Geniml

If you're working with genomic intervals and machine learning, this handles the messy middle layer between BED files and actual models. It gives you Region2Vec for unsupervised region embeddings, BEDspace for joint region and metadata embeddings, and scEmbed specifically for single-cell ATAC-seq data. The universe building tools are honestly the most useful part since tokenization quality makes or breaks everything downstream. It's built on word2vec concepts applied to genomic regions, which sounds odd but works well in practice. Includes CLI tools and proper evaluation metrics. Ships with multiple consensus peak methods (coverage cutoff, HMM, maximum likelihood) that give you statistical rigor when combining datasets.

skills.sh installs: 2K
GitHub stars: 56.6K
last updated: Aug 21, 2026

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Install

$ npx -y skills add k-dense-ai/scientific-agent-skills --skill geniml --agent claude-code

Installs into .claude/skills of the current project.

Files

Geniml

Use Geniml for machine learning and statistical workflows over genomic interval sets. Treat coordinates, assemblies, token vocabularies, model artifacts, and sample grouping as explicit contracts. The bundled scripts validate or plan; they do not import Geniml, contact services, deserialize models, or execute training.

Bash is declared only for explicit, user-approved uv, Python, Geniml, Gtars, Git, and native CLI commands shown in this guide; bundled Python helpers do not spawn subprocesses. Example paths under data/, refs/, work/, and models/ are user-provided project placeholders, not missing bundled files.