The field of natural language processing (NLP) is making significant strides in mental health and skill assessment. Researchers are exploring the use of large language models (LLMs) and other advanced NLP techniques to identify features of personal and professional skills, detect mental health conditions such as bipolar disorder, and predict personality traits. These innovative approaches have the potential to improve the accuracy and efficiency of mental health assessments and skill evaluations, which can lead to better outcomes for individuals and organizations. Noteworthy papers in this area include one that demonstrates the effectiveness of latent space fusion for predicting daily depressive symptoms, and another that showcases the use of comparative learning for efficient story point estimation in agile software development. Another notable paper presents a deep learning-based Forced-Choice Neural Cognitive Diagnostic Model for personality testing, which overcomes the limitations of traditional models and is applicable to various item block types. Overall, the field is moving towards more advanced and innovative uses of NLP for mental health and skill assessment, with a focus on improving accuracy, efficiency, and interpretability.