Skill OpenCode
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.
Skill OpenCode
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…
Skill OpenCode
Query the CELLxGENE Census (61M+ cells) programmatically. Use when you need expression data across tissues, diseases, or cell types from the largest curated single-cell atlas. Best for population-scale queries, reference atlas comparisons. For analyzing your own data use scanpy or scvi-tools.
Skill OpenCode
This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file…
Skill OpenCode
Use when the user is doing AI/ML work in a scientific domain — biology, chemistry, physics, astronomy, climate, genomics, materials science, medicine, ecology, energy, conservation, engineering, mathematics, scientific reasoning, drug discovery, protein design, weather modeling, theorem proving, single-cell, PDE…
Skill OpenCode
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological…
Skill OpenCode
Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use…
Skill OpenCode
RNA velocity analysis with scVelo. Estimate cell state transitions from unspliced/spliced mRNA dynamics, infer trajectory directions, compute latent time, and identify driver genes in single-cell RNA-seq data. Complements Scanpy/scVI-tools for trajectory inference.
Skill OpenCode
Deep generative models for single-cell omics. Use when you need probabilistic batch correction (scVI), transfer learning, differential expression with uncertainty, or multi-modal integration (TOTALVI, MultiVI). Best for advanced modeling, batch effects, multimodal data. For standard analysis pipelines use scanpy.
Skill OpenCode
Run remote BLAST searches against NCBI servers via Bio.Blast.NCBIWWW: pick the correct program and database, configure parameters (word size, composition-based statistics, hitlistsize), submit and poll the RID, then parse and interpret hits using Karlin-Altschul E-values and bit-scores. Avoids the maxtargetseqs misuse…
Skill OpenCode
Workflow for retrieving public omics datasets, sequences, annotations, and literature-linked biological resources from repositories such as NCBI GEO/SRA, UniProt, Reactome, and PubMed.
Skill OpenCode
Cluster samples or features from bulk transcriptomics, proteomics, or metabolomics data.
Skill OpenCode
Reconstruct disease progression trajectories from longitudinal patient omics data.
Skill OpenCode
Infer differentiation trajectories, pseudotime ordering, RNA velocity, and cell fate probabilities from scRNA-seq data.
Skill OpenCode
Complete single-cell RNA-seq analysis using Scanpy from raw data to cell type annotation with clustering and visualization.
Skill OpenCode
Complete single-cell RNA-seq analysis using Seurat from raw data to cell type annotation with clustering and visualization.
Skill OpenCode
Advanced single-cell multi-omics analysis including scRNA-seq, scCITE-seq, scATAC-seq, and TARGET-seq. Use when analyzing single-cell data, cell type identification, trajectory analysis, differential expression, UMAP/clustering, integrating protein and RNA modalities (TotalVI), or working with Scanpy, Seurat…
Skill OpenCode
Autonomous biomedical AI agent framework for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Use this skill when conducting multi-step biomedical research including CRISPR screening design, single-cell RNA-seq analysis, ADMET prediction, GWAS interpretation…
Skill OpenCode
Turn bulk RNA-seq cohorts into synthetic single-cell datasets using omicverse's Bulk2Single workflow for cell fraction estimation, beta-VAE generation, and quality control comparisons against reference scRNA-seq.
Skill OpenCode
Extend scRNA-seq developmental trajectories with BulkTrajBlend by generating intermediate cells from bulk RNA-seq, training beta-VAE and GNN models, and interpolating missing states.
Skill OpenCode
scRNA-seq Explorer.
Skill OpenCode
Publication-quality visualizations for biomedical and genomics data. Use when creating volcano plots, heatmaps, UMAP plots, dot plots, survival curves, forest plots, or multi-panel figures. Includes scanpy, matplotlib, seaborn, plotly workflows with journal-ready aesthetics and proper statistical annotations.
Skill OpenCode
AI-driven integration of cellular imaging, laser microdissection, and ultra-sensitive mass spectrometry for spatially-resolved single-cell proteomics.
Skill OpenCode
Guide through omicverse's alignment module for SRA downloading, FASTQ quality control, STAR alignment, gene quantification, and single-cell kallisto/bustools pipelines covering both bulk and single-cell RNA-seq workflows.
At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: