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Triggers when users request test case generation, QA planning, test scenario creation, or need structured test documentation. Produces detailed test cases covering functional, edge case, error handling, and state transition scenarios.","masx200\u002Fcodebuddy-plugins-official","01M6QTAD604WMDCJNQX9QSJRKY","MIT","https:\u002F\u002Fgithub.com\u002Fmasx200\u002Fcodebuddy-plugins-official\u002Fblob\u002Fmain\u002Fplugins\u002Foh-my-codebuddy\u002Fskills\u002Ftest-cases\u002FSKILL.md",[30],{"categoryNo":31,"categoryName":32,"taxonomyType":33,"taxonomyTypeDesc":34},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",[36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55],"test","test cases","documentation","documents","planning","error handling","state","qa","edge","requirements","generation","creation","triggers","users","case","error","functional","request","transition","when","2026-11-10 10:12:30",{"skillNo":58,"name":59,"nameZh":16,"description":60,"descriptionZh":16,"repositoryFullName":25,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":5,"repositoryNo":26,"starsCount":13,"licenseSpdxId":27,"licenseFileUrl":16,"sourceHtmlUrl":61,"categories":62,"keywords":63,"publishedAt":56},"01M6QX0H3RP29CXHXTTK5JXDPX","zarr-python","Chunked N-D arrays for cloud storage. Compressed arrays, parallel I\u002FO, S3\u002FGCS integration, NumPy\u002FDask\u002FXarray compatible, for large-scale scientific computing pipelines.","https:\u002F\u002Fgithub.com\u002Fmasx200\u002Fcodebuddy-plugins-official\u002Fblob\u002Fmain\u002Fexternal_plugins\u002Fscientific-skills\u002Fzarr-python\u002FSKILL.md",[],[64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81],"python","zarr python","zarr","storage","integration","s3","gcs","cloud","dask","parallel","pipelines","arrays","numpy","scientific computing","scale","scientific","Cloud Storage","for",{"skillNo":83,"name":84,"nameZh":16,"description":85,"descriptionZh":16,"repositoryFullName":25,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":5,"repositoryNo":26,"starsCount":13,"licenseSpdxId":27,"licenseFileUrl":16,"sourceHtmlUrl":86,"categories":87,"keywords":102,"publishedAt":56},"01M6QX08CHYH2SFYMH9VS295QB","venue-templates","Access comprehensive LaTeX templates, formatting requirements, and submission guidelines for major scientific publication venues (Nature, Science, PLOS, IEEE, ACM), academic conferences (NeurIPS, ICML, CVPR, CHI), research posters, and grant proposals (NSF, NIH, DOE, DARPA). This skill should be used when preparing manuscripts for journal submission, conference papers, research posters, or grant proposals and need venue-specific formatting requirements and templates.","https:\u002F\u002Fgithub.com\u002Fmasx200\u002Fcodebuddy-plugins-official\u002Fblob\u002Fmain\u002Fexternal_plugins\u002Fscientific-skills\u002Fvenue-templates\u002FSKILL.md",[88,91,94,97],{"categoryNo":89,"categoryName":90,"taxonomyType":33,"taxonomyTypeDesc":34},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":92,"categoryName":93,"taxonomyType":33,"taxonomyTypeDesc":34},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":95,"categoryName":96,"taxonomyType":33,"taxonomyTypeDesc":34},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":98,"categoryName":99,"taxonomyType":100,"taxonomyTypeDesc":101},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[103,104,105,106,107,108,109,110,111,112,45,113,114,79,115,81,116,117,118,55],"templates","venue