Run OpenMM molecular dynamics simulations and analyze the resulting trajectories with MDAnalysis.
Guides Claude through loading molecular data and training/evaluating property-prediction models, from fingerprint baselines to GNNs and pretrained chemistry transformers.
Predict spatial expression for ~19k protein-coding genes directly from 224x224 H&E histology tiles.
Drafts and validates research-only CDS artifacts — intended-use statements, GRADE evidence profiles, aggregate cohort tables, survival plans, model evaluations, and privacy/governance checklists.
Discovers and wires up curated Hugging Face datasets, models, and Spaces across 17 scientific domains, from protein design to climate modeling.
Turns SMILES structures into ML-ready feature vectors using 100+ featurizers such as ECFP, MACCS, descriptors and ChemBERTa embeddings.
A skill that guides Claude through LaminDB artifact registration, querying, lineage tracking, and ontology-backed validation for biological data.
Builds auditable market research reports where every claim maps to a source and TAM/SAM/SOM plus forecasts are presented as scenarios.
Query the 1000 Genomes Project cohort (3,202 genomes, GRCh38) at the level of individual participants and variants.
An end-to-end phylogenetics pipeline: MAFFT alignment, IQ-TREE 2 / FastTree ML inference, and ETE3 tree analysis and plotting.
Extract velocity fields from PIV image pairs and derive vorticity, strain rate, and turbulence statistics with OpenPIV.
Run and interpret ORA/GSEA pathway enrichment (GO, KEGG, Reactome, MSigDB) from gene lists or ranked gene tables.
Teaches Claude idiomatic Polars — expressions, lazy queries, and pandas-to-Polars migration.
Query 40+ bioinformatics databases such as UniProt, KEGG, ChEMBL and Reactome through one consistent Python interface.
Run a complete bulk RNA-seq differential expression analysis in Python with PyDESeq2, from raw counts to volcano plots.
Guides correct pysam usage for reading, querying, filtering, and writing SAM/BAM/CRAM, VCF/BCF, FASTA/FASTQ, and tabix data.
Teaches Claude to build and run cloud molecular-modeling pipelines — pKa, conformers, docking, cofolding — through Rowan's Python API.
Gives Claude fine-grained RDKit guidance for SMILES/SDF parsing, descriptors, fingerprints, substructure search, reactions and 2D/3D generation.
An end-to-end Scanpy workflow skill for scRNA-seq: QC, normalization, dimensionality reduction, clustering, marker genes and cell-type annotation.
Zero-shot time series forecasting with Google's TimesFM foundation model — no training required.