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Use this for direct HTTP\u002FREST work or UniProt-specific control.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33418,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Funiprot-database\u002FSKILL.md",[211],{"categoryNo":212,"categoryName":213,"taxonomyType":53,"taxonomyTypeDesc":54},"01JBSEEDCAT000000000000010","后端与接口",[215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236],"database","uniprot database","python","workflows","http","services","REST","bioservices","api","access","direct","work","use","databases","id","mapping","with","interface","for","swiss","FASTA","Protein","2026-11-10 10:12:30",{"skillNo":239,"name":240,"nameZh":13,"description":241,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":242,"categories":243,"keywords":247,"publishedAt":237},"01M6AFQGEA3HRVKV132D2EAMDF","umap-learn","UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D\u002F3D visualization, clustering preprocessing (HDBSCAN), supervised\u002Fparametric UMAP, for high-dimensional data.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fumap-learn\u002FSKILL.md",[244],{"categoryNo":245,"categoryName":246,"taxonomyType":53,"taxonomyTypeDesc":54},"01JBSEEDCAT000000000000013","分析与可视化",[248,249,250,251,252,253,254,255,256,257,258,259,260,233,261,262,263,264],"learn","umap learn","umap","visualization","fast","data","clustering","3D","learning","preprocessing","dimensionality reduction","manifold","high","2d","supervised","parametric","Nonlinear",{"skillNo":266,"name":267,"nameZh":13,"description":268,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":269,"categories":270,"keywords":275,"publishedAt":237},"01M6AFQBGFE8H59D97E5V89RVW","treatment-plans","Generate concise (3-4 page), focused medical treatment plans in LaTeX\u002FPDF format for all clinical specialties. 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Prioritizes brevity and clinical actionability.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Ftreatment-plans\u002FSKILL.md",[271,274],{"categoryNo":272,"categoryName":273,"taxonomyType":53,"taxonomyTypeDesc":54},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":44,"categoryName":45,"taxonomyType":6,"taxonomyTypeDesc":7},[276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299],"treatment plans","plans","PDF","generate","compliance","hipaa","regulatory compliance","LaTeX","Citations","Bash","text","read","write","edit","evidence","goal","mental health","formatting","medical","clinical","professional","brevity","therapy","smart goal",{"skillNo":301,"name":302,"nameZh":13,"description":303,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":304,"categories":305,"keywords":312,"publishedAt":237},"01M6AFQ7WPC4E8YTWQ3ZA1GSH4","transformers","This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Ftransformers\u002FSKILL.md",[306,309],{"categoryNo":307,"categoryName":308,"taxonomyType":53,"taxonomyTypeDesc":54},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":310,"categoryName":311,"taxonomyType":53,"taxonomyTypeDesc":54},"01JBSEEDCAT000000000000023","Skill与智能体开发",[313,314,315,316,317,318,319,320,321,322,323,286,324,325,326,327,328,329,330,331,332,233,333],"Transformers","image","tasks","speech","multimodal","Fine-Tuning","Computer Vision","detection","tuning","classification","Audio","computer","speech recognition","question","vision","translation","natural language","natural language processing","processing","object detection","when",{"skillNo":335,"name":336,"nameZh":13,"description":337,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":338,"categories":339,"keywords":341,"publishedAt":237},"01M6AFQ4EH4PP7HWC8T3525A4W","torchdrug","Graph-based drug discovery toolkit. Molecular property prediction (ADMET), protein modeling, knowledge graph reasoning, molecular generation, retrosynthesis, GNNs (GIN, GAT, SchNet), 40+ datasets, for PyTorch-based ML on molecules, proteins, and biomedical graphs.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Ftorchdrug\u002FSKILL.md",[340],{"categoryNo":310,"categoryName":311,"taxonomyType":53,"taxonomyTypeDesc":54},[336,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,233,358,236,359],"pytorch","ml","toolkit","reasoning","graph","knowledge graph","discovery","knowledge","datasets","biomedical","proteins","generation","modeling","molecular","gat","gin","prediction","ADMET",{"skillNo":361,"name":362,"nameZh":13,"description":363,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":364,"categories":365,"keywords":366,"publishedAt":237},"01M6AFQ12HAETYMGJSMQED9XHD","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\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Ftorch_geometric\u002FSKILL.md",[],[367,368,369,346,370,322,256,371,372,373,374,355,356,375,233,376,358],"torch geometric","deep learning","neural networks","networks","node","Neural","deep","graphs","link","graph neural networks",{"skillNo":378,"name":379,"nameZh":13,"description":380,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":381,"categories":382,"keywords":385,"publishedAt":237},"01M6AFPWXREHJXD2YNFG93E83Z","sympy","Use this skill when working with symbolic mathematics in Python. This skill should be used for symbolic computation tasks including solving equations algebraically, performing calculus operations (derivatives, integrals, limits), manipulating algebraic expressions, working with matrices symbolically, physics calculations, number theory problems, geometry computations, and generating executable code from mathematical expressions. Apply this skill when the user needs exact symbolic results rather than numerical approximations, or when working with mathematical formulas that contain variables and parameters.