SkillGNN: Large Language Model-Guided Component Evolution for Knowledge Graph Completion

Graph neural networks (GNNs) for knowledge graph completion still rely heavily on manually designed components. Large language models (LLMs) can generate alternative designs, but feedback on individual candidates does not by itself ensure that design experience accumulates across iterations. We present SkillGNN, a framework that couples component-wise GNN evolution with the refinement of a reusable textual design Skill. The Skill guides an LLM agent to propose local computations within a fixed component interface while preserving the surrounding GNN architecture. A task-grounded evaluator combines validity and structural checks with subgraph training, providing diagnostic feedback that SkillOpt uses to refine the Skill for subsequent proposals. Experiments on WN18RR and FB15K-237 show that the evolved NBFNet scorers improve full-graph link prediction over NBFNet and one-shot LLM component design when trained from scratch. Additional experiments examine the influence of the design LLM and show improvements from evolving NBFNet's node-update component and CompGCN's scoring component.