Together with other scikit-learn developers we’ve created an umbrella organization for scikit-learn-related projects named scikit-learn-contrib. The idea is for this organization to host projects that are deemed too specific or too experimental to be included in the scikit-learn codebase but still offer an API which is compatible with scikit-learn and would like to benefit of the visibility of being labeled as scikit-learn-compatible.
We’ve set two requirements to being under this umbrella right now (this might evolve in the future). The first requirement is to have a scikit-learn compatible API, i.e., to follow the guide on the scikit-learn documentation so that objects can be used by scikit-learn meta-estimators (such as GridSearchCV). The second condition is that projects should be actively maintaned and have a high-quality codebase. Judging the quality of a codebase is difficult and subjective, but we agreed that at the bare minimum, the source code should be tested using continuous integration tools such as travis and reach a good test coverage (above 80%). More information is not available on the scikit-learn-contrib repository.
The first project to be hosted by this organization is lightning, but we hope that others will follow. If you would like to submit a new project, open an issue at the main project and we will look into it. There is also a project template for new and old projects.