[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ACX233KE9DASJ2JS3BJBD9":3,"skill-same:01M6ACX233KE9DASJ2JS3BJBD9":93},{"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":16,"sourceHtmlUrl":17,"categories":18,"keywords":38,"publishedAt":49,"linkOnly":50,"markdownContent":51,"markdownBody":52,"frontMatter":53,"refName":61,"commitSha":62,"skillDirectory":63,"forksCount":64,"repositoryHtmlUrl":65,"repositoryHomepageUrl":6,"ownerHtmlUrl":66,"ownerBlogUrl":67,"ownerDescription":6,"updatedAt":68,"install":69,"resources":82},"01M6ACX233KE9DASJ2JS3BJBD9","product-metrics-dashboard-design",null,"Define and design a product metrics dashboard with key metrics, data sources, visualization types, and alert thresholds. Use when creating a metrics dashboard, defining KPIs, setting up product analytics, or building a data monitoring plan.","Significant-Gravitas\u002Fskills-catalog","Significant-Gravitas","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F130738209?v=4","01M6AC83EPDGBFN19MWYRREJD0","AutoGPT","01M6ACEY98TKHDWJH0B93PHNMG",2,"MIT","https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fproduct-metrics-dashboard-design\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fproduct-metrics-dashboard-design\u002FSKILL.md",[19,24,27,32,35],{"categoryNo":20,"categoryName":21,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000013","分析与可视化","SCENARIO","使用场景",{"categoryNo":25,"categoryName":26,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":28,"categoryName":29,"taxonomyType":30,"taxonomyTypeDesc":31},"01JBSEEDCAT000000000000031","产品经理","PROFESSION","适用职业",{"categoryNo":33,"categoryName":34,"taxonomyType":30,"taxonomyTypeDesc":31},"01JBSEEDCAT000000000000032","设计师",{"categoryNo":36,"categoryName":37,"taxonomyType":30,"taxonomyTypeDesc":31},"01JBSEEDCAT000000000000033","数据分析师",[39,40,41,42,43,44,45,46,47,48],"design","dashboard","product","metrics","product metrics dashboard design","github","analytics","monitoring plan","visualization","https","2026-11-10 10:12:30",false,"---\nname: product-metrics-dashboard-design\ndescription: Define and design a product metrics dashboard with key metrics, data sources, visualization types, and alert thresholds. Use when creating a metrics dashboard, defining KPIs, setting up product analytics, or building a data monitoring plan.\nlicense: MIT\nmetadata:\n  author: Pawel Huryn\n  source: phuryn\u002Fpm-skills\u002Fpm-product-discovery\u002Fskills\u002Fmetrics-dashboard\n  source_url: https:\u002F\u002Fgithub.com\u002Fphuryn\u002Fpm-skills\u002Fblob\u002F8607e3b077817f89bf4a9b623246219734ac3be0\u002Fpm-product-discovery\u002Fskills\u002Fmetrics-dashboard\u002FSKILL.md\n  upstream-commit: 8607e3b077817f89bf4a9b623246219734ac3be0\n  original-name: metrics-dashboard\n  packaged-by: AutoGPT\n  packaged-on: '2026-09-25'\n---\n\n> Packaging adaptation by AutoGPT, 2026-09-25. Original authorship and licence are retained. Changes are limited to the recorded name, metadata and local references; see ATTRIBUTION.md in this package.\n\n\n## Product Metrics Dashboard\n\nDesign a comprehensive product metrics dashboard with the right metrics, visualizations, and alert thresholds.\n\n### Context\n\nYou are designing a metrics dashboard for **$ARGUMENTS**.\n\nIf the user provides files (existing dashboards, analytics data, OKRs, or strategy docs), read them first.\n\n### Domain Context\n\n**Metrics vs KPIs vs NSM**: Metrics = all measurable things. KPIs = a few key quantitative metrics tracked over a longer period. North Star Metric = a single customer-centric KPI that is a leading indicator of business success.\n\n**4 criteria for a good metric** (Ben Yoskovitz, *Lean Analytics*): (1) Understandable — creates a common language. (2) Comparative — over time, not a snapshot. (3) Ratio or Rate — more revealing than whole numbers. (4) Behavior-changing — the Golden Rule: \"If a metric won't change how you behave, it's a bad metric.