ChromSkills enables interpretable and reproducible agentic chromatin data analysis

Abstract

High-throughput assays generate diverse chromatin datasets that require flexible workflows and context-dependent parameter choices. Although large language models (LLMs) can assist analysis, unconstrained LLM-based execution often exhibits unstable behavior and limited reproducibility. We present ChromSkills, a curated library of domain-specific analytical Skills for agentic chromatin data analysis on the Claude Code platform. ChromSkills encodes expert-informed decision logic and parameter-selection rules as modular, human-readable Skills and couples them with structured tool interfaces to enable reproducible execution and interpretable workflow composition from natural-language task descriptions. Across representative chromatin analysis tasks, ChromSkills consistently selected appropriate tools and parameters across repeated runs and improved execution stability and token efficiency when using Model Context Protocol-based tools. Together, ChromSkills provides a practical framework for trustworthy AI-enabled, agentic chromatin data analysis by guiding LLMs with transparent, reusable domain knowledge.