# Contributions The SDMetrics library welcomes contributions for new metrics! {% hint style="success" %} **Benefits of contribution.** SDMetrics is the largest, standalone library for synthetic data evaluation. With over 400K downloads (and counting!), your metric will reach a wide audience on its way to greater adoption by the synthetic data community. {% endhint %} ## SDMetrics Values The SDMetrics library counts **fairness**, **accuracy** and **explainability** as our top values. #### Fairness SDMetrics is a model-agnostic library. Anyone who has real and synthetic data can use the library, no matter where or how the synthetic data was generated. **Accuracy** The SDMetrics library is verified by thousands of users across the globe. The core maintenance team includes MIT alums and AI researchers, here to ensure that metrics and reports can be trusted. #### Explainability SDMetrics is moving towards intuitive, easily explainable metrics and reports. With years of experience in deployable machine learning systems, we understand that clear communication of simple metrics can make or break your synthetic data project. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.sdv.dev/sdmetrics/resources/contributions.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.