\"\n\n**8 metric types**: Vanity vs Actionable (only actionable metrics change behavior), Qualitative vs Quantitative (WHAT vs WHY — you need both; never stop talking to customers), Exploratory vs Reporting (explore data to uncover unexpected insights), Lagging vs Leading (leading indicators enable faster learning cycles, e.g. customer complaints predict churn).\n\n**5 action steps**: (1) Audit metrics against the 4 good-metric criteria. (2) Update dashboards — ensure all key metrics are good ones. (3) Identify vanity metrics — be careful how you use them. (4) Classify leading vs lagging indicators. (5) Pick one problem and dig deep into the data.\n\nFor case studies and more detail: [Are You Tracking the Right Metrics?](https:\u002F\u002Fwww.productcompass.pm\u002Fp\u002Fare-you-tracking-the-right-metrics) by Ben Yoskovitz\n\n### Instructions\n\n1. **Identify the metrics framework** — organize metrics into layers:\n\n   **North Star Metric**: The single metric that best captures core value delivery\n\n   **Input Metrics** (3-5): The levers that drive the North Star\n\n   **Health Metrics**: Guardrails that ensure overall product health\n\n   **Business Metrics**: Revenue, cost, and unit economics\n\n2. **For each metric, define**:\n\n   | Metric | Definition | Data Source | Visualization | Target | Alert Threshold |\n   |---|---|---|---|---|---|\n   | [Name] | [Exact calculation: numerator\u002Fdenominator, time window] | [Where the data comes from] | [Line chart \u002F Bar \u002F Number \u002F Funnel] | [Goal value] | [When to trigger an alert] |\n\n3. **Design the dashboard layout**:\n\n   ```\n   ┌─────────────────────────────────────────────┐\n   │  NORTH STAR: [Metric] — [Current Value]     │\n   │  Trend: [↑\u002F↓ X% vs last period]             │\n   ├──────────────────┬──────────────────────────┤\n   │  Input Metric 1  │  Input Metric 2          │\n   │  [Sparkline]     │  [Sparkline]             │\n   ├──────────────────┼──────────────────────────┤\n   │  Input Metric 3  │  Input Metric 4          │\n   │  [Sparkline]     │  [Sparkline]             │\n   ├──────────────────┴──────────────────────────┤\n   │  HEALTH: [Latency] [Error Rate] [NPS]       │\n   ├─────────────────────────────────────────────┤\n   │  BUSINESS: [MRR] [CAC] [LTV] [Churn]        │\n   └─────────────────────────────────────────────┘\n   ```\n\n4. **Set review cadence**:\n   - **Daily**: Operational health (errors, latency, critical flows)\n   - **Weekly**: Input metrics and engagement trends\n   - **Monthly**: North Star, business metrics, OKR progress\n   - **Quarterly**: Strategic review and metric recalibration\n\n5. **Define alerts**:\n   - What thresholds trigger investigation?\n   - Who gets alerted and through what channel?\n   - What's the expected response time?\n\n6. **Recommend tools** based on the user's context:\n   - Amplitude, Mixpanel, PostHog for product analytics\n   - Looker, Metabase, Mode for SQL-based dashboards\n   - Datadog, Grafana for operational health\n\nThink step by step. Save the dashboard specification as a markdown document.\n\n---\n\n### Further Reading\n\n- [The Ultimate List of Product Metrics](https:\u002F\u002Fwww.productcompass.pm\u002Fp\u002Fthe-ultimate-list-of-product-metrics)\n- [The North Star Framework 101](https:\u002F\u002Fwww.productcompass.pm\u002Fp\u002Fthe-north-star-framework-101)\n- [The Product Analytics Playbook: AARRR, HEART, Cohorts & Funnels for PMs](https:\u002F\u002Fwww.productcompass.pm\u002Fp\u002Fthe-product-analytics-playbook-aarrr)\n- [AARRR (Pirate) Metrics: The 5-Stage Framework for Growth](https:\u002F\u002Fwww.productcompass.pm\u002Fp\u002Faarrr-pirate-metrics)\n- [The Google HEART Framework: Your Guide to Measuring User-Centric Success](https:\u002F\u002Fwww.productcompass.pm\u002Fp\u002Fthe-google-heart-framework)\n- [Funnel Analysis 101: How to Track and Optimize Your User Journey](https:\u002F\u002Fwww.productcompass.pm\u002Fp\u002Ffunnel-analysis)\n- [Are You Tracking the Right Metrics?](https:\u002F\u002Fwww.productcompass.pm\u002Fp\u002Fare-you-tracking-the-right-metrics)\n- [Continuous Product Discovery Masterclass (CPDM)](https:\u002F\u002Fwww.productcompass.pm\u002Fp\u002Fcpdm) (video course)\n","> Packaging adaptation by AutoGPT, 2026-09-25. Original authorship and licence are retained. Changes are limited to the recorded name, metadata and local references; see ATTRIBUTION.md in this package.