oncology research.

ClinVar contains critical variant classifications for clinical genetics, but accessing and interpreting this data requires specialized knowledge. This skill provides comprehensive guidance for querying ClinVar via web interface, API, or FTP downloads, with detailed interpretation guidelines for clinical significance classifications.

Access ClinPGx to retrieve pharmacogenomics data including gene-drug interactions, CPIC guidelines, and allele function information. This skill supports precision medicine by providing clinical decision support for genotype-guided medication dosing.

Clinical researchers need evidence-based documents for drug development and regulatory submissions. This skill creates publication-ready clinical decision support documents with biomarker stratification and GRADE evidence grading.

This skill helps researchers find papers, extract metadata, and generate properly formatted BibTeX entries. It validates existing citations and ensures reference accuracy for scientific writing and publication.

Design and simulate quantum circuits using Google's Cirq framework. This skill provides comprehensive documentation for building quantum circuits, noise modeling, hardware integration, and running experiments on quantum computers and simulators.

Researchers waste hours manually searching bioRxiv for relevant preprints. This skill automates literature discovery with precise filtering by keywords, authors, date ranges, and scientific categories.

Process DNA, RNA, and protein sequences efficiently using Biopython. Access NCBI databases programmatically, perform sequence alignments, and automate complex bioinformatics pipelines for research applications.

Biomni transforms complex biomedical research by autonomously executing multi-step analysis tasks. Researchers can focus on scientific questions while AI handles data processing, literature review, and computational analysis across genomics, drug discovery, and clinical domains.

Working with astronomical data requires handling complex coordinate systems, file formats, and calculations. This skill provides guidance for using Astropy to process celestial coordinates, FITS files, and cosmological computations.