Curate scientific AI datasets, models, and demos from Hugging Face.
npx skills add https://github.com/shushuzn/Rairos --skill hugging-science-shushuzn
Please help me install this Agent Skill. Skill: hugging-science Source: https://github.com/shushuzn/Rairos/tree/main/skills/hugging-science Command: npx skills add https://github.com/shushuzn/Rairos --skill hugging-science-shushuzn
It helps users discover high-signal, curated scientific AI resources (datasets, models, and interactive demos) and then use them correctly for domain-specific research and experimentation.
Ask for a curated dataset and runnable approach for a specific scientific task (for example, “Find me a protein model for mutation effect prediction and tell me how to load it with transformers”).
High-intent search queries and answers about installing and using this skill.
You can find curated scientific ML datasets and models by querying a structured catalog that matches your domain to high-signal Hugging Face resources across 17 scientific topics. It provides actionable pointers to relevant datasets, models, and interactive demos.
To load a protein model for mutation effect prediction with transformers, locate the model via the scientific catalog, then execute it using the transformers library while handling common scientific gotchas like trust_remote_code.
Yes, you can run interactive climate modeling demos using gradio_client. The catalog identifies relevant Gradio Spaces for scientific tasks and provides correct execution paths to call them remotely.
Hugging Face supports genomics and materials science workflows through hosted datasets and models. The catalog indexes these resources across biology, chemistry, and physics to help you locate and operationalize them.
The best way to access Hugging Face datasets for scientific machine learning is through a curated index that maps your specific domain need to the right dataset entries. You then load the data using the datasets library.
You need trust_remote_code when running scientific models with transformers because many domain-specific architectures require custom modeling code. The catalog highlights this requirement as a common gotcha for scientific execution.
Zeyuyang-0420Discover scientific datasets and models on Hugging Face.
Ritabrata-ChakrabortyAutomate model inference and fine-tuning for NLP, vision, audio, and multimodal tasks.
huggingfaceManage Hugging Face Hub resources via the hf CLI.
abcnutsDispatch parallel agents to independently solve multiple unrelated debugging tasks and consolidate results into one report.
