Specification-Driven Application Skeleton Generation Using a Multi-agent System.

Large Language Models (LLMs) have shown significant promise in automating repetitive tasks, particularly in code generation. However, most existing approaches focus on generating isolated code blocks, often lacking global context and coherence across an entire application. Key limitations include the absence of interactive previews, limited support for custom modifications, and insufficient attention to code quality and compliance with specifications. To address these challenges and accelerate application prototyping, we propose a multi-agent approach for generating complete application skeletons from textual specifications. Our framework enables real-time interactive previews and allows users to iteratively refine the generated applications through a conversational agent. To ensure quality and specification compliance, we employ an LLM-based evaluator that assesses the generated code. Once validated, the application skeleton can be downloaded for further development or deployment. This approach aims to bridge the gap between intent and implementation, streamlining early-stage software design and development. We illustrate our approach through a case study, demonstrating the applicability and effectiveness of each step in the proposed method.