\n\n\n## Product Metrics Dashboard\n\nDesign a comprehensive product metrics dashboard with the right metrics, visualizations, and alert thresholds.\n\n### Context\n\nYou are designing a metrics dashboard for **$ARGUMENTS**.\n\nIf the user provides files (existing dashboards, analytics data, OKRs, or strategy docs), read them first.\n\n### Domain Context\n\n**Metrics vs KPIs vs NSM**: Metrics = all measurable things. KPIs = a few key quantitative metrics tracked over a longer period. North Star Metric = a single customer-centric KPI that is a leading indicator of business success.\n\n**4 criteria for a good metric** (Ben Yoskovitz, *Lean Analytics*): (1) Understandable — creates a common language. (2) Comparative — over time, not a snapshot. (3) Ratio or Rate — more revealing than whole numbers. (4) Behavior-changing — the Golden Rule: \"If a metric won't change how you behave, it's a bad metric.\"\n\n**8 metric types**: Vanity vs Actionable (only actionable metrics change behavior), Qualitative vs Quantitative (WHAT vs WHY — you need both; never stop talking to customers), Exploratory vs Reporting (explore data to uncover unexpected insights), Lagging vs Leading (leading indicators enable faster learning cycles, e.g. customer complaints predict churn).\n\n**5 action steps**: (1) Audit metrics against the 4 good-metric criteria. (2) Update dashboards — ensure all key metrics are good ones. (3) Identify vanity metrics — be careful how you use them. (4) Classify leading vs lagging indicators. (5) Pick one problem and dig deep into the data.\n\nFor case studies and more detail: [Are You Tracking the Right Metrics?](https:\u002F\u002Fwww.productcompass.pm\u002Fp\u002Fare-you-tracking-the-right-metrics) by Ben Yoskovitz\n\n### Instructions\n\n1. **Identify the metrics framework** — organize metrics into layers:\n\n   **North Star Metric**: The single metric that best captures core value delivery\n\n   **Input Metrics** (3-5): The levers that drive the North Star\n\n   **Health Metrics**: Guardrails that ensure overall product health\n\n   **Business Metrics**: Revenue, cost, and unit economics\n\n2. **For each metric, define**:\n\n   | Metric | Definition | Data Source | Visualization | Target | Alert Threshold |\n   |---|---|---|---|---|---|\n   | [Name] | [Exact calculation: numerator\u002Fdenominator, time window] | [Where the data comes from] | [Line chart \u002F Bar \u002F Number \u002F Funnel] | [Goal value] | [When to trigger an alert] |\n\n3. **Design the dashboard layout**:\n\n   ```\n   ┌─────────────────────────────────────────────┐\n   │  NORTH STAR: [Metric] — [Current Value]     │\n   │  Trend: [↑\u002F↓ X% vs last period]             │\n   ├──────────────────┬──────────────────────────┤\n   │  Input Metric 1  │  Input Metric 2          │\n   │  [Sparkline]     │  [Sparkline]             │\n   ├──────────────────┼──────────────────────────┤\n   │  Input Metric 3  │  Input Metric 4          │\n   │  [Sparkline]     │  [Sparkline]             │\n   ├──────────────────┴──────────────────────────┤\n   │  HEALTH: [Latency] [Error Rate] [NPS]       │\n   ├─────────────────────────────────────────────┤\n   │  BUSINESS: [MRR] [CAC] [LTV] [Churn]        │\n   └─────────────────────────────────────────────┘\n   ```\n\n4. **Set review cadence**:\n   - **Daily**: Operational health (errors, latency, critical flows)\n   - **Weekly**: Input metrics and engagement trends\n   - **Monthly**: North Star, business metrics, OKR progress\n   - **Quarterly**: Strategic review and metric recalibration\n\n5. **Define alerts**:\n   - What thresholds trigger investigation?\n   - Who gets alerted and through what channel?\n   - What's the expected response time?\n\n6. **Recommend tools** based on the user's context:\n   - Amplitude, Mixpanel, PostHog for product analytics\n   - Looker, Metabase, Mode for SQL-based dashboards\n   - Datadog, Grafana for operational health\n\nThink step by step. Save the dashboard specification as a markdown document.