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.
8.3
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Machine Learning
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Excellent skill for genomic interval machine learning. The SKILL.md provides comprehensive coverage with clear descriptions of five distinct capabilities (Region2Vec, BEDspace, scEmbed, consensus peaks, utilities), each properly referenced to detailed documentation files. Task knowledge is outstanding with complete workflows, CLI commands, Python examples, and practical troubleshooting guidance. Structure is exemplary: concise overview with organized sections pointing to specialized reference files, avoiding clutter while maintaining completeness. The skill addresses a complex, specialized domain (genomic ML) that would require substantial tokens and domain expertise for a CLI agent to handle independently. Minor novelty limitation: while highly specialized, the underlying methods (word2vec-style embeddings, StarSpace) are established techniques applied to genomics rather than fundamentally novel algorithms. The skill effectively packages complex bioinformatics workflows into an accessible interface, making it highly valuable for reducing cognitive load and token costs in genomic data analysis tasks.
K-Dense-AI
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