geniml

combined

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 collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning. Supports audited local Geniml genomic-interval workflows: validate BED and universe contracts, plan Region2Vec or scEmbed runs, inspect model/tokenizer compatibility, and assess consensus universes.

When to use

Working with bulk genomic data (ChIP-seq, ATAC-seq, etc.)
Need unsupervised embeddings without metadata
Comparing region sets across experiments
Building features for downstream supervised learning
Metadata labels available (cell types, tissues, conditions)
Need to query regions by metadata or vice versa
Want joint embedding space for regions and labels
Building searchable genomic databases

Skill page

Conflict

The three copies cover the same high-level domain (Geniml for genomic interval ML) but diverge significantly in scope, safety, and technical detail. The K-Dense-AI copy is a rigorous, safety-focused operational guide with specific version pins (geniml 0.8.4, gtars 0.10.0), a 'safety gate' for data validation, and explicit warnings about Zarr/AnnData incompatibility. The aipoch and FreedomIntelligence copies are broader feature overviews that include BEDspace (which K-Dense omits in the visible text) and provide different installation advice (unpinned vs pinned). The FreedomIntelligence copy contains a syntax error in its install command (`uv uv pip`). Because the K-Dense copy has critical safety and compatibility constraints that the others lack, and the others have feature coverage (BEDspace) and workflow examples that K-Dense lacks or truncates, no single copy is a complete reproduction of the others. Combining them yields a skill that is both safe and comprehensive.