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    "summary_en": "Use when the user is doing AI/ML work in a scientific domain such as biology, chemistry, physics, astronomy, climate, genomics, materials, medicine, ecology, energy, engineering, math, drug discovery, protein design, weather modeling, theorem proving, single-cell, or PDE solving. Hugging Science is a curated catalog of scientific datasets, models, blog posts, and interactive Spaces. This skill helps discover and use resources via `datasets`, `transformers`, the HF Inference API, `gradio_client`, and methodology citations.",
    "summary_zh": "当用户在生物学、化学、物理学、天文学、气候学、基因组学、材料科学、医学、生态学、能源、工程学、数学、药物发现、蛋白质设计、天气建模、定理证明、单细胞或偏微分方程求解等科学领域进行AI/ML工作时使用。Hugging Science是一个精选的科学数据集、模型、博客文章和交互式Spaces目录。此技能有助于通过`datasets`、`transformers`、HF推理API、`gradio_client`和方法论引用发现和使用资源。",
    "description_en": "Hugging Science is a curated, LLM-friendly index of scientific datasets, models, blog posts, and interactive demos for ML researchers. It provides a static, parseable catalog at huggingscience.co across 17 scientific domains, exposing llms.txt, llms-full.txt, and per-domain topic files. It also references a Hugging Face organization (huggingface.co/hugging-science) with community-submitted datasets, models, and ~27 interactive Spaces. The skill guides discovery and usage of resources via standard Hugging Face tooling: datasets library, transformers, Inference API, and gradio_client. It includes a workflow for identifying relevant domains, fetching catalog content, selecting resources, using them appropriately, and citing methodology blogs. It emphasizes authentication via HF_TOKEN from .env, caution with trust_remote_code and sending data to Spaces, and handling large scientific datasets via streaming. The skill is part of Scientific Agent Skills by K-Dense and includes bundled scripts and reference files.",
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