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Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FAI-Research\u002Fautoresearch\u002FSKILL.md",[409,410],{"categoryNo":247,"categoryName":248,"taxonomyType":11,"taxonomyTypeDesc":12},{"categoryNo":316,"categoryName":317,"taxonomyType":11,"taxonomyTypeDesc":12},[405,412,289,290,258,413,414,388,415,416,417,418,419,420,421,422,423,424,425,335,232],"orchestration","code","loop","heartbeat","autonomous","outer loop","experiment","patterns","project","end","direction","Autonomous Research","Two-Loop Architecture","Experiment Orchestration",{"skillNo":427,"name":428,"nameZh":18,"description":429,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":18,"repositoryNo":205,"starsCount":206,"licenseSpdxId":207,"licenseFileUrl":18,"sourceHtmlUrl":430,"categories":431,"keywords":437,"publishedAt":233},"01M6KQ84AZTB06HC97H4GTY3QA","academic-plotting","Generates publication-quality figures for ML papers from research context. 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Use when creating any figure for a conference paper.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FAI-Research\u002Facademic-plotting\u002Facademic-plotting\u002FSKILL.md",[432,435,436],{"categoryNo":433,"categoryName":434,"taxonomyType":11,"taxonomyTypeDesc":12},"01JBSEEDCAT000000000000013","分析与可视化",{"categoryNo":247,"categoryName":248,"taxonomyType":11,"taxonomyTypeDesc":12},{"categoryNo":73,"categoryName":74,"taxonomyType":58,"taxonomyTypeDesc":59},[438,439,440,320,145,441,442,443,290,444,445,446,447,448,418,449,450,451,335,452,232,453],"Academic","plotting","academic plotting","gemini","genai","components","generate","visualization","data","context","auto","diagrams","chart","figures","data-driven","Publication",{"skillNo":455,"name":428,"nameZh":18,"description":429,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":18,"repositoryNo":205,"starsCount":206,"licenseSpdxId":207,"licenseFileUrl":18,"sourceHtmlUrl":456,"categories":457,"keywords":461,"publishedAt":233},"01M6KQ7XHJCMHS1SVGW77YF56E","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FAI-Research\u002Facademic-plotting\u002FSKILL.md",[458,459,460],{"categoryNo":433,"categoryName":434,"taxonomyType":11,"taxonomyTypeDesc":12},{"categoryNo":247,"categoryName":248,"taxonomyType":11,"taxonomyTypeDesc":12},{"categoryNo":73,"categoryName":74,"taxonomyType":58,"taxonomyTypeDesc":59},[438,439,440,320,145,441,442,443,290,444,445,446,447,448,418,449,450,451,335,452,232,453],{"skillNo":463,"name":464,"nameZh":18,"description":465,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":18,"repositoryNo":205,"starsCount":206,"licenseSpdxId":207,"licenseFileUrl":18,"sourceHtmlUrl":466,"categories":467,"keywords":468,"publishedAt":233},"01M6KQ7N6VAGCP97K8JY3WDE55","sentencepiece","Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences\u002Fsec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FAI-Models-and-Architecture\u002Fsentencepiece\u002Fsentencepiece\u002FSKILL.md",[],[464,145,469,470,471,219,472,473,474,475,476,477,478,479,480,481,482,483,484,229,232],"memory","support","Reproducible","text","multilingual","deterministic","algorithms","as","cjk","Tokenization","BPE","Unigram","Language-Independent","CJK Languages","Unicode","you",{"skillNo":486,"name":464,"nameZh":18,"description":465,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":18,"repositoryNo":205,"starsCount":206,"licenseSpdxId":207,"licenseFileUrl":18,"sourceHtmlUrl":487,"categories":488,"keywords":489,"publishedAt":233},"01M6KQ7GBNTN6VPYZBXJ4CK72Y","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FAI-Models-and-Architecture\u002Fsentencepiece\u002FSKILL.md",[],[464,145,469,470,471,219,472,473,474,475,476,477,478,479,480,481,482,483,484,229,232],{"skillNo":491,"name":492,"nameZh":18,"description":493,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":18,"repositoryNo":205,"starsCount":206,"licenseSpdxId":207,"licenseFileUrl":18,"sourceHtmlUrl":494,"categories":495,"keywords":496,"publishedAt":233},"01M6KQ7AZXVT8FHMWT9PJNE5WH","rwkv-architecture","RNN+Transformer hybrid with O(n) inference. Linear time, infinite context, no KV cache. Train like GPT (parallel), infer like RNN (sequential). Linux Foundation AI project. Production at Windows, Office, NeMo. RWKV-7 (March 2025). Models up to 14B parameters.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FAI-Models-and-Architecture\u002Frwkv\u002Frwkv\u002FSKILL.md",[],[290,497,498,499,500,501,219,221,502,447,503,504,505,420,506,507,508,509,229,510],"rwkv","rwkv architecture","windows","linear","NeMo","parallel","up","complexity","time","infer","Model Architecture","2025","at","hybrid",{"skillNo":512,"name":492,"nameZh":18,"description":493,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":18,"repositoryNo":205,"starsCount":206,"licenseSpdxId":207,"licenseFileUrl":18,"sourceHtmlUrl":513,"categories":514,"keywords":515,"publishedAt":233},"01M6KQ76QBGNR8QKRKVRMC57YC","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FAI-Models-and-Architecture\u002Frwkv\u002FSKILL.md",[],[290,497,498,499,500,501,219,221,502,447,503,504,505,420,506,507,508,509,229,510],10877,18]