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fsympy\u002FSKILL.md",[383,384],{"categoryNo":310,"categoryName":311,"taxonomyType":53,"taxonomyTypeDesc":54},{"categoryNo":5,"categoryName":8,"taxonomyType":6,"taxonomyTypeDesc":7},[379,217,315,386,227,387,388,389,390,391,231,392,393,394,233,395,396,397,333,398],"code","mathematics","physics","geometry","integrals","contain","Variables","formulas","number","numerical","apply","limits","computation",{"skillNo":400,"name":401,"nameZh":13,"description":402,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":403,"categories":404,"keywords":408,"publishedAt":237},"01M6AFPSKQ1ME2VP9NCWCBM46Z","string-database","Query STRING API for protein-protein interactions (59M proteins, 20B interactions). Network analysis, GO\u002FKEGG enrichment, interaction discovery, 5000+ species, for systems biology.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fstring-database\u002FSKILL.md",[405,406,407],{"categoryNo":212,"categoryName":213,"taxonomyType":53,"taxonomyTypeDesc":54},{"categoryNo":310,"categoryName":311,"taxonomyType":53,"taxonomyTypeDesc":54},{"categoryNo":5,"categoryName":8,"taxonomyType":6,"taxonomyTypeDesc":7},[215,409,410,411,412,413,414,223,348,415,352,416,417,418,419,420,421,233,236],"string database","STRING","go","network","enrichment","analysis","query","systems biology","systems","interaction","KEGG","biology","network analysis",{"skillNo":423,"name":424,"nameZh":13,"description":425,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":426,"categories":427,"keywords":429,"publishedAt":237},"01M6AFPP4F6EEDTH9ZA2K927HG","statsmodels","Statistical modeling toolkit. OLS, GLM, logistic, ARIMA, time series, hypothesis tests, diagnostics, AIC\u002FBIC, for rigorous statistical inference and econometric analysis.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fstatsmodels\u002FSKILL.md",[428],{"categoryNo":310,"categoryName":311,"taxonomyType":53,"taxonomyTypeDesc":54},[424,430,431,344,432,433,414,434,435,436,437,354,438,439,233],"inference","diagnostics","time series","hypothesis","time","statistical inference","arima","series","tests","glm",{"skillNo":441,"name":442,"nameZh":13,"description":443,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":444,"categories":445,"keywords":450,"publishedAt":237},"01M6AFPJ9A4DQNZKY955HKF9R8","statistical-analysis","Statistical analysis toolkit. Hypothesis tests (t-test, ANOVA, chi-square), regression, correlation, Bayesian stats, power analysis, assumption checks, APA reporting, for academic research.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fstatistical-analysis\u002FSKILL.md",[446,449],{"categoryNo":447,"categoryName":448,"taxonomyType":53,"taxonomyTypeDesc":54},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":310,"categoryName":311,"taxonomyType":53,"taxonomyTypeDesc":54},[414,451,452,453,344,454,455,456,433,457,458,459,460,461,462,438,463,233,464,465],"statistical analysis","research","test","stats","reporting","correlation","regression","power","square","ANOVA","t-test","chi-square","chi","academic research","Bayesian",{"skillNo":467,"name":468,"nameZh":13,"description":469,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":470,"categories":471,"keywords":473,"publishedAt":237},"01M6AFPEZNZ9FP0T9WY6VJP65Z","stable-baselines3","Use this skill for reinforcement learning tasks including training RL agents (PPO, SAC, DQN, TD3, DDPG, A2C, etc.), creating custom Gym environments, implementing callbacks for monitoring and control, using vectorized environments for parallel training, and integrating with deep RL workflows. This skill should be used when users request RL algorithm implementation, agent training, environment design, or RL experimentation.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fstable-baselines3\u002FSKILL.md",[472],{"categoryNo":310,"categoryName":311,"taxonomyType":53,"taxonomyTypeDesc":54},[474,475,218,476,315,477,478,479,480,256,481,482,373,483,484,485,486,233,487,488,333],"stable baselines3","design","training","reinforcement learning","vectorized","monitoring","PPO","parallel","environment","experimentation","Callbacks","environments","implementing","custom","request",{"skillNo":490,"name":491,"nameZh":13,"description":492,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":493,"categories":494,"keywords":496,"publishedAt":237},"01M6AFPB84JNQQE5BXC1EA893Q","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\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fsimpy\u002FSKILL.md",[495],{"categoryNo":310,"categoryName":311,"taxonomyType":53,"taxonomyTypeDesc":54},[491,217,497,412,498,499,227,434,500,501,502,503,504,505,231,506,507,333,508,509],"framework","simulation","system","shared","as","events","resources","manufacturing","event","traffic","queues","entities","Simulations",{"skillNo":511,"name":512,"nameZh":13,"description":513,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":514,"categories":515,"keywords":520,"publishedAt":237},"01M6AFP4WH776W9JVQ5Y07VABG","seaborn","Statistical visualization. Scatter, box, violin, heatmaps, pair plots, regression, correlation matrices, KDE, faceted plots, for exploratory analysis and publication figures.