Purpose: Providing manual formative feedbacks to trainees in minimally invasive surgery is time-consuming and requires the observations of experts whose availability is often limited. The existing automatic assessment methods often provide a coarse and unidimensional evaluation that lacks formative value. Method: We trained a multi-layer perceptron to establish the multiple non-linear regression mapping a set of kinematic metrics to five scores representing six surgical technical criteria. We tested our method on two datasets: A new in-house laparoscopy dataset and a well-known robotic laparoscopy dataset. Results: Our results report that a simple deep learning model using motion metrics as inputs outperforms the state-of-the-art method based on a more elaborate end-to-end neural network. We also show that our method is suitable for both robotic and nonrobotic laparoscopic skills assessment. Moreover, the provided assessment is formative by rating specific technical skills, showing the student where to direct her/his training efforts. Conclusions: We developed a rather simple method for the automatic assessment, not only of the global surgical technical level, but of precise surgical technical skills. Our method shows that descriptive motion metrics combined with a simple deep learning model is powerful enough to capture and extract high level concepts such as surgical flow of operation or tissue handling. Such method can simplify access to a formative surgical training in both robotic and non-robotic laparoscopy.