[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skills:stats":3,"skills:pro6":8,"skills:sce6":55,"skills:creators9":100,"skills:all:stars:154:":195},{"skillCount":4,"professionCount":5,"scenarioCount":6,"creatorCount":7},196852,11,7,1926,[9,20,27,34,41,48],{"categoryNo":10,"taxonomyType":11,"taxonomyTypeDesc":12,"categoryName":13,"categoryNameEn":14,"description":15,"level":16,"skillCount":17,"parentNo":18,"parentName":18,"children":19},"01JBSEEDCAT000000000000030","PROFESSION","适用职业","开发工程师","Software 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Creator","作者、编辑与自媒体创作者",3940,[],{"categoryNo":42,"taxonomyType":11,"taxonomyTypeDesc":12,"categoryName":43,"categoryNameEn":44,"description":45,"level":16,"skillCount":46,"parentNo":18,"parentName":18,"children":47},"01JBSEEDCAT000000000000035","市场营销","Marketer","市场推广、增长与品牌运营人员",3446,[],{"categoryNo":49,"taxonomyType":11,"taxonomyTypeDesc":12,"categoryName":50,"categoryNameEn":51,"description":52,"level":16,"skillCount":53,"parentNo":18,"parentName":18,"children":54},"01JBSEEDCAT000000000000037","律师与法务","Legal","合同、合规与法律事务人员",3316,[],[56,65,72,79,86,93],{"categoryNo":57,"taxonomyType":58,"taxonomyTypeDesc":59,"categoryName":60,"categoryNameEn":61,"description":62,"level":16,"skillCount":63,"parentNo":18,"parentName":18,"children":64},"01JBSEEDCAT000000000000022","SCENARIO","使用场景","智能体与自动化","Agents & Automation","Skill、MCP、智能体与工作流自动化",39458,[],{"categoryNo":66,"taxonomyType":58,"taxonomyTypeDesc":59,"categoryName":67,"categoryNameEn":68,"description":69,"level":16,"skillCount":70,"parentNo":18,"parentName":18,"children":71},"01JBSEEDCAT000000000000006","软件开发","Software Development","编码、测试、部署等软件研发任务",26531,[],{"categoryNo":73,"taxonomyType":58,"taxonomyTypeDesc":59,"categoryName":74,"categoryNameEn":75,"description":76,"level":16,"skillCount":77,"parentNo":18,"parentName":18,"children":78},"01JBSEEDCAT000000000000014","设计创意","Design & Creative","视觉设计、品牌与多媒体创作",13569,[],{"categoryNo":80,"taxonomyType":58,"taxonomyTypeDesc":59,"categoryName":81,"categoryNameEn":82,"description":83,"level":16,"skillCount":84,"parentNo":18,"parentName":18,"children":85},"01JBSEEDCAT000000000000001","办公效率","Office Productivity","日常办公文档、表格、演示与沟通类任务",6289,[],{"categoryNo":87,"taxonomyType":58,"taxonomyTypeDesc":59,"categoryName":88,"categoryNameEn":89,"description":90,"level":16,"skillCount":91,"parentNo":18,"parentName":18,"children":92},"01JBSEEDCAT000000000000017","内容创作","Content Creation","写作、文案与营销内容生产",6029,[],{"categoryNo":94,"taxonomyType":58,"taxonomyTypeDesc":59,"categoryName":95,"categoryNameEn":96,"description":97,"level":16,"skillCount":98,"parentNo":18,"parentName":18,"children":99},"01JBSEEDCAT000000000000020","研究与学习","Research & Learning","资料检索、知识整理与学习辅导",5531,[],{"records":101,"total":7,"current":16,"size":194},[102,114,123,133,143,153,163,172,182],{"accountNo":103,"login":104,"displayName":105,"avatarUrl":106,"accountType":107,"accountTypeDesc":108,"profileDescription":109,"skillCount":110,"repoCount":111,"professionCount":5,"scenarioCount":6,"totalStars":112,"updatedAt":113},"01M698VYDXZ6JW5WH3CRV650R8","microsoft","Microsoft","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6154722?v=4","ORGANIZATION","组织","Open source projects and samples from Microsoft",4160,229,828772,"2026-11-13 04:10:52",{"accountNo":115,"login":116,"displayName":116,"avatarUrl":117,"accountType":107,"accountTypeDesc":108,"profileDescription":118,"skillCount":119,"repoCount":120,"professionCount":6,"scenarioCount":6,"totalStars":121,"updatedAt":122},"01M698P3FSAZ6B4R928ZF19KPT","openclaw","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F252820863?v=4","Your personal, open source AI assistant.",362,32,437711,"2026-11-13 03:02:05",{"accountNo":124,"login":125,"displayName":126,"avatarUrl":127,"accountType":107,"accountTypeDesc":108,"profileDescription":18,"skillCount":128,"repoCount":129,"professionCount":6,"scenarioCount":130,"totalStars":131,"updatedAt":132},"01M6AC29YGCR4S2NTJFV0G8B81","openai","OpenAI","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F14957082?v=4",171,23,6,342695,"2026-11-13 