A comprehensive collection of ready-to-use scientific and research skills for AI agents.

A python library for multi omics included bulk, single cell and spatial RNA-seq analysis.

Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.

Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.

An overview of algorithms for estimating pseudotime in single-cell RNA-seq data

🐟 🍣 🍱 Highly-accurate & wicked fast transcript-level quantification from RNA-seq reads using selective alignment

A Python implementation of the DESeq2 pipeline for bulk RNA-seq DEA.

Cell type annotation for single-cell RNA-seq using multi-LLM consensus

Proteomics search & quantification so fast that it feels like magic

197 bioinformatics & life science skills for Claude Code and AI agents — BixBench 92.0% accuracy. RNA-seq, single-cell, drug discovery, proteomics, and more. Powers OmicsHorizon.

Multi-agent LLM driven cell type annotation for single-cell RNA-Seq data

Scientific research engine with adversarial review, tree search, and serendipity detection. Use when: exploring hypotheses, validating findings against literature, running computational experiments with quality gates, or hunting for unexpected discoveries. Do NOT use for simple Q&A, code editing, or non-research tasks.