Shuyan Huang

Do Personalized Skills Help Coding Agents? An Empirical Study of Developer Interaction Histories

This work proposes a framework for extracting reusable developer preferences from interaction traces, generates personalized skills through rule-based bootstrapping and evidence-grounded refinement, and evaluates them using a reproducible replay framework with an interactive, trajectory-conditioned LLM-based human developer simulator.