Access to appropriate community and healthcare resources is critical for addressing both medical and non-medical determinants of health. However, developing reliable recommendation systems for such resources is challenging due to the scarcity of domain-specific structured knowledge. Although knowledge graphs (KGs) are a promising solution for enriching decision-making in recommendation systems, constructing a domain-specific KG typically requires significant manual effort and curated datasets, resources that are not always available in community health contexts.In this study, we explore the feasibility of repurposing an existing knowledge graph originally designed for precision medicine, PrimeKG, to support resource recommendation in a broader community health context. We introduce CHARM (Community Health Assistance via Reused Medical knowledge graphs), a recommendation system framework that integrates Large Language Models (LLMs) with adapted KGs. CHARM utilizes LLMs to interpret unstructured physician notes and extract patient needs, while the repurposed knowledge graph supplements contextual medical and social insights to enhance recommendation accuracy.We examine four strategies for incorporating knowledge graphs into the LLM-driven pipeline and evaluate their effectiveness using 58 diverse patient scenarios. Our results demonstrate that integrating the knowledge graph between the keyword extraction and problem identification stages (the CHARM strategy) yields the highest performance in both keyword and resource recommendation tasks. Notably, the approach performs well even for non-biomedical scenarios, validating the transferability of the precision medicine KG to broader healthcare domains.This work contributes a scalable, hybrid methodology for community resource recommendation that reduces reliance on domain-specific KGs. It also provides empirical evidence supporting the cross-domain reuse of biomedical KGs to assist in addressing the social determinants of health.