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Scientific Agent Skills: A Library of Procedural Knowledge for Research Agents

by Timothy Kassis, Vinayak Agarwal, Yuhuan He, Darshil Patel and Aubrey M. BruecknerarXivpublished

Hraness cites a source capture. The source author remains the source.

gist

Scientific Agent Skills is K-Dense's open library of 163 agent-loadable procedures across 16 research areas, from genomics and cheminformatics to study design and scientific communication. Each skill is a directory whose versioned instruction file loads only when a task needs it, often with references and scripts. The paper reports no task-level evaluation; it measures documentation cost instead: resident descriptions of all skills take 7.1% of a 200,000-token window, and the median documented workflow fits in 23.9%, though loading every reference overflows 29 of 46 workflows.

ideas

  • Working code is not defensible analysis. Field-specific procedural choices decide which test applies, which identifier namespace is authoritative, and which caveats must travel with a result.
  • Skills are authored procedures, not retrieval passages. Each skill is a directory built around a human-readable instruction file the agent loads only when the task appears to call for it.
  • Progressive disclosure keeps standing context small. Always-resident name-and-description rows for 163 skills cost 7.1% of a 200,000-token window; instruction bodies and references stay deferred.
  • Reference loading is the overflow risk. Median documented workflows fit at 23.9% with instructions alone, but 29 of 46 exceed the window if every named reference file is loaded.
  • This is a resource paper, not a task eval. The authors report no host selection rate and no controlled study that skills improve scientific outcomes.

quotes

A language-model agent asked to analyse an experiment will usually return working code.

Timothy Kassis et al., opening the procedural-failure frame.

Whether the analysis is defensible is a different question.

Timothy Kassis et al., separating runnable code from scientific validity.

Each skill is a directory built around a versioned, human-readable instruction file.

Timothy Kassis et al., defining the skill payload.

We report no task-level evaluation and no host selection rate.

Timothy Kassis et al., stating what the paper does not measure.