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fseaborn\u002FSKILL.md",[516,517],{"categoryNo":245,"categoryName":246,"taxonomyType":53,"taxonomyTypeDesc":54},{"categoryNo":518,"categoryName":519,"taxonomyType":53,"taxonomyTypeDesc":54},"01JBSEEDCAT000000000000015","视觉设计",[512,251,456,414,521,457,522,523,524,233,525,526],"box","pair","matrices","figures","Publication","Heatmaps",{"skillNo":528,"name":529,"nameZh":13,"description":530,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":531,"categories":532,"keywords":534,"publishedAt":237},"01M6AFNXQMFEH2TWRX67JDBQF6","scikit-survival","Comprehensive toolkit for survival analysis and time-to-event modeling in Python using scikit-survival. Use this skill when working with censored survival data, performing time-to-event analysis, fitting Cox models, Random Survival Forests, Gradient Boosting models, or Survival SVMs, evaluating survival predictions with concordance index or Brier score, handling competing risks, or implementing any survival analysis workflow with the scikit-survival library.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fscikit-survival\u002FSKILL.md",[533],{"categoryNo":310,"categoryName":311,"taxonomyType":53,"taxonomyTypeDesc":54},[535,536,217,537,344,538,539,253,414,227,434,354,540,541,542,505,543,486,233,544,333],"scikit survival","survival","workflow","index","library","gradient boosting","Cox","predictions","random","gradient",{"skillNo":546,"name":547,"nameZh":13,"description":548,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":549,"categories":550,"keywords":551,"publishedAt":237},"01M6AFNT49T2QK8BPMZS93N2BK","scikit-learn","Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fscikit-learn\u002FSKILL.md",[],[552,248,217,553,554,343,555,556,254,321,557,457,558,547,257,559,560,561,562,233,262,333,563],"scikit learn","machine learning","documentation","best practices","Evaluation","model","algorithms","unsupervised learning","unsupervised","ML Pipelines","Hyperparameter Tuning","Supervised Learning",{"skillNo":565,"name":566,"nameZh":13,"description":567,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":568,"categories":569,"keywords":570,"publishedAt":237},"01M6AFNQ6WKGJ6Z1YA67XEXGR2","scikit-bio","Biological data toolkit. Sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha\u002Fbeta, UniFrac), ordination (PCoA), PERMANOVA, FASTA\u002FNewick I\u002FO, for microbiome analysis.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fscikit-bio\u002FSKILL.md",[],[571,344,572,573,574,253,414,575,576,577,578,233,579,235],"scikit bio","metrics","alpha","beta","diversity","sequence analysis","microbiome","trees","sequence",{"skillNo":581,"name":582,"nameZh":13,"description":583,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":584,"categories":585,"keywords":591,"publishedAt":237},"01M6AFNKNR210473R6PJXHQVPN","scientific-writing","Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process: (1) create section outlines with key points using research-lookup, (2) convert to flowing prose. IMRAD structure, citations (APA\u002FAMA\u002FVancouver), figures\u002Ftables, reporting guidelines (CONSORT\u002FSTROBE\u002FPRISMA), for research papers and journal submissions.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fscientific-writing\u002FSKILL.md",[586,589,590],{"categoryNo":587,"categoryName":588,"taxonomyType":53,"taxonomyTypeDesc":54},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":310,"categoryName":311,"taxonomyType":53,"taxonomyTypeDesc":54},{"categoryNo":30,"categoryName":31,"taxonomyType":6,"taxonomyTypeDesc":7},[592,593,594,452,595,596,597,455,598,284,599,600,285,287,288,289,601,602,373,603,604,605,606,607],"writing","scientific writing","scientific","guidelines","core","create","deep research","Outlines","lookup","never","structure","full","prisma","journal","papers","core skill",{"skillNo":609,"name":610,"nameZh":13,"description":611,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":612,"categories":613,"keywords":617,"publishedAt":237},"01M6AFNFXD475MWNKFD3J679S3","scientific-visualization","Create publication figures with matplotlib\u002Fseaborn\u002Fplotly. Multi-panel layouts, error bars, significance markers, colorblind-safe, export PDF\u002FEPS\u002FTIFF, for journal-ready scientific plots.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fscientific-visualization\u002FSKILL.md",[614,615,616],{"categoryNo":272,"categoryName":273,"taxonomyType":53,"taxonomyTypeDesc":54},{"categoryNo":245,"categoryName":246,"taxonomyType":53,"taxonomyTypeDesc":54},{"categoryNo":310,"categoryName":311,"taxonomyType":53,"taxonomyTypeDesc":54},[251,618,594,278,619,597,620,621,512,622,623,605,624,625,626,627,231,524,233,525],"scientific visualization","safe","matplotlib","plotly","export","ready","markers","panel","error","multi",152,18]