Skill Issue: Lessons from Optimizing Repository SKILLs for Coding Agents

Abstract:Coding agents increasingly read repository knowledge from SKILLs --- plain \texttt{.md} files versioned alongside the code. Recent work synthesizes these files automatically, by optimizing the document against a benchmark. A bare repository comes with no benchmark, and the synthetic tasks prior work builds are small enough that a capable agent saturates them with no document at all. We mine harder tasks --- merged pull requests of the repository, reverted at a single frozen base commit; and score a candidate document by whether the same agent does better with it than without it. On three Kotlin repositories, the documents GEPA finds raise this score by $4.9$pp on average, and the ones SkillOpt finds leave it where it started, $0.1$pp above the seed. The GEPA gain matches what prior work reports with the same optimizer, and at the dataset size a single repository supplies it cannot be separated from the agent's run-to-run variance; settling that would take more tasks than one repository's history yields. The documents themselves read better than the score: a maintainer of one repository found in them knowledge one only gets by working in the project.

Artificial Intelligence (cs.AI)

arXiv:2609.12742 [cs.AI]

(or arXiv:2609.12742v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.12742 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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