Do Personalized Skills Help Coding Agents? An Empirical Study of Developer Interaction Histories
Large language model (LLM)-powered agents have rapidly evolved from code-completion tools into solvers of complex software engineering tasks. As developers collaborate with coding agents over time, their preferences emerge through repeated interactions and can be used to adapt agent behavior to better meet individual developers' needs. Capturing and reusing these preferences may reduce repeated corrections and improve developer-agent collaboration. Agent skills provide a lightweight mechanism for transferring experience without modifying model parameters. However, existing work primarily focuses on task-specific skills, and it remains unclear whether developer-specific skills distilled fr...
| Authors | Shuyan Huang, Kai Du, Andrew Lan |
|---|---|
| Primary category | cs.SE |
| Published | 10 August 2026 |
| Last updated at source | 15 August 2026 |
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