04:03:41",{"accountNo":134,"login":135,"displayName":136,"avatarUrl":137,"accountType":107,"accountTypeDesc":108,"profileDescription":18,"skillCount":138,"repoCount":139,"professionCount":140,"scenarioCount":6,"totalStars":141,"updatedAt":142},"01M6AC83E6K4FDKK7HZ8WNMQKE","apache","The Apache Software Foundation","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F47359?v=4",420,75,8,298176,"2026-11-13 03:14:28",{"accountNo":144,"login":145,"displayName":146,"avatarUrl":147,"accountType":107,"accountTypeDesc":108,"profileDescription":148,"skillCount":149,"repoCount":150,"professionCount":5,"scenarioCount":6,"totalStars":151,"updatedAt":152},"01M698VYEFDM0VJBZDYYJ2T83S","google","Google","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F1342004?v=4","Google ❤️ Open Source",535,48,242366,"2026-11-13 04:17:09",{"accountNo":154,"login":155,"displayName":156,"avatarUrl":157,"accountType":107,"accountTypeDesc":108,"profileDescription":158,"skillCount":159,"repoCount":160,"professionCount":5,"scenarioCount":6,"totalStars":161,"updatedAt":162},"01M6AC83DS133TPXFSP1DHNCC6","github","GitHub","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F9919?v=4","How people build software.",940,28,238441,"2026-11-13 03:57:35",{"accountNo":164,"login":165,"displayName":166,"avatarUrl":167,"accountType":107,"accountTypeDesc":108,"profileDescription":18,"skillCount":168,"repoCount":169,"professionCount":5,"scenarioCount":6,"totalStars":170,"updatedAt":171},"01M697XGDPXJAVQ8391V2BCYTE","anthropics","Anthropic","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F76263028?v=4",674,17,231826,"2026-11-13 04:18:26",{"accountNo":173,"login":174,"displayName":175,"avatarUrl":176,"accountType":107,"accountTypeDesc":108,"profileDescription":18,"skillCount":177,"repoCount":178,"professionCount":179,"scenarioCount":6,"totalStars":180,"updatedAt":181},"01M6AC83EPDGBFN19MWYRREJD0","Significant-Gravitas","AutoGPT","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F130738209?v=4",86,2,10,188014,"2026-11-13 02:38:59",{"accountNo":183,"login":184,"displayName":185,"avatarUrl":186,"accountType":187,"accountTypeDesc":188,"profileDescription":189,"skillCount":190,"repoCount":191,"professionCount":130,"scenarioCount":6,"totalStars":192,"updatedAt":193},"01M6DJCCDDG53MTYYRTXAXFG17","garrytan","Garry Tan","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F19957?v=4","USER","个人作者","Writes software, dreams. ",324,3,174614,"2026-11-13 04:05:07",9,{"records":196,"total":4,"current":642,"size":643},[197,237,269,289,314,343,364,386,425,447,474,499,525,546,568,585,608,624],{"skillNo":198,"name":199,"nameZh":18,"description":200,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":209,"categories":210,"keywords":214,"publishedAt":236},"01M6AFK68HPY4A3KKPZ12ZBNBS","pyhealth","Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III\u002FIV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).","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\u002Fpyhealth\u002FSKILL.md",[211],{"categoryNo":212,"categoryName":213,"taxonomyType":58,"taxonomyTypeDesc":59},"01JBSEEDCAT000000000000023","Skill与智能体开发",[199,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235],"testing","machine learning","deep learning","tasks","toolkit","gnn","data","applications","healthcare","coding","signals","deep","medical","clinical","medical coding","implementing","for","retain","when","prediction","NDC","2026-11-10 10:12:30",{"skillNo":238,"name":239,"nameZh":18,"description":240,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":241,"categories":242,"keywords":251,"publishedAt":236},"01M6AFK2YXRAVCSGSQVKC7384R","pydicom","Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpydicom\u002FSKILL.md",[243,246,249,250],{"categoryNo":244,"categoryName":245,"taxonomyType":58,"taxonomyTypeDesc":59},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":247,"categoryName":248,"taxonomyType":58,"taxonomyTypeDesc":59},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":212,"categoryName":213,"taxonomyType":58,"taxonomyTypeDesc":59},{"categoryNo":35,"categoryName":36,"taxonomyType":11,"taxonomyTypeDesc":12},[239,252,253,254,255,218,256,257,221,258,222,259,223,260,261,262,227,263,264,265,266,267,268,233],"writing","image","python","workflows","metadata","library","Ray","analysis","images","files","pixel","processing","communications","imaging","extracting","reading","formats",{"skillNo":270,"name":271,"nameZh":18,"description":272,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":273,"categories":274,"keywords":276,"publishedAt":236},"01M6AFJZ340ZXP1AADVXFE3YXD","pydeseq2","Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano\u002FMA plots, for RNA-seq analysis.