templates","guidelines","science","Bash","access","read","write","edit","proposals","formatting","journal","papers","plos","submission","ieee",{"skillNo":120,"name":121,"nameZh":16,"description":122,"descriptionZh":16,"repositoryFullName":25,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":5,"repositoryNo":26,"starsCount":13,"licenseSpdxId":27,"licenseFileUrl":16,"sourceHtmlUrl":123,"categories":124,"keywords":128,"publishedAt":56},"01M6QWZSQ3SX0WZJW5GAJYEKQG","vaex","Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV\u002FHDF5\u002FArrow\u002FParquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.","https:\u002F\u002Fgithub.com\u002Fmasx200\u002Fcodebuddy-plugins-official\u002Fblob\u002Fmain\u002Fexternal_plugins\u002Fscientific-skills\u002Fvaex\u002FSKILL.md",[125],{"categoryNo":126,"categoryName":127,"taxonomyType":33,"taxonomyTypeDesc":34},"01JBSEEDCAT000000000000013","分析与可视化",[121,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,55],"memory","machine learning","create","visualization","dataframe","Evaluation","analyzing","data","in memory","visualizations","work","files","do","statistics","processing","ML Pipelines","at","lazy",{"skillNo":148,"name":149,"nameZh":16,"description":150,"descriptionZh":16,"repositoryFullName":25,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":5,"repositoryNo":26,"starsCount":13,"licenseSpdxId":27,"licenseFileUrl":16,"sourceHtmlUrl":151,"categories":152,"keywords":159,"publishedAt":56},"01M6QWY01HXDWCMMPBZVHH2VP3","treatment-plans","Generate concise (3-4 page), focused medical treatment plans in LaTeX\u002FPDF format for all clinical specialties. Supports general medical treatment, rehabilitation therapy, mental health care, chronic disease management, perioperative care, and pain management. Includes SMART goal frameworks, evidence-based interventions with minimal text citations, regulatory compliance (HIPAA), and professional formatting. Prioritizes brevity and clinical actionability.","https:\u002F\u002Fgithub.com\u002Fmasx200\u002Fcodebuddy-plugins-official\u002Fblob\u002Fmain\u002Fexternal_plugins\u002Fscientific-skills\u002Ftreatment-plans\u002FSKILL.md",[153,156],{"categoryNo":154,"categoryName":155,"taxonomyType":33,"taxonomyTypeDesc":34},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":157,"categoryName":158,"taxonomyType":100,"taxonomyTypeDesc":101},"01JBSEEDCAT000000000000037","律师与法务",[160,161,162,163,164,107,165,109,110,111,166,167,168,113,169,170,171,172,173,174],"treatment plans","plans","PDF","generate","Citations","text","evidence","goal","mental health","medical","clinical","professional","brevity","therapy","smart goal",{"skillNo":176,"name":177,"nameZh":16,"description":178,"descriptionZh":16,"repositoryFullName":25,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":5,"repositoryNo":26,"starsCount":13,"licenseSpdxId":27,"licenseFileUrl":16,"sourceHtmlUrl":179,"categories":180,"keywords":181,"publishedAt":56},"01M6QWWJT18RBNY4A62XZGZKGG","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\u002Fmasx200\u002Fcodebuddy-plugins-official\u002Fblob\u002Fmain\u002Fexternal_plugins\u002Fscientific-skills\u002Ftorch_geometric\u002FSKILL.md",[],[182,183,184,185,186,187,188,189,190,191,192,193,194,195,81,196],"torch geometric","deep learning","neural networks","graph","networks","classification","learning","node","Neural","deep","graphs","molecular","gat","link","graph neural networks",{"skillNo":198,"name":199,"nameZh":16,"description":200,"descriptionZh":16,"repositoryFullName":25,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":5,"repositoryNo":26,"starsCount":13,"licenseSpdxId":27,"licenseFileUrl":16,"sourceHtmlUrl":201,"categories":202,"keywords":204,"publishedAt":56},"01M6QWV65R7PDS6DMPF97KC7C4","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\u002Fmasx200\u002Fcodebuddy-plugins-official\u002Fblob\u002Fmain\u002Fexternal_plugins\u002Fscientific-skills\u002Fstatistical-analysis\u002FSKILL.md",[203],{"categoryNo":31,"categoryName":32,"taxonomyType":33,"taxonomyTypeDesc":34},[205,206,207,36,208,209,136,210,211,212,213,214,215,216,217,218,219,220,81,221],"analysis","statistical analysis","research","help","reporting","statsmodels","results","use","power","models","guidance","selection","tests","with","you","implementing","academic