We present a deep learning model based on an autoencoder for the reconstruction of cranial and facial defects using the Medical Open Network for Artificial Intelligence (MONAI) framework, which has been pre-trained on the MUG500+ and SkullFix dataset. The implementation follows the MONAI contribution guidelines, hence, it can be easily tried out and used, and extended by MONAI users. The primary goal of this paper lies in the investigation of open-sourcing codes and pre-trained deep learning models under the MONAI framework. The pre-trained models generated in this work deliver reasonable results on the cranial and facial reconstruction task and provide an ideal starting-point for other researchers interested in further investigating the topic. We released the codes and the pre-trained model at the official MONAI ‘research contributions’ GitHub repository: https://github.com/Project-MONAI/research-contributions/tree/master/SkullRec. This contribution has two novelties: 1. Pre-training an autoencoder on the MUG500+ and SkullFix dataset for cranial and facial reconstruction using MONAI, and open-sourcing the codes and weights for other MONAI users; 2. Demonstrating that existing MONAI tutorials can be easily adapted to new use cases, such as skull (cranial and facial) reconstruction.