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpydeseq2\u002FSKILL.md",[275],{"categoryNo":212,"categoryName":213,"taxonomyType":58,"taxonomyTypeDesc":59},[271,254,259,277,278,279,280,281,282,283,284,285,286,231,287,288],"RNA","gene expression","tests","expression","DESeq2","volcano","de","rna-seq","bulk","seq","gene","fdr",{"skillNo":290,"name":291,"nameZh":18,"description":292,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":293,"categories":294,"keywords":296,"publishedAt":236},"01M6AFJVBH9WEGCNM0XXVPQX5S","pufferlib","This skill should be used when working with reinforcement learning tasks including high-performance RL training, custom environment development, vectorized parallel simulation, multi-agent systems, or integration with existing RL environments (Gymnasium, PettingZoo, Atari, Procgen, etc.). Use this skill for implementing PPO training, creating PufferEnv environments, optimizing RL performance, or developing policies with CNNs\u002FLSTMs.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpufferlib\u002FSKILL.md",[295],{"categoryNo":212,"categoryName":213,"taxonomyType":58,"taxonomyTypeDesc":59},[291,297,298,218,299,300,301,302,303,304,305,306,307,308,309,310,311,230,231,312,313,233],"performance","training","development","reinforcement learning","vectorized","integration","PPO","simulation","learning","multi-agent","parallel","environment","use","high-performance","environments","Existing","custom",{"skillNo":315,"name":316,"nameZh":18,"description":317,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":318,"categories":319,"keywords":325,"publishedAt":236},"01M6AFJQZBHZGQCG0Y36QE8PE5","pubmed-database","Direct REST API access to PubMed. Advanced Boolean\u002FMeSH queries, E-utilities API, batch processing, citation management. For Python workflows, prefer biopython (Bio.Entrez). Use this for direct HTTP\u002FREST work or custom API implementations.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpubmed-database\u002FSKILL.md",[320,323,324],{"categoryNo":321,"categoryName":322,"taxonomyType":58,"taxonomyTypeDesc":59},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":212,"categoryName":213,"taxonomyType":58,"taxonomyTypeDesc":59},{"categoryNo":10,"categoryName":13,"taxonomyType":11,"taxonomyTypeDesc":12},[326,327,328,254,329,255,330,331,332,333,334,335,336,337,338,339,263,340,341,231,313,342],"database","pubmed database","pubmed","mesh","http","batch","REST","biopython","citation management","api","access","direct","work","Entrez","Utilities","advanced","boolean",{"skillNo":344,"name":345,"nameZh":18,"description":346,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":347,"categories":348,"keywords":352,"publishedAt":236},"01M6AFJMWHD86KMHBM1GGQTT2R","pubchem-database","Query PubChem via PUG-REST API\u002FPubChemPy (110M+ compounds). Search by name\u002FCID\u002FSMILES, retrieve properties, similarity\u002Fsubstructure searches, bioactivity, for cheminformatics.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpubchem-database\u002FSKILL.md",[349,350,351],{"categoryNo":321,"categoryName":322,"taxonomyType":58,"taxonomyTypeDesc":59},{"categoryNo":212,"categoryName":213,"taxonomyType":58,"taxonomyTypeDesc":59},{"categoryNo":10,"categoryName":13,"taxonomyType":11,"taxonomyTypeDesc":12},[326,353,354,332,335,355,356,357,358,359,360,361,231,362,363],"pubchem database","search","properties","query","cheminformatics","rest api","name","similarity","via","by name","SMILES",{"skillNo":365,"name":366,"nameZh":18,"description":367,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":368,"categories":369,"keywords":371,"publishedAt":236},"01M6AFJH4560DBPWQS02C39FSZ","protocolsio-integration","Integration with protocols.io API for managing scientific protocols. This skill should be used when working with protocols.io to search, create, update, or publish protocols; manage protocol steps and materials; handle discussions and comments; organize workspaces; upload and manage files; or integrate protocols.io functionality into workflows. Applicable for protocol discovery, collaborative protocol development, experiment tracking, lab protocol management, and scientific documentation.