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Supports initializing repos, defining configs\u002Fsystem prompts, streaming row updates, and SQL-based dataset querying\u002Ftransformation. Designed to work alongside HF MCP server for comprehensive dataset workflows.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fdata-engineering\u002Fhugging-face-datasets\u002FSKILL.md",[504,505,506],{"categoryNo":339,"categoryName":340,"taxonomyType":11,"taxonomyTypeDesc":12},{"categoryNo":239,"categoryName":240,"taxonomyType":11,"taxonomyTypeDesc":12},{"categoryNo":73,"categoryName":74,"taxonomyType":58,"taxonomyTypeDesc":59},[508,509,510,511,512,272,513,244,514,515,516,517,442,518,519,520,521,522,523,255],"hugging face","datasets","hugging face datasets","face","MCP","repos","streaming","server","HuggingFace","system","hf","hub","manage","duckdb","transformation","hf mcp",{"skillNo":525,"name":526,"nameZh":18,"description":527,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":528,"categories":529,"keywords":531,"publishedAt":231},"01M6XFT0TNSNBTB95RE60QS1AC","hdf5-pde-data-loading","Patterns for loading PDE simulation datasets (PDEBench, PhiFlow, JAX-CFD) from HDF5 files. Handles layout detection (single tensor vs separate variables), spatial\u002Ftemporal downsampling, multi-variable systems, HuggingFace and DaRUS data sources, and efficient PyTorch DataLoader creation. Use when preparing PDE data for neural operator training.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fdata-engineering\u002Fhdf5-pde-data-loading\u002FSKILL.md",[530],{"categoryNo":73,"categoryName":74,"taxonomyType":58,"taxonomyTypeDesc":59},[532,439,533,534,535,536,537,290,538,539,467,540,541,542,220,543,544,545,255,546,230,547],"data loading","pde","loading","HDF5","hdf5 pde data loading","pytorch","operator","simulation","patterns","layout","creation","sources","CFD","Variables","data sources","Neural Operator",{"skillNo":549,"name":550,"nameZh":18,"description":551,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":552,"categories":553,"keywords":554,"publishedAt":231},"01M6XFS1WDCS6CQPCEDPWY1Y76","torch-geometric","Graph Neural Networks (PyG). Node\u002Fgraph classification, link prediction, GCN, GAT, GraphSAGE, heterogeneous graphs, molecular property prediction, for geometric deep learning.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fcoding\u002Ftorch_geometric\u002FSKILL.md",[],[555,556,557,558,559,560,561,562,415,563,564,565,566,567,220,568,255,569,570,571],"torch geometric","deep learning","neural networks","graph","networks","classification","learning","coding","Neural","deep","graphs","molecular","gat","link","graph neural networks","prediction","Molecules",{"skillNo":573,"name":574,"nameZh":18,"description":575,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":576,"categories":577,"keywords":581,"publishedAt":231},"01M6XFR8Y27DT83XR92FSRXSD0","statistical-analysis","Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fcoding\u002Fstatistical-analysis\u002FSKILL.md",[578],{"categoryNo":579,"categoryName":580,"taxonomyType":11,"taxonomyTypeDesc":12},"01JBSEEDCAT000000000000008","测试与质量",[582,583,584,585,586,439,587,562,588,386,589,590,591,220,592,229,593,255,594,230],"analysis","statistical analysis","research","test","reporting","statsmodels","results","power","selection","tests","with","implementing","academic research",{"skillNo":596,"name":597,"nameZh":18,"description":598,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":599,"categories":600,"keywords":601,"publishedAt":231},"01M6XFR3Q3A9TNSTBFE74KR1DJ","slime-rl-training","Provides guidance for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fcoding\u002Fslime\u002FSKILL.md",[],[290,602,603,604,272,245,605,439,291,561,562,606,607,608,220,609,610,593,255,230],"rl","slime rl training","slime","SGLang","router","scaling","glm","megatron","Megatron-LM",{"skillNo":612,"name":613,"nameZh":18,"description":614,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":615,"categories":616,"keywords":618,"publishedAt":231},"01M6XFQYYVWQF4F73AKJ5BMD7H","simpy","Process-based discrete-event simulation framework in Python. Use this skill when building simulations of systems with processes, queues, resources, and time-based events such as manufacturing systems, service operations, network traffic, logistics, or any system where entities interact with shared resources over time.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fcoding\u002Fsimpy\u002FSKILL.md",[617],{"categoryNo":239,"categoryName":240,"taxonomyType":11,"taxonomyTypeDesc":12},[613,431,245,619,539,517,562,386,620,621,622,220,623,624,625,626,627,628,230,629],"network","time","shared","as","events","resources","manufacturing","event","traffic","queues","Simulations",10823,18]