[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M72ABP43XQ7VB146KD8M9BRS":3,"skill-same:01M72ABP43XQ7VB146KD8M9BRS":47},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":24,"commitSha":25,"skillDirectory":26,"forksCount":27,"repositoryHtmlUrl":28,"repositoryHomepageUrl":6,"ownerHtmlUrl":29,"ownerBlogUrl":30,"ownerDescription":31,"updatedAt":32,"install":33,"resources":46},"01M72ABP43XQ7VB146KD8M9BRS","product-analytics",null,"Use product analytics to understand user behavior, measure feature adoption, and inform product decisions with data. Use when defining events, analyzing funnels and retention, diagnosing drops, or evaluating the impact of changes.","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\u002Fproduct-management\u002Fproduct-analytics\u002FSKILL.md",[],[],"2026-11-10 16:52:40",false,"---\nname: product-analytics\ndescription: >\n  Use product analytics to understand user behavior, measure feature adoption, and inform product decisions with data.\n  Use when defining events, analyzing funnels and retention, diagnosing drops, or evaluating the impact of changes.\n---\n\n# Product Analytics\n\n## Overview\n\nProduct analytics turns usage into insight. Done well, it answers what users do, where they struggle, and whether changes improved outcomes.\n\n## When to Use\n\n- Defining instrumentation for new features\n- Analyzing activation, retention, and engagement\n- Measuring impact after launches\n- Exploring user behavior to generate hypotheses\n\n## Core Practices\n\n- Define a clear analytics tracking plan (events, properties, ownership)\n- Focus on a small set of meaningful funnels and retention views\n- Segment by cohort, persona proxy, or acquisition source when relevant\n- Combine quantitative patterns with qualitative research\n- Establish baselines before judging experiments or launches\n\n## Principles\n\n- Instrument for decisions, not for collecting everything possible\n- Protect data quality — bad events create false confidence\n- Look at trends and cohorts, not only snapshots\n- Privacy and compliance constraints are part of the design\n\n## Verification\n\n- [ ] Key product questions can be answered with available data\n- [ ] Event definitions are documented and trusted\n- [ ] Insights regularly appear in product decisions\n","# Product Analytics\n\n## Overview\n\nProduct analytics turns usage into insight. Done well, it answers what users do, where they struggle, and whether changes improved outcomes.\n\n## When to Use\n\n- Defining instrumentation for new features\n- Analyzing activation, retention, and engagement\n- Measuring impact after launches\n- Exploring user behavior to generate hypotheses\n\n## Core Practices\n\n- Define a clear analytics tracking plan (events, properties, ownership)\n- Focus on a small set of meaningful funnels and retention views\n- Segment by cohort, persona proxy, or acquisition source when relevant\n- Combine quantitative patterns with qualitative research\n- Establish baselines before judging experiments or launches\n\n## Principles\n\n- Instrument for decisions, not for collecting everything possible\n- Protect data quality — bad events create false confidence\n- Look at trends and cohorts, not only snapshots\n- Privacy and compliance constraints are part of the design\n\n## Verification\n\n- [ ] Key product questions can be answered with available data\n- [ ] Event definitions are documented and trusted\n- [ ] Insights regularly appear in product decisions\n",{"name":5,"description":7},"main","8d2fd11b59496bfb3792e747e5519817e554e6cf","skills\u002Fproduct-management\u002Fproduct-analytics",1,"https:\u002F\u002Fgithub.com\u002Fitsual\u002Fagent-skills-collection","https:\u002F\u002Fgithub.com\u002Fitsual","https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Farockialiborious\u002F","Data Science & Analytics Leader | Helping organizations rethink their data-driven decision making process | AI Storyteller","2026-07-22 23:04:48",{"source":8,"skillName":5,"commands":34,"prompt":44,"zipAvailable":45},[35,38,41],{"tool":36,"command":37},"npx","npx skills add itsual\u002Fagent-skills-collection --skill product-analytics",{"tool":39,"command":40},"bunx","bunx