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This paper introduces "Scientific Agent Skills," an open library of 163 procedural instructions designed to guide language model agents in performing defensible scientific analyses across domains like genomics and medical imaging. By providing versioned, human-readable instruction files that specify authoritative identifiers and required caveats, the project addresses critical gaps in agent reliability where mere code generation is insufficient for rigorous research. The approach shifts focus from raw task completion to embedding domain-specific procedural knowledge directly into agent workflows.
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Scientific Agent Skills: A Library of Procedural Knowledge for Research Agents

arXiv2 min read

arXiv:2609.00065v1 Announce Type: cross Abstract: A language-model agent asked to analyse an experiment will usually return working code. Whether the analysis is defensible is a different question. A defensible analysis depends on procedural choices: which test the field accepts, which identifier namespace is authoritative, and which caveats must accompany a result. We present Scientific Agent Skills, an open library of 163 such procedures in 16 areas of practice, including genomics, cheminformatics, medical imaging, study design and scientific communication. Each skill is a directory built around a versioned, human-readable instruction file. An agent loads the file only when a task calls for it; the directory often also contains reference material and runnable scripts. We report no task-level evaluation and no host selection rate. Openly licensed and available at https://github.com/K-Dense-AI/scientific-agent-skills.

Abstract:A language-model agent asked to analyse an experiment will usually return working code. Whether the analysis is defensible is a different question. A defensible analysis depends on procedural choices: which test the field accepts, which identifier namespace is authoritative, and which caveats must accompany a result. We present Scientific Agent Skills, an open library of 163 such procedures in 16 areas of practice, including genomics, cheminformatics, medical imaging, study design and scientific communication. Each skill is a directory built around a versioned, human-readable instruction file. An agent loads the file only when a task calls for it; the directory often also contains reference material and runnable scripts. We report no task-level evaluation and no host selection rate. Openly licensed and available at this https URL.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2609.00065 [cs.CL]

(or arXiv:2609.00065v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.00065 Focus to learn more arXiv-issued DOI via DataCite

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Full text under the publisher's open licence · 221 words

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