Your README's Overall Grade

Your overall score is calculated as an average of your README's headers, code samples, text, and image scores. Each section provides insights and suggestions for improving the quality of your README relative to the 10,000 popular repositories we've analyzed.

These grades are not definitive. Rather, they're the result of machine learning, and are provided on a "best effort" basis. We recognize that the model doesn't account for all the complexity and nuance a README has. Ultimately, you should use your own judgement about what to include, remove, and ignore. Inevitably, there will be results that don't make sense. tl;dr data science is hard.

Feel free to contact us @Algorithmia or by email if you feel something is particularly egregious.

Model Assumptions

Popular repositories probably have a good, well-documented README

Popular repositories have more stars than bad repositories

Each programming language has unique characteristics

In general, we found a higher correlation between a README's quality and the specific headers, and text used throughout. Conversely, we found a lower correlation between the quality and the number of code samples, and the number of images in the README.

In order to correct this, we removed any repository that had zero images, or code snippets from our model, because these are helpful, additive features.

Note: If the README you analyze falls outside of the top 10 languages (i.e. Javascript, Java, Ruby, Python, PHP, HTML, CSS, C++, C, or C#), we default to using a model trained on all of the languages.

How was this made?

Learn more about our data science approach to analyzing GitHub README's, and find the complete code sample in the Algorithmia Sample Apps repo here, which earned a B grade. 😎