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.
8.7
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0
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Machine Learning
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Excellent genomic ML skill with comprehensive coverage of five distinct capabilities (Region2Vec, BEDspace, scEmbed, consensus peaks, utilities). The SKILL.md provides clear overview with well-organized references to detailed documentation files, avoiding clutter while maintaining completeness. Includes concrete workflows, CLI commands, decision guidance ('when to use which tool'), and troubleshooting. Strong task knowledge with code examples and parameter guidance. The skill addresses genuinely complex genomic ML tasks that would require extensive agent iteration and domain expertise. Minor score reduction in novelty only because some individual operations (like training embeddings) could theoretically be done by a capable agent, though the domain-specific integration, universe building methods, and specialized tokenization provide substantial value.
davila7
Skill Author
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