research",{"skillNo":223,"name":224,"nameZh":16,"description":225,"descriptionZh":16,"repositoryFullName":25,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":5,"repositoryNo":26,"starsCount":13,"licenseSpdxId":27,"licenseFileUrl":16,"sourceHtmlUrl":226,"categories":227,"keywords":228,"publishedAt":56},"01M6QWTMR2XS948NV1K31498T8","stable-baselines3","Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib instead.","https:\u002F\u002Fgithub.com\u002Fmasx200\u002Fcodebuddy-plugins-official\u002Fblob\u002Fmain\u002Fexternal_plugins\u002Fscientific-skills\u002Fstable-baselines3\u002FSKILL.md",[],[229,230,231,232,233,234,235,188,236,73,237,238,239,240,241,242,243,244,81,245],"stable baselines3","performance","experiments","training","learn","vectorized","api","multi-agent","ready","algorithms","scikit-learn","high-performance","prototyping","quick","environments","production-ready","reinforcement learning algorithms",{"skillNo":247,"name":248,"nameZh":16,"description":249,"descriptionZh":16,"repositoryFullName":25,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":5,"repositoryNo":26,"starsCount":13,"licenseSpdxId":27,"licenseFileUrl":16,"sourceHtmlUrl":250,"categories":251,"keywords":253,"publishedAt":56},"01M6QWTE3QJSYDTXF0BDH5AK2V","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\u002Fmasx200\u002Fcodebuddy-plugins-official\u002Fblob\u002Fmain\u002Fexternal_plugins\u002Fscientific-skills\u002Fsimpy\u002FSKILL.md",[252],{"categoryNo":95,"categoryName":96,"taxonomyType":33,"taxonomyTypeDesc":34},[248,64,254,255,256,257,258,259,212,260,261,262,263,264,265,266,267,218,268,269],"framework","network","simulation","system","operations","process","time","shared","systems","as","events","resources","manufacturing","event","traffic","queues",{"skillNo":271,"name":272,"nameZh":16,"description":273,"descriptionZh":16,"repositoryFullName":25,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":5,"repositoryNo":26,"starsCount":13,"licenseSpdxId":27,"licenseFileUrl":16,"sourceHtmlUrl":274,"categories":275,"keywords":277,"publishedAt":56},"01M6QWT32DG0W3G4XQ3WCR9SKQ","shap","Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.","https:\u002F\u002Fgithub.com\u002Fmasx200\u002Fcodebuddy-plugins-official\u002Fblob\u002Fmain\u002Fexternal_plugins\u002Fscientific-skills\u002Fshap\u002FSKILL.md",[276],{"categoryNo":95,"categoryName":96,"taxonomyType":33,"taxonomyTypeDesc":34},[272,278,183,279,280,281,135,188,282,212,214,191,283,284,285,286,287,288,289,220],"tensorflow","pytorch","debugging","linear","model","bias","black","random forest","predictions","Interpretability","random","tree",{"skillNo":291,"name":292,"nameZh":16,"description":293,"descriptionZh":16,"repositoryFullName":25,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":5,"repositoryNo":26,"starsCount":13,"licenseSpdxId":27,"licenseFileUrl":16,"sourceHtmlUrl":294,"categories":295,"keywords":299,"publishedAt":56},"01M6QWR6Q8Q6H06P9R26E6G5S2","scientific-writing","Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research-lookup then (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\u002Fmasx200\u002Fcodebuddy-plugins-official\u002Fblob\u002Fmain\u002Fexternal_plugins\u002Fscientific-skills\u002Fscientific-writing\u002FSKILL.md",[296,297,298],{"categoryNo":89,"categoryName":90,"taxonomyType":33,"taxonomyTypeDesc":34},{"categoryNo":95,"categoryName":96,"taxonomyType":33,"taxonomyTypeDesc":34},{"categoryNo":98,"categoryName":99,"taxonomyType":100,"taxonomyTypeDesc":101},[300,301,79,209,302,164,303,107,109,110,111,304,305,191,306,307,114,115,308,309],"writing","scientific writing","deep research","Outlines","never","structure","full","prisma","tables","core skill",{"skillNo":311,"name":312,"nameZh":16,"description":313,"descriptionZh":16,"repositoryFullName":25,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":5,"repositoryNo":26,"starsCount":13,"licenseSpdxId":27,"licenseFileUrl":16,"sourceHtmlUrl":314,"categories":315,"keywords":318,"publishedAt":56},"01M6QWQZDSM0VR2MB6Q31D9VZX","scientific-visualization","Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib\u002Fseaborn\u002Fplotly