Explore skills

Efficient storage and retrieval of genomic variant data using TileDB. Scalable VCF/BCF ingestion, incremental sample addition, compressed storage, parallel queries, and export capabilities for population genomics.

Therapeutics Data Commons provides AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, and molecular oracles for therapeutic ML and pharmacological prediction.

Molecular featurization for ML with over 100 featurizers, including ECFP, MACCS, descriptors, and pretrained models (ChemBERTa). Converts SMILES to features for QSAR and molecular ML.

A Pythonic wrapper for RDKit, offering a simplified interface and sensible defaults. It's ideal for standard drug discovery tasks such as SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, and parallel processing, returning native rdkit.Chem.Mol objects.

This skill is for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use it when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches, particularly for univariate and multivariate time series.

Use this skill for working with Brain Imaging Data Structure (BIDS) datasets, including organizing neuroscience and biomedical data, querying layouts, validating compliance, converting DICOM to BIDS, and creating BIDS derivatives.

Perform fast CLI/Python queries across 20+ bioinformatics databases for quick lookups of gene info, BLAST searches, AlphaFold structures, and enrichment analysis. It's best for interactive exploration and simple queries, with biopython or bioservices recommended for batch processing or advanced workflows.

This skill provides the data structure for annotated matrices in single-cell analysis, ideal for .h5ad files or integration with the scverse ecosystem. For analysis workflows, use scanpy; for probabilistic models, scvi-tools; and for population-scale queries, cellxgene-census.

Hugging Face Transformers for loading Hub models, running pipeline inference, text generation, and Trainer fine-tuning on NLP, vision, audio, and multimodal tasks. Use when working with AutoModel, pipelines, tokenizers, or TrainingArguments—not for general ML outside the Transformers library.

Microscopy data management platform for high-content screening and microscopy workflows. It allows accessing images via Python, retrieving datasets, analyzing pixels, managing ROIs/annotations, and batch processing.

A comprehensive skill for writing markdown and Mermaid diagrams. It establishes text-based diagrams as the default documentation standard, offering full style guides, 24 diagram type references, and 9 document templates for scientific documents, reports, analyses, or visualizations.

Diffusion-based molecular docking predicts protein-ligand binding poses from PDB/SMILES, providing confidence scores and enabling virtual screening for structure-based drug design. It is not intended for affinity prediction.