Meta-Learning (ML) or "learning-to-learn" techniques have been established for fast and data-efficient learning within and across task domains. Neuromorphic embedded learning systems can strongly benefit from ML by acquiring prior knowledge in a relevant task domain. Community-led neruomorphic benchmarks and libraries for ML are still missing. This project/challenge address these gaps in two steps: 1) Defining event-based meta-learning libraries and benchmarks; and 2) Creating and participating in a cross domain meta learning challenges.