with publication styles. For quick exploration use seaborn or plotly directly.","https:\u002F\u002Fgithub.com\u002Fmasx200\u002Fcodebuddy-plugins-official\u002Fblob\u002Fmain\u002Fexternal_plugins\u002Fscientific-skills\u002Fscientific-visualization\u002FSKILL.md",[316,317],{"categoryNo":126,"categoryName":127,"taxonomyType":33,"taxonomyTypeDesc":34},{"categoryNo":95,"categoryName":96,"taxonomyType":33,"taxonomyTypeDesc":34},[132,319,79,320,321,322,106,323,212,237,324,325,113,114,326,51,327,242,81,117],"scientific visualization","safe","Meta","plotly","annotations","exploration","meta-skill","panel","figures",{"skillNo":329,"name":330,"nameZh":16,"description":331,"descriptionZh":16,"repositoryFullName":25,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":5,"repositoryNo":26,"starsCount":13,"licenseSpdxId":27,"licenseFileUrl":16,"sourceHtmlUrl":332,"categories":333,"keywords":344,"publishedAt":56},"01M6QWQ6K2MHCV34EDGQGKQPQC","scientific-slides","Build slide decks and presentations for research talks. Use this for making PowerPoint slides, conference presentations, seminar talks, research presentations, thesis defense slides, or any scientific talk. Provides slide structure, design templates, timing guidance, and visual validation. Works with PowerPoint and LaTeX Beamer.","https:\u002F\u002Fgithub.com\u002Fmasx200\u002Fcodebuddy-plugins-official\u002Fblob\u002Fmain\u002Fexternal_plugins\u002Fscientific-skills\u002Fscientific-slides\u002FSKILL.md",[334,337,340,341],{"categoryNo":335,"categoryName":336,"taxonomyType":33,"taxonomyTypeDesc":34},"01JBSEEDCAT000000000000003","演示文稿",{"categoryNo":338,"categoryName":339,"taxonomyType":33,"taxonomyTypeDesc":34},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":95,"categoryName":96,"taxonomyType":33,"taxonomyTypeDesc":34},{"categoryNo":342,"categoryName":343,"taxonomyType":100,"taxonomyTypeDesc":101},"01JBSEEDCAT000000000000032","设计师",[345,346,79,347,348,349,350,107,109,110,111,212,305,351,352,353,354,355,81,356],"scientific slides","slides","design","presentations","powerpoint","defense","visual","thesis","visual validation","slide decks","timing","Beamer",{"skillNo":358,"name":359,"nameZh":16,"description":360,"descriptionZh":16,"repositoryFullName":25,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":5,"repositoryNo":26,"starsCount":13,"licenseSpdxId":27,"licenseFileUrl":16,"sourceHtmlUrl":361,"categories":362,"keywords":364,"publishedAt":56},"01M6QWPJEK9QZWJ09SYSMV9XKA","scientific-schematics","Create publication-quality scientific diagrams using Nano Banana Pro AI with smart iterative refinement. Uses Gemini 3 Pro for quality review. Only regenerates if quality is below threshold for your document type. Specialized in neural network architectures, system diagrams, flowcharts, biological pathways, and complex scientific visualizations.","https:\u002F\u002Fgithub.com\u002Fmasx200\u002Fcodebuddy-plugins-official\u002Fblob\u002Fmain\u002Fexternal_plugins\u002Fscientific-skills\u002Fscientific-schematics\u002FSKILL.md",[363],{"categoryNo":95,"categoryName":96,"taxonomyType":33,"taxonomyTypeDesc":34},[365,79,366,131,367,257,368,369,109,110,111,370,371,372,373,374,375,376,377,81],"scientific schematics","gemini","nano banana pro","quality","specialized","diagrams","document","banana","type","neural network","architectures","pathways","nano",{"skillNo":379,"name":380,"nameZh":16,"description":381,"descriptionZh":16,"repositoryFullName":25,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":5,"repositoryNo":26,"starsCount":13,"licenseSpdxId":27,"licenseFileUrl":16,"sourceHtmlUrl":382,"categories":383,"keywords":386,"publishedAt":56},"01M6QWNYGW9K7GCEBBGS1A2J3T","scientific-critical-thinking","Evaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review