Starlitnightly

This skill streamlines OmicVerse data loading by replacing scanpy with ov.io readers for h5ad, 10x, Visium, Nanostring, and CSV formats.

This skill infers gene regulatory networks from scRNA-seq, prunes regulons, and scores regulon activity for cell-type resolution.

This skill helps you run foundation model workflows for single-cell analysis, from embedding to annotation and integration across 22 models with a unified API.

This skill helps you annotate gene results and explore pathways, literature, and drug associations using BioContext's unified Python API.

This skill identifies pseudotime-associated genes driving lineage decisions by adaptive ridge regression and Mellon-based density scoring.

This skill guides end-to-end FASTQ-to-count analysis in OmicVerse, automating download, QC, alignment, quantification, and single-cell workflows.

This skill guides you through proper dictionary-based gene set enrichment in OmicVerse, ensuring correct data formats and error-free analysis.

This skill helps you generate publication-quality matplotlib and seaborn visualizations for bioinformatics data, supporting multi-panel layouts and

This skill exports bioinformatics results and tables to formatted Excel files using openpyxl, running locally for compatibility with all LLM providers.

This skill reconstructs single-cell profiles from bulk RNA-seq using Bulk2Single, trains a beta-VAE, and benchmarks against reference scRNA-seq.

This skill harmonises bulk RNA-seq data across batches using ComBat, exports corrected matrices, and benchmarks pre/post correction visually.

This skill creates professional PDF reports with text, tables, and embedded images using reportlab, enabling local, provider-agnostic analysis documentation.

This skill maps single-cell references to spatial transcriptomics profiles, enabling spot-level reconstruction, marker visualization, and downstream reporting.

This skill guides you through loading TCGA data, initializing metadata, and exporting annotated AnnData while enabling survival analyses.

This skill provides quick, actionable guidance to integrate and visualize single-cell multi-omics data across MOFA, GLUE, SIMBA, TOSICA, and StaVIA.

This skill guides you through bulk RNA-seq differential expression analysis in omicverse, from gene ID mapping to visualization and pathway enrichment.

This skill guides you through single-cell annotation workflows from SCSA to GPTAnno and weighted transfer, enabling accurate cell type labeling.

This skill quantifies ligand-receptor communication in annotated single-cell data using CellPhoneDB v5 and generates CellChat-style visualizations for

This skill helps you query STRING for protein interactions, build PPI networks with pyPPI, and render styled network figures from gene lists.