Effectively onboarding newcomers is essential for the success of open source projects. These projects often provide onboarding guidelines in their ‘CONTRIBUTING’ files (e.g., CONTRIBUTING.md on GitHub). These files explain, for example, how to find open tasks, create contribution packs, and submit code for review. However, these files often do not follow a standard structure, can be too large, and miss barriers commonly found by newcomers. In this paper, we propose an automated approach to parse these CONTRIBUTING files and assess how they address onboarding barriers. We manually classified a sample of files according to a model of onboarding bar- riers from the literature, trained a machine learning classifier that automatically predicts the categories of each paragraph (precision: 0.655, recall: 0.662), and surveyed developers to investigate their perspective of the predictions’ adequacy (75% of the predictions were considered adequate). We found that CONTRIBUTING files typically do not cover the barriers newcomers face (52% of the analyzed projects missed at least 3 out of the 6 barriers faced by newcomers; 84% missed at least 2). Our analysis also revealed that information about choosing a task and talking with the community, two of the most recurrent barriers newcomers face, are neglected in more than 75% of the projects. We made available our classifier as an online service that analyzes the content of a given CONTRIBUTING file. Our approach may help community builders identify missing information in the project ecosystem they maintain and newcomers can understand what to expect in CONTRIBUTING files.