writing use peer-review.","https:\u002F\u002Fgithub.com\u002Fmasx200\u002Fcodebuddy-plugins-official\u002Fblob\u002Fmain\u002Fexternal_plugins\u002Fscientific-skills\u002Fscientific-critical-thinking\u002FSKILL.md",[384,385],{"categoryNo":338,"categoryName":339,"taxonomyType":33,"taxonomyTypeDesc":34},{"categoryNo":342,"categoryName":343,"taxonomyType":100,"taxonomyTypeDesc":101},[387,388,389,79,347,300,390,391,205,392,107,109,110,111,166,393,394,395,396,81],"scientific critical thinking","thinking","critical","review","risk","peer review","frameworks","teaching","grading","claims",{"skillNo":398,"name":399,"nameZh":16,"description":400,"descriptionZh":16,"repositoryFullName":25,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":5,"repositoryNo":26,"starsCount":13,"licenseSpdxId":27,"licenseFileUrl":16,"sourceHtmlUrl":401,"categories":402,"keywords":403,"publishedAt":56},"01M6QWNEAWJ7W7ZCMTH1764DVF","scientific-brainstorming","Creative research ideation and exploration. Use for open-ended brainstorming sessions, exploring interdisciplinary connections, challenging assumptions, or identifying research gaps. Best for early-stage research planning when you do not have specific observations yet. For formulating testable hypotheses from data use hypothesis-generation.","https:\u002F\u002Fgithub.com\u002Fmasx200\u002Fcodebuddy-plugins-official\u002Fblob\u002Fmain\u002Fexternal_plugins\u002Fscientific-skills\u002Fscientific-brainstorming\u002FSKILL.md",[],[404,405,79,207,406,40,407,212,408,409,141,324,410,411,412,46,219,413,81,414],"brainstorming","scientific brainstorming","sessions","open","ideation","creative","connections","research ideation","stage","observations","gaps",{"skillNo":416,"name":417,"nameZh":16,"description":418,"descriptionZh":16,"repositoryFullName":25,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":5,"repositoryNo":26,"starsCount":13,"licenseSpdxId":27,"licenseFileUrl":16,"sourceHtmlUrl":419,"categories":420,"keywords":421,"publishedAt":56},"01M6QWMTMD8PQQERW3274DD33H","scholar-evaluation","Systematically evaluate scholarly work using the ScholarEval framework, providing structured assessment across research quality dimensions including problem formulation, methodology, analysis, and writing with quantitative scoring and actionable feedback.","https:\u002F\u002Fgithub.com\u002Fmasx200\u002Fcodebuddy-plugins-official\u002Fblob\u002Fmain\u002Fexternal_plugins\u002Fscientific-skills\u002Fscholar-evaluation\u002FSKILL.md",[],[134,422,423,300,207,424,425,205,426,368,139,427,428,429,430,431,432,433,218,434],"scholar evaluation","scholar","assessment","methodology","feedback","research quality","scoring","dimensions","across","structured","problem","actionable","evaluate",{"skillNo":436,"name":437,"nameZh":16,"description":438,"descriptionZh":16,"repositoryFullName":25,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":5,"repositoryNo":26,"starsCount":13,"licenseSpdxId":27,"licenseFileUrl":16,"sourceHtmlUrl":439,"categories":440,"keywords":442,"publishedAt":56},"01M6QWM3CY6KE88P9BA0DGSKKT","research-lookup","Look up current research information using Perplexity Sonar Pro Search or Sonar Reasoning Pro models through OpenRouter. Automatically selects the best model based on query complexity. Search academic papers, recent studies, technical documentation, and general research information with citations.","https:\u002F\u002Fgithub.com\u002Fmasx200\u002Fcodebuddy-plugins-official\u002Fblob\u002Fmain\u002Fexternal_plugins\u002Fscientific-skills\u002Fresearch-lookup\u002FSKILL.md",[441],{"categoryNo":92,"categoryName":93,"taxonomyType":33,"taxonomyTypeDesc":34},[207,443,444,38,445,164,446,107,282,447,109,110,111,448,449,450,451,115,452,453],"lookup","research lookup","search","perplexity","openrouter","up","complexity","query","information","sonar","current research",18]