Recently, community search in directed graphs has garnered significant attention, particularly with the introduction of the D-truss, also known as the \((k_c, k_f)\) -truss, which serves as a robust subgraph structure in directed graphs. In this structure, each edge must form cycle (flow) triangles with at least \(k_c\) ( \(k_f\) ) vertices. The goal of maximal D-truss community search is to identify the largest D-truss for a given query vertex in a directed graph. However, existing methods often lack both efficiency and effectiveness when applied to large-scale and dynamic directed graphs. To overcome these limitations, we investigate the problem of maximal triangle-connected D-truss community search (MDTCS) in dynamic directed graphs in this paper. First, we introduce the concept of triangle connectivity. Then, we explore an efficient index, named PartialTruss, which effectively captures the partial correlation of edges within a D-truss community based on triangle connectivity. Next, an effective search algorithm that utilizes the PartialTruss index is proposed. Furthermore, we propose a novel method for efficiently maintaining the index in response to dynamic updates in directed graphs. Finally, we conduct extensive experiments on real-world networks, which demonstrate that our community search method, leveraging the PartialTruss index, achieves an improvement in efficiency by 1–2 orders of magnitude compared to state-of-the-art methods.