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fprotocolsio-integration\u002FSKILL.md",[370],{"categoryNo":212,"categoryName":213,"taxonomyType":58,"taxonomyTypeDesc":59},[302,372,255,373,299,374,375,376,354,377,378,335,379,380,381,261,382,383,384,385,233],"protocolsio integration","documentation","create","update","protocol","protocols","publish","experiment","integrate","workspaces","organize","io","scientific","Experiment Tracking",{"skillNo":387,"name":388,"nameZh":18,"description":389,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":390,"categories":391,"keywords":406,"publishedAt":236},"01M6AFJDGPKA15MEN1EDJY2510","pptx-posters","Create professional research posters in LaTeX using beamerposter, tikzposter, or baposter. Support for conference presentations, academic posters, and scientific communication. Includes layout design, color schemes, multi-column formats, figure integration, and poster-specific best practices for visual communication.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpptx-posters\u002FSKILL.md",[392,395,398,401,402,403],{"categoryNo":393,"categoryName":394,"taxonomyType":58,"taxonomyTypeDesc":59},"01JBSEEDCAT000000000000003","演示文稿",{"categoryNo":396,"categoryName":397,"taxonomyType":58,"taxonomyTypeDesc":59},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":399,"categoryName":400,"taxonomyType":58,"taxonomyTypeDesc":59},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":212,"categoryName":213,"taxonomyType":58,"taxonomyTypeDesc":59},{"categoryNo":21,"categoryName":22,"taxonomyType":11,"taxonomyTypeDesc":12},{"categoryNo":404,"categoryName":405,"taxonomyType":11,"taxonomyTypeDesc":12},"01JBSEEDCAT000000000000034","行政与办公人员",[407,408,409,410,411,412,374,413,414,302,415,416,417,418,419,420,421,422,423,424,384,268,231],"pptx","pptx posters","design","research","support","presentations","best practices","communication","LaTeX","Bash","read","write","edit","visual","poster","layout","professional","color",{"skillNo":426,"name":427,"nameZh":18,"description":428,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":429,"categories":430,"keywords":434,"publishedAt":236},"01M6AFJA1N2PCYFCRRDCG0TPEM","polars","Fast DataFrame library (Apache Arrow). Select, filter, group_by, joins, lazy evaluation, CSV\u002FParquet I\u002FO, expression API, for high-performance data analysis workflows.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpolars\u002FSKILL.md",[431],{"categoryNo":432,"categoryName":433,"taxonomyType":58,"taxonomyTypeDesc":59},"01JBSEEDCAT000000000000013","分析与可视化",[427,435,297,255,135,436,437,438,257,439,440,441,221,259,335,442,280,443,310,444,445,231,446],"data analysis","arrow","parquet","dataframe","fast","csv","Evaluation","select","filter","apache arrow","high","lazy",{"skillNo":448,"name":449,"nameZh":18,"description":450,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":451,"categories":452,"keywords":458,"publishedAt":236},"01M6AFJ6PM5D0AVX46WEF1SYF3","plotly","Interactive scientific and statistical data visualization library for Python. Use when creating charts, plots, or visualizations including scatter plots, line charts, bar charts, heatmaps, 3D plots, geographic maps, statistical distributions, financial charts, and dashboards. Supports both quick visualizations (Plotly Express) and fine-grained customization (graph objects). Outputs interactive HTML or static images (PNG, PDF, SVG).","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fplotly\u002FSKILL.md",[453,456,457],{"categoryNo":454,"categoryName":455,"taxonomyType":58,"taxonomyTypeDesc":59},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":432,"categoryName":433,"taxonomyType":58,"taxonomyTypeDesc":59},{"categoryNo":28,"categoryName":29,"taxonomyType":11,"taxonomyTypeDesc":12},[449,459,254,460,461,462,257,221,463,464,465,466,260,467,468,469,470,471,384,472,473,233],"PDF","data