Release OpenSkill artifacts (models, dataset) on Hugging Face

Hi @bbsngg 🤗

Niels here from the open-source team at Hugging Face. I discovered your work through Hugging Face's daily papers as yours got featured: https://huggingface.co/papers/2606.06741.
The paper page lets people discuss about your paper and lets them find artifacts about it (your models, datasets or demo for instance), you can also claim
the paper as yours which will show up on your public profile at HF, add Github and project page URLs.

I saw on your GitHub repository that the code for the OpenSkill framework, as well as the skills and benchmark, are "on the way". It'd be great to make these artifacts available on the 🤗 hub once they are ready, to improve their discoverability/visibility.
We can add tags so that people find them when filtering https://huggingface.co/models and https://huggingface.co/datasets.

Uploading models (for OpenSkill agents/skills)

See here for a guide: https://huggingface.co/docs/hub/models-uploading.

In this case, we could leverage the PyTorchModelHubMixin class which adds from_pretrained and push_to_hub to any custom nn.Module. Alternatively, one can leverages the hf_hub_download one-liner to download a checkpoint from the hub.

We encourage researchers to push each model checkpoint to a separate model repository, so that things like download stats also work. We can then also link the checkpoints to the paper page.

Uploading dataset (for SkillsBench data, if applicable)

Would be awesome to make the dataset available on 🤗 , so that people can do:

from datasets import load_dataset

dataset = load_dataset("your-hf-org-or-username/your-dataset")

See here for a guide: https://huggingface.co/docs/datasets/loading.

Besides that, there's the dataset viewer which allows people to quickly explore the first few rows of the data in the browser.

Let me know if you're interested/need any help regarding this!

Cheers,

Niels
ML Engineer @ HF 🤗

Start by checking the repository's forthcoming OpenSkill code, skills, and benchmark artifacts, then review the Hugging Face model-uploading and dataset-loading guides linked in the issue. Done means the ready model checkpoints and, if applicable, SkillsBench dataset are published on Hugging Face and linked to the paper page.