UniRepLKNet proposes architectural guidelines for building large-kernel CNNs that deliver universal perception across multiple modalities. It unifies image, audio, video, and time-series processing within a single ConvNet architecture, outperforming modality-specific models. The work builds on structural re-parameterization principles and provides pretrained models on Hugging Face.