visualization","visualization","graph","3D","Charts","png","html","maps","objects","express","distributions","line","quick","static",{"skillNo":475,"name":476,"nameZh":18,"description":477,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":478,"categories":479,"keywords":482,"publishedAt":236},"01M6AFJ2PXVHW4JFZ4AG2JT2JX","perplexity-search","Perform AI-powered web searches with real-time information using Perplexity models via LiteLLM and OpenRouter. This skill should be used when conducting web searches for current information, finding recent scientific literature, getting grounded answers with source citations, or accessing information beyond the model's knowledge cutoff. Provides access to multiple Perplexity models including Sonar Pro, Sonar Pro Search (advanced agentic search), and Sonar Reasoning Pro through a single OpenRouter API key.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fperplexity-search\u002FSKILL.md",[480,481],{"categoryNo":399,"categoryName":400,"taxonomyType":58,"taxonomyTypeDesc":59},{"categoryNo":212,"categoryName":213,"taxonomyType":58,"taxonomyTypeDesc":59},[354,483,484,485,486,487,488,489,335,490,336,491,492,493,494,495,384,496,341,497,231,498,233],"perplexity","perplexity search","agentic","web","reasoning","Citations","real-time","knowledge","model","openrouter","models","time","literature","information","sonar","source",{"skillNo":500,"name":501,"nameZh":18,"description":502,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":503,"categories":504,"keywords":505,"publishedAt":236},"01M6AFHZ628J6HT0WDJW7TMPQ8","pennylane","Cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Enables building and training quantum circuits with automatic differentiation, seamless integration with PyTorch\u002FJAX\u002FTensorFlow, and device-independent execution across simulators and quantum hardware (IBM, Amazon Braket, Google, Rigetti, IonQ, etc.). Use when working with quantum circuits, variational quantum algorithms (VQE, QAOA), quantum neural networks, hybrid quantum-classical models, molecular simulations, quantum chemistry calculations, or any quantum computing tasks requiring gradient-based optimization, hardware-agnostic programming, or quantum machine learning workflows.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpennylane\u002FSKILL.md",[],[501,145,254,506,216,255,507,508,298,218,509,510,257,302,511,512,513,514,515,516,517,518,519,520,521,522,523,233,524],"tensorflow","neural networks","pytorch","quantum chemistry","quantum computing","Optimization","ibm","execution","networks","algorithms","across","molecular","amazon braket","differentiation","quantum algorithms","gradient","automatic","hybrid","Simulations",{"skillNo":526,"name":527,"nameZh":18,"description":528,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":529,"categories":530,"keywords":532,"publishedAt":236},"01M6AFHVP7KY2FXZ6CNVEMB98J","peer-review","Systematic peer review toolkit. Evaluate methodology, statistics, design, reproducibility, ethics, figure integrity, reporting standards, for manuscript and grant review across disciplines.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpeer-review\u002FSKILL.md",[531],{"categoryNo":212,"categoryName":213,"taxonomyType":58,"taxonomyTypeDesc":59},[533,534,535,409,219,536,537,538,416,417,418,419,539,540,541,516,542,543,544,231,545],"review","peer review","peer","reporting","methodology","integrity","ethics","standards","reproducibility","statistics","systematic","manuscript","evaluate",{"skillNo":547,"name":548,"nameZh":18,"description":549,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":550,"categories":551,"keywords":553,"publishedAt":236},"01M6AFHRZQVKVVVM82Q5FVJ1AT","pdb-database","Access RCSB PDB for 3D protein\u002Fnucleic acid structures. Search by text\u002Fsequence\u002Fstructure, download coordinates (PDB\u002FmmCIF), retrieve metadata, for structural biology and drug discovery.