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#large language models Open access Aug 2026

ChromSkills enables interpretable and domain-guided agentic chromatin data analysis

Abstract High-throughput chromatin assays require flexible workflows and context-aware parameter choices. However, unconstrained large language model-based analysis can suffer from inconsistent tool selection, parameterization, and execution. We present ChromSkills, a curated library of domain-specific analytical Skills for agentic chromatin data analysis on coding-agent platforms that support Skills. ChromSkills encodes expert decision logic and parameter-selection rules as modular, human-readable Skills linked to structured tool interfaces, enabling interpretable workflow composition and consistent execution from natural-language tasks. Across representative analyses, ChromSkills improved tool and parameter consistency, execution stability, and token efficiency, providing a transparent and domain-guided framework for AI-assisted chromatin data analysis.

Yuxuan Zhang, Yiman Wang, Yang Tan et al. · 0 citations