\n\n---\n\n### Further Reading\n\n- [The Ultimate List of Product Metrics](https:\u002F\u002Fwww.productcompass.pm\u002Fp\u002Fthe-ultimate-list-of-product-metrics)\n- [The North Star Framework 101](https:\u002F\u002Fwww.productcompass.pm\u002Fp\u002Fthe-north-star-framework-101)\n- [The Product Analytics Playbook: AARRR, HEART, Cohorts & Funnels for PMs](https:\u002F\u002Fwww.productcompass.pm\u002Fp\u002Fthe-product-analytics-playbook-aarrr)\n- [AARRR (Pirate) Metrics: The 5-Stage Framework for Growth](https:\u002F\u002Fwww.productcompass.pm\u002Fp\u002Faarrr-pirate-metrics)\n- [The Google HEART Framework: Your Guide to Measuring User-Centric Success](https:\u002F\u002Fwww.productcompass.pm\u002Fp\u002Fthe-google-heart-framework)\n- [Funnel Analysis 101: How to Track and Optimize Your User Journey](https:\u002F\u002Fwww.productcompass.pm\u002Fp\u002Ffunnel-analysis)\n- [Are You Tracking the Right Metrics?](https:\u002F\u002Fwww.productcompass.pm\u002Fp\u002Fare-you-tracking-the-right-metrics)\n- [Continuous Product Discovery Masterclass (CPDM)](https:\u002F\u002Fwww.productcompass.pm\u002Fp\u002Fcpdm) (video course)\n",{"name":5,"license":15,"metadata":54,"description":7},{"author":55,"source":56,"source_url":57,"packaged-by":12,"packaged-on":58,"original-name":59,"upstream-commit":60},"Pawel Huryn","phuryn\u002Fpm-skills\u002Fpm-product-discovery\u002Fskills\u002Fmetrics-dashboard","https:\u002F\u002Fgithub.com\u002Fphuryn\u002Fpm-skills\u002Fblob\u002F8607e3b077817f89bf4a9b623246219734ac3be0\u002Fpm-product-discovery\u002Fskills\u002Fmetrics-dashboard\u002FSKILL.md","2026-09-25","metrics-dashboard","8607e3b077817f89bf4a9b623246219734ac3be0","main","7ed3b73314d5186120e8d4006febfd7e1c92cf08","skills\u002Fproduct-metrics-dashboard-design",0,"https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002Fskills-catalog","https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas","agpt.co","2026-11-10 22:38:50",{"source":8,"skillName":5,"commands":70,"prompt":80,"zipAvailable":81},[71,74,77],{"tool":72,"command":73},"npx","npx skills add Significant-Gravitas\u002Fskills-catalog --skill product-metrics-dashboard-design",{"tool":75,"command":76},"bunx","bunx skills add Significant-Gravitas\u002Fskills-catalog --skill product-metrics-dashboard-design",{"tool":78,"command":79},"pnpm","pnpm dlx skills add Significant-Gravitas\u002Fskills-catalog --skill product-metrics-dashboard-design","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fproduct-metrics-dashboard-design\u002FSKILL.md",true,[83,89],{"path":84,"resourceType":85,"resourceTypeDesc":86,"sizeBytes":87,"sourceHtmlUrl":88},"ATTRIBUTION.md","REFERENCE","参考文档",1233,"https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fproduct-metrics-dashboard-design\u002FATTRIBUTION.md",{"path":90,"resourceType":90,"resourceTypeDesc":91,"sizeBytes":92,"sourceHtmlUrl":16},"LICENSE","许可证",1068,[94,107,130,140,154,172],{"skillNo":95,"name":96,"nameZh":6,"description":97,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":98,"licenseFileUrl":99,"sourceHtmlUrl":100,"categories":101,"keywords":102,"publishedAt":49},"01M6AD3S89TGF74S7MK4K5CGC7","workplace-memory-management","Two-tier memory system that makes Claude a true workplace collaborator. Decodes shorthand, acronyms, nicknames, and internal language so Claude understands requests like a colleague would. CLAUDE.md for working memory, memory\u002F directory for the full knowledge base.","Apache-2.0","https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fworkplace-memory-management\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fworkplace-memory-management\u002FSKILL.md",[],[103,104,105,48,44,106],"workplace memory management","memory","plugins","directory",{"skillNo":108,"name":109,"nameZh":6,"description":110,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":111,"sourceHtmlUrl":112,"categories":113,"keywords":123,"publishedAt":49},"01M6AD3F5X5DYW1BABD49BQKP8","website-seo-audit","Audit