Open Source Software projects provide onboarding guidelines in CONTRIBUTING files (e.g., CONTRIBUTING.md on GitHub). These files explain, for example, how to find open tasks, pack contributions, and submit code for review. However, these files often do not follow a standard structure, can be overly extensive, and miss barriers commonly found by newcomers. This paper proposes an automated approach to parse these CONTRIBUTING files and assess how they cover onboarding barriers. We manually classified a sample of CONTRIBUTING files according to a model of onboarding barriers 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 how adequate the predictions were from their perspective (75% of the predictions were considered adequate). We also found that CONTRIBUTING files typically do not cover the barriers newcomers face (52% of the analyzed projects were missing at least 3 out of the 6 barriers faced by newcomers; 84% missing at least 2). In particular, our analysis 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 capable of analyzing the content of a given CONTRIBUTING file. Our approach may help community builders identify missing information in the ecosystem of projects they maintain and newcomers to understand what to expect in CONTRIBUTING files.