skills add itsual\u002Fagent-skills-collection --skill product-analytics",{"tool":42,"command":43},"pnpm","pnpm dlx skills add itsual\u002Fagent-skills-collection --skill product-analytics","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fitsual\u002Fagent-skills-collection\u002Fblob\u002Fmain\u002Fskills\u002Fproduct-management\u002Fproduct-analytics\u002FSKILL.md",true,[],[48,56,63,70,77,85],{"skillNo":49,"name":50,"nameZh":6,"description":51,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":52,"categories":53,"keywords":54,"publishedAt":55},"01M7297PK9X4VZRN6HAD9A58M9","compliance-monitoring","Design compliance monitoring and testing programs that verify controls work in practice and surface issues before regulators do. Use when building monitoring plans, compliance testing, or continuous control monitoring.","https:\u002F\u002Fgithub.com\u002Fitsual\u002Fagent-skills-collection\u002Fblob\u002Fmain\u002Fskills\u002Fcompliance\u002Fcompliance-monitoring\u002FSKILL.md",[],[],"2026-11-10 16:53:54",{"skillNo":57,"name":58,"nameZh":6,"description":59,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":60,"categories":61,"keywords":62,"publishedAt":55},"01M7297KXVGGV1CH7CEHA62FAT","complaints-handling","Operate complaints-handling processes that meet regulatory standards — logging, timely resolution, root-cause analysis, and fair outcomes. Use when designing or improving regulated complaints frameworks or analyzing complaint themes for conduct risk.","https:\u002F\u002Fgithub.com\u002Fitsual\u002Fagent-skills-collection\u002Fblob\u002Fmain\u002Fskills\u002Fcompliance\u002Fcomplaints-handling\u002FSKILL.md",[],[],{"skillNo":64,"name":65,"nameZh":6,"description":66,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":67,"categories":68,"keywords":69,"publishedAt":55},"01M7297HXEX2PSHBEDVWRJC924","aml-kyc-basics","Apply AML\u002FKYC fundamentals — customer due diligence, monitoring, suspicious activity escalation, and recordkeeping — for financial crime compliance. Use when designing onboarding controls, transaction monitoring concepts, or AML program components in financial services.","https:\u002F\u002Fgithub.com\u002Fitsual\u002Fagent-skills-collection\u002Fblob\u002Fmain\u002Fskills\u002Fcompliance\u002Faml-kyc-basics\u002FSKILL.md",[],[],{"skillNo":71,"name":72,"nameZh":6,"description":73,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":74,"categories":75,"keywords":76,"publishedAt":55},"01M7297G229RT4FXA2KW7EG2K6","ai-model-compliance","Apply compliance controls to AI and automated decision systems — transparency, fairness, human oversight, documentation, and regulatory alignment. Use when deploying AI in regulated processes (credit, fraud, customer decisions) or responding to AI governance expectations.","https:\u002F\u002Fgithub.com\u002Fitsual\u002Fagent-skills-collection\u002Fblob\u002Fmain\u002Fskills\u002Fcompliance\u002Fai-model-compliance\u002FSKILL.md",[],[],{"skillNo":78,"name":79,"nameZh":6,"description":80,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":81,"categories":82,"keywords":83,"publishedAt":84},"01M729KEEE7VTT48V1J7YMS8GA","data-quality-management","Manage data quality end-to-end — dimensions, rules, remediation, and prevention — beyond one-off cleaning scripts. Use when building sustained data quality programs or fixing systemic accuracy, completeness, or consistency problems.","https:\u002F\u002Fgithub.com\u002Fitsual\u002Fagent-skills-collection\u002Fblob\u002Fmain\u002Fskills\u002Fdata-analytics\u002Fdata-quality-management\u002FSKILL.md",[],[],"2026-11-10 16:53:43",{"skillNo":86,"name":87,"nameZh":6,"description":88,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":89,"categories":90,"keywords":91,"publishedAt":84},"01M729K3VDSZC6TF67TYQ4Z8W9","data-lineage","Capture and use data lineage — where data comes from, how it transforms, and what depends on it — for impact analysis, debugging, and trust. Use when tracing metric origins, assessing change impact, or diagnosing broken pipelines.","https:\u002F\u002Fgithub.com\u002Fitsual\u002Fagent-skills-collection\u002Fblob\u002Fmain\u002Fskills\u002Fdata-analytics\u002Fdata-lineage\u002FSKILL.md",[],[]]