Researchers have introduced the "Super Library Agent" problem, which addresses the inefficiencies inherent in using LLM coding agents to manage portfolios of related applications. Traditional application-by-application workflows often result in duplicated domain logic, increased verbosity, and structural erosion across independent codebases. To mitigate these issues, the authors propose a framework where an agent sequentially generates N related applications while simultaneously maintaining a shared "Super Library" of reusable components. The proposed methodology moves beyond simple sequential scaffolding—which typically suffers from low extraction recall and fragile dependency migration—by implementing candidate-guided extraction via code chunk summaries, pre-extraction codebase consolidation, and context-aware migration utilizing call-graph information and extraction traces. Experimental evaluations conducted on WebGen-Bench and PaperBench demonstrate that this approach successfully preserves application functionality while significantly reducing redundancy and the overall token footprint compared to zero-shot methods. Furthermore, the method avoids the structural erosion common in naive library construction and achieves measurable reductions in both Lines of Code (LOC) and Minimum Description Length (MDL), facilitating more efficient and maintainable software portfolios.