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpdb-database\u002FSKILL.md",[552],{"categoryNo":321,"categoryName":322,"taxonomyType":58,"taxonomyTypeDesc":59},[326,554,555,354,256,463,556,336,557,558,559,560,561,562,563,564,565,231,566,567],"pdb database","pdb","discovery","download","text","coordinates","structure","acid","structural biology","structural","drug discovery","biology","sequence","Protein",{"skillNo":569,"name":570,"nameZh":18,"description":571,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":572,"categories":573,"keywords":575,"publishedAt":236},"01M6AFHNDB3HZ04QN53JR1SFVG","pathml","Computational pathology toolkit for analyzing whole-slide images (WSI) and multiparametric imaging data. Use this skill when working with histopathology slides, H&E stained images, multiplex immunofluorescence (CODEX, Vectra), spatial proteomics, nucleus detection\u002Fsegmentation, tissue graph construction, or training ML models on pathology data. Supports 160+ slide formats including Aperio SVS, NDPI, DICOM, OME-TIFF for digital pathology workflows.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpathml\u002FSKILL.md",[574],{"categoryNo":212,"categoryName":213,"taxonomyType":58,"taxonomyTypeDesc":59},[570,576,255,577,298,219,462,578,221,579,580,581,309,260,582,265,583,268,231,584,233],"codex","ml","analyzing","detection","segmentation","slides","computational","with","pathology",{"skillNo":586,"name":587,"nameZh":18,"description":588,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":589,"categories":590,"keywords":595,"publishedAt":236},"01M6AFHHJAEXRNABWHMCRAWBE3","paper-2-web","This skill should be used when converting academic papers into promotional and presentation formats including interactive websites (Paper2Web), presentation videos (Paper2Video), and conference posters (Paper2Poster). Use this skill for tasks involving paper dissemination, conference preparation, creating explorable academic homepages, generating video abstracts, or producing print-ready posters from LaTeX or PDF sources.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpaper-2-web\u002FSKILL.md",[591,592,593,594],{"categoryNo":396,"categoryName":397,"taxonomyType":58,"taxonomyTypeDesc":59},{"categoryNo":244,"categoryName":245,"taxonomyType":58,"taxonomyTypeDesc":59},{"categoryNo":399,"categoryName":400,"taxonomyType":58,"taxonomyTypeDesc":59},{"categoryNo":212,"categoryName":213,"taxonomyType":58,"taxonomyTypeDesc":59},[486,596,597,459,218,415,598,599,417,418,419,600,309,601,602,603,604,605,606,268,231,607],"paper 2 web","paper","dissemination","Video","interactive","ready","sources","presentation","videos","print","papers","preparation",{"skillNo":609,"name":610,"nameZh":18,"description":611,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":612,"categories":613,"keywords":615,"publishedAt":236},"01M6AFHEVW3EKVSEGT8D92PGXX","opentrons-integration","Lab automation platform for Flex\u002FOT-2 robots. Write Protocol API v2 protocols, liquid handling, hardware modules (heater-shaker, thermocycler), labware management, for automated pipetting workflows.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fopentrons-integration\u002FSKILL.md",[614],{"categoryNo":212,"categoryName":213,"taxonomyType":58,"taxonomyTypeDesc":59},[302,616,617,255,376,377,335,418,618,619,620,621,622,231,623],"opentrons integration","automation","management","modules","hardware","platform","lab","liquid",{"skillNo":625,"name":626,"nameZh":18,"description":627,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":628,"categories":629,"keywords":632,"publishedAt":236},"01M6AFHBEDV4CHZ78K55Z6QD6X","opentargets-database","Query Open Targets Platform for target-disease associations, drug target discovery, tractability\u002Fsafety data, genetics\u002Fomics evidence, known drugs, for therapeutic target identification.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fopentargets-database\u002FSKILL.md",[630,631],{"categoryNo":321,"categoryName":322,"taxonomyType":58,"taxonomyTypeDesc":59},{"categoryNo":212,"categoryName":213,"taxonomyType":58,"taxonomyTypeDesc":59},[326,633,221,634,556,635,636,356,637,621,638,639,640,641,231],"opentargets database","Safety","open","evidence","genetics","drugs","identification","omics","targets",154,18]