EffiSkill: Agent Skill Based Automated Code Efficiency Optimization

• URL: http://arxiv.org/abs/2603.27850v1
• Date: Sun, 29 Mar 2026 20:09:57 GMT
• Title: EffiSkill: Agent Skill Based Automated Code Efficiency Optimization
• Abstract summary: EffiSkill is a framework for code-efficiency optimization that builds a portable optimization toolbox for LLM-based agents.<n>EffiSkill adopts a two-stage design: Stage I mines Operator and Meta Skills from large-scale slow/fast program pairs to build a skill library.<n>Stage II applies this library to unseen programs through execution-free diagnosis, skill retrieval, plan composition, and candidate generation.
• Score: 18.68243664364758
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Code efficiency is a fundamental aspect of software quality, yet how to harness large language models (LLMs) to optimize programs remains challenging. Prior approaches have sought for one-shot rewriting, retrieved exemplars, or prompt-based search, but they do not explicitly distill reusable optimization knowledge, which limits generalization beyond individual instances. In this paper, we present EffiSkill, a framework for code-efficiency optimization that builds a portable optimization toolbox for LLM-based agents. The key idea is to model recurring slow-to-fast transformations as reusable agent skills that capture both concrete transformation mechanisms and higher-level optimization strategies. EffiSkill adopts a two-stage design: Stage I mines Operator and Meta Skills from large-scale slow/fast program pairs to build a skill library; Stage II applies this library to unseen programs through execution-free diagnosis, skill retrieval, plan composition, and candidate generation, without runtime feedback. Results on EffiBench-X show that EffiSkill achieves higher optimization success rates, improving over the strongest baseline by 3.69 to 12.52 percentage points across model and language settings. These findings suggest that mechanism-level skill reuse provides a useful foundation for execution-free code optimization, and that the resulting skill library can serve as a reusable resource for broader agent workflows.

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