a website, investigate its real search opportunities, and deliver a short data-backed report on the few changes most likely to grow organic traffic that converts.","https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fwebsite-seo-audit\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fwebsite-seo-audit\u002FSKILL.md",[114,117,120],{"categoryNo":115,"categoryName":116,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000019","营销推广",{"categoryNo":118,"categoryName":119,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":121,"categoryName":122,"taxonomyType":30,"taxonomyTypeDesc":31},"01JBSEEDCAT000000000000035","市场营销",[124,125,126,44,105,48,127,128,129],"website seo audit","audit","website","search","report","plugin",{"skillNo":131,"name":132,"nameZh":6,"description":133,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":134,"sourceHtmlUrl":135,"categories":136,"keywords":137,"publishedAt":49},"01M6AD32RYMK03P50DM0T184NJ","verification-before-completion","Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always","https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fverification-before-completion\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fverification-before-completion\u002FSKILL.md",[],[138,139,48,44],"verification before completion","verification",{"skillNo":141,"name":142,"nameZh":6,"description":143,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":98,"licenseFileUrl":144,"sourceHtmlUrl":145,"categories":146,"keywords":147,"publishedAt":49},"01M6AD2WSQG7BGPXA7XP4DFZX8","vendor-evaluation","Evaluate a vendor — cost analysis, risk assessment, and recommendation. Use when reviewing a new vendor proposal, deciding whether to renew or replace a contract, comparing two vendors side-by-side, or building a TCO breakdown and negotiation points before procurement sign-off.","https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fvendor-evaluation\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fvendor-evaluation\u002FSKILL.md",[],[148,149,105,48,150,151,152,44,153],"vendor evaluation","negotiation","assessment","risk assessment","cost analysis","review",{"skillNo":155,"name":156,"nameZh":6,"description":157,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":98,"licenseFileUrl":158,"sourceHtmlUrl":159,"categories":160,"keywords":167,"publishedAt":49},"01M6AD2RBT6004C07RZRDSTA52","vendor-contract-status","Check the status of existing agreements with a vendor across all connected systems — CLM, CRM, email, and document storage — with gap analysis and upcoming deadlines. Use when onboarding or renewing a vendor, when you need a consolidated view of what's signed and what's missing (MSA, DPA, SOW), or when checking for approaching expirations and surviving obligations.","https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fvendor-contract-status\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fvendor-contract-status\u002FSKILL.md",[161,164],{"categoryNo":162,"categoryName":163,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000005","邮件与沟通",{"categoryNo":165,"categoryName":166,"taxonomyType":30,"taxonomyTypeDesc":31},"01JBSEEDCAT000000000000037","律师与法务",[168,169,44,170,105,171,48],"vendor contract status","status","storage","onboarding",{"skillNo":173,"name":174,"nameZh":6,"description":175,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":176,"sourceHtmlUrl":177,"categories":178,"keywords":180,"publishedAt":49},"01M6AD2KPNXN5CNS3CA4FRWT97","user-cohort-analysis","Perform cohort analysis on user engagement data — retention curves, feature adoption trends, and segment-level insights. Use when analyzing user retention by cohort, studying feature adoption over time, investigating churn patterns, or identifying engagement trends.","https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fuser-cohort-analysis\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fuser-cohort-analysis\u002FSKILL.md",[179],{"categoryNo":36,"categoryName":37,"taxonomyType":30,"taxonomyTypeDesc":31},[181,45,48,44],"user cohort analysis"]