[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"package:01M747BKXB3YEH3GBGMMQKG8QX":3},{"packageNo":4,"name":5,"subtitle":6,"coverUrl":7,"price":8,"originalPrice":9,"skillCount":10,"salesCount":11,"coreAbilities":12,"publishedAt":13,"targetAudience":14,"seoTitle":15,"seoDescription":15,"updatedAt":16,"previewItems":17,"effects":56},"01M747BKXB3YEH3GBGMMQKG8QX","数据分析师技能包","从数据清洗、SQL查询到统计分析、可视化和商业洞察。","https:\u002F\u002F3dingyun.oss-cn-shenzhen.aliyuncs.com\u002Fnethave\u002Fadmin\u002Fimage\u002F202611\u002Fmwbi8j5masubd47i.png",39.9,60,10,0,[],"2026-11-11 10:35:31",[],null,"2026-11-11 10:35:38",[18,36,46],{"itemNo":19,"skill":20,"purpose":15},"01M747BKY214DED7NG2B98H1S1",{"skillNo":21,"name":22,"nameZh":15,"description":23,"descriptionZh":15,"repositoryFullName":24,"ownerLogin":25,"ownerAvatarUrl":26,"ownerAccountNo":27,"ownerDisplayName":28,"repositoryNo":29,"starsCount":30,"licenseSpdxId":31,"licenseFileUrl":15,"sourceHtmlUrl":32,"categories":33,"keywords":34,"publishedAt":35},"01M7291QJF3V3EPF64T9Z8W3WJ","analytics-problem-framing","Frame analytics and data science problems so questions, decisions, success metrics, and constraints are clear before analysis begins. Use when scoping data projects, avoiding analysis theater, or aligning stakeholders on what “done” means.","itsual\u002Fagent-skills-collection","itsual","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F47685900?v=4","01M7291J4K9RE0EWMQB0JM3WFN","Arockia Liborious","01M7291J58T0363Q45MQP9KESY",7,"MIT","https:\u002F\u002Fgithub.com\u002Fitsual\u002Fagent-skills-collection\u002Fblob\u002Fmain\u002Fskills\u002Fdata-analytics\u002Fanalytics-problem-framing\u002FSKILL.md",[],[],"2026-11-10 16:29:02",{"itemNo":37,"skill":38,"purpose":15},"01M747BKYCYPAVN743S06KKDYX",{"skillNo":39,"name":40,"nameZh":15,"description":41,"descriptionZh":15,"repositoryFullName":24,"ownerLogin":25,"ownerAvatarUrl":26,"ownerAccountNo":27,"ownerDisplayName":28,"repositoryNo":29,"starsCount":30,"licenseSpdxId":31,"licenseFileUrl":15,"sourceHtmlUrl":42,"categories":43,"keywords":44,"publishedAt":45},"01M729KS2NZQ1Z241QC2B15J55","exploratory-data-analysis","Perform exploratory data analysis (EDA) to understand distributions, relationships, data quality issues, and promising signals before formal modeling. Use when first encountering a dataset or validating assumptions prior to deeper analysis.","https:\u002F\u002Fgithub.com\u002Fitsual\u002Fagent-skills-collection\u002Fblob\u002Fmain\u002Fskills\u002Fdata-analytics\u002Fexploratory-data-analysis\u002FSKILL.md",[],[],"2026-11-10 16:53:31",{"itemNo":47,"skill":48,"purpose":15},"01M747BKYPAAJ4WYFX0E706WYJ",{"skillNo":49,"name":50,"nameZh":15,"description":51,"descriptionZh":15,"repositoryFullName":24,"ownerLogin":25,"ownerAvatarUrl":26,"ownerAccountNo":27,"ownerDisplayName":28,"repositoryNo":29,"starsCount":30,"licenseSpdxId":31,"licenseFileUrl":15,"sourceHtmlUrl":52,"categories":53,"keywords":54,"publishedAt":55},"01M729HQJCBG39G7HTWPW71XSX","data-cleaning","Clean and prepare datasets — missing values, types, duplicates, outliers, and consistency — with documented, reversible transformations. Use when preparing data for analysis, modeling, or reporting pipelines.","https:\u002F\u002Fgithub.com\u002Fitsual\u002Fagent-skills-collection\u002Fblob\u002Fmain\u002Fskills\u002Fdata-analytics\u002Fdata-cleaning\u002FSKILL.md",[],[],"2026-11-10 16:53:43",[]]