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arXiv · 2604.12387

oxo-call: Documentation-grounded Skill Augmentation for Accurate Bioinformatics Command-line Generation with Large Language Models

Yun Peng·Yujun Sun·Jia Ding·Bin Yan·Zhangyu Wang·Chunyang Wang·Chenyang Shu·Jian-Guo Zhou·Shixiang Wang
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Abstract

Command-line bioinformatics tools remain essential for genomic analysis, yet their diversity in syntax and parameterization presents a persistent barrier to productive research. We present oxo-call, a Rust-based command-line assistant that translates natural-language task descriptions into accurate tool invocations through two complementary strategies: documentation-first grounding, which provides the large language model (LLM) with the complete, version-specific help text of each target tool, and curated skill augmentation, which primes the model with domain-expert concepts, common pitfalls, and worked examples. oxo-call (v0.10) ships >150 built-in skills covering 44 analytical categories, from variant calling and genome assembly to single-cell transcriptomics, compiled into a single, statically linked binary. Every generated command is logged with provenance metadata to support reproducible research. oxo-call also provides a DAG-based workflow engine, extensibility through user-defined and community skills via the Model Context Protocol, and support for local LLM inference to address data-privacy requirements. oxo-call is freely available for academic use at https://traitome.github.io/oxo-call/.

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Yun Peng, Yujun Sun, Jia Ding, Bin Yan, Zhangyu Wang, Chunyang Wang, Chenyang Shu, Jian-Guo Zhou, Shixiang Wang. 2026-04-14. oxo-call: Documentation-grounded Skill Augmentation for Accurate Bioinformatics Command-line Generation with Large Language Models. https://arxiv.org/abs/2604.12387

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