[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-anthropic-metrics-review":3,"mdc--lb3h9t-key":34,"related-org-anthropic-metrics-review":2056,"related-repo-anthropic-metrics-review":2245},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":12,"stars":23,"repoUrl":24,"updatedAt":25,"license":26,"forks":27,"topics":28,"repo":29,"sourceUrl":32,"mdContent":33},"metrics-review","review product metrics and trends","Review and analyze product metrics with trend analysis and actionable insights. Use when running a weekly, monthly, or quarterly metrics review, investigating a sudden spike or drop, comparing performance against targets, or turning raw numbers into a scorecard with recommended actions.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},"anthropic","Anthropic","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fanthropic.png","anthropics",[13,17,20],{"name":14,"slug":15,"type":16},"Reporting","reporting","tag",{"name":18,"slug":19,"type":16},"Product Management","product-management",{"name":21,"slug":22,"type":16},"Analytics","analytics",22885,"https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins","2026-04-06T17:59:05.276705",null,2736,[],{"repoUrl":24,"stars":23,"forks":27,"topics":30,"description":31},[],"Open source repository of plugins primarily intended for knowledge workers to use in Claude Cowork","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Ftree\u002FHEAD\u002Fproduct-management\u002Fskills\u002Fmetrics-review","---\nname: metrics-review\ndescription: Review and analyze product metrics with trend analysis and actionable insights. Use when running a weekly, monthly, or quarterly metrics review, investigating a sudden spike or drop, comparing performance against targets, or turning raw numbers into a scorecard with recommended actions.\nargument-hint: \"\u003Ctime period or metric focus>\"\n---\n\n# Metrics Review\n\n> If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](..\u002F..\u002FCONNECTORS.md).\n\nReview and analyze product metrics, identify trends, and surface actionable insights.\n\n## Usage\n\n```\n\u002Fmetrics-review $ARGUMENTS\n```\n\n## Workflow\n\n### 1. Gather Metrics Data\n\nIf **~~product analytics** is connected:\n- Pull key product metrics for the relevant time period\n- Get comparison data (previous period, same period last year, targets)\n- Pull segment breakdowns if available\n\nIf no analytics tool is connected, ask the user to provide:\n- The metrics and their values (paste a table, screenshot, or describe)\n- Comparison data (previous period, targets)\n- Any context on recent changes (launches, incidents, seasonality)\n\nAsk the user:\n- What time period to review? (last week, last month, last quarter)\n- What metrics to focus on? Or should we review the full product metrics suite?\n- Are there specific targets or goals to compare against?\n- Any known events that might explain changes (launches, outages, marketing campaigns, seasonality)?\n\n### 2. Organize the Metrics\n\nStructure the review using a metrics hierarchy: North Star metric at the top, L1 health indicators (acquisition, activation, engagement, retention, revenue, satisfaction), and L2 diagnostic metrics for drill-down. See **Product Metrics Hierarchy** below for full definitions.\n\nIf the user has not defined their metrics hierarchy, help them identify their North Star and key L1 metrics before proceeding.\n\n### 3. Analyze Trends\n\nFor each key metric:\n- **Current value**: What is the metric today?\n- **Trend**: Up, down, or flat compared to previous period? Over what timeframe?\n- **vs Target**: How does it compare to the goal or target?\n- **Rate of change**: Is the trend accelerating or decelerating?\n- **Anomalies**: Any sudden changes, spikes, or drops?\n\nIdentify correlations:\n- Do changes in one metric correlate with changes in another?\n- Are there leading indicators that predict lagging metric changes?\n- Do segment breakdowns reveal that an aggregate trend is driven by a specific cohort?\n\n### 4. Generate the Review\n\n#### Summary\n2-3 sentences: overall product health, most notable changes, key callout.\n\n#### Metric Scorecard\nTable format for quick scanning:\n\n| Metric | Current | Previous | Change | Target | Status |\n|--------|---------|----------|--------|--------|--------|\n| [Metric] | [Value] | [Value] | [+\u002F- %] | [Target] | [On track \u002F At risk \u002F Miss] |\n\n#### Trend Analysis\nFor each metric worth discussing:\n- What happened and how significant is the change\n- Why it likely happened (attribution based on known events, correlated metrics, segment analysis)\n- Whether this is a one-time event or a sustained trend\n\n#### Bright Spots\nWhat is going well:\n- Metrics beating targets\n- Positive trends to sustain\n- Segments or features showing strong performance\n\n#### Areas of Concern\nWhat needs attention:\n- Metrics missing targets or trending negatively\n- Early warning signals before they become problems\n- Metrics where we lack visibility or understanding\n\n#### Recommended Actions\nSpecific next steps based on the analysis:\n- Investigations to run (dig deeper into a concerning trend)\n- Experiments to launch (test hypotheses about what could improve a metric)\n- Investments to make (double down on what is working)\n- Alerts to set (monitor a metric more closely)\n\n#### Context and Caveats\n- Known data quality issues\n- Events that affect comparability (outages, holidays, launches)\n- Metrics we should be tracking but are not yet\n\n### 5. Follow Up\n\nAfter generating the review:\n- Ask if any metric needs deeper investigation\n- Offer to create a dashboard spec for ongoing monitoring\n- Offer to draft experiment proposals for areas of concern\n- Offer to set up a metrics review template for recurring use\n\n## Product Metrics Hierarchy\n\n### North Star Metric\nThe single metric that best captures the core value your product delivers to users. It should be:\n\n- **Value-aligned**: Moves when users get more value from the product\n- **Leading**: Predicts long-term business success (revenue, retention)\n- **Actionable**: The product team can influence it through their work\n- **Understandable**: Everyone in the company can understand what it means and why it matters\n\n**Examples by product type**:\n- Collaboration tool: Weekly active teams with 3+ members contributing\n- Marketplace: Weekly transactions completed\n- SaaS platform: Weekly active users completing core workflow\n- Content platform: Weekly engaged reading\u002Fviewing time\n- Developer tool: Weekly deployments using the tool\n\n### L1 Metrics (Health Indicators)\nThe 5-7 metrics that together paint a complete picture of product health. These map to the key stages of the user lifecycle:\n\n**Acquisition**: Are new users finding the product?\n- New signups or trial starts (volume and trend)\n- Signup conversion rate (visitors to signups)\n- Channel mix (where are new users coming from)\n- Cost per acquisition (for paid channels)\n\n**Activation**: Are new users reaching the value moment?\n- Activation rate: % of new users who complete the key action that predicts retention\n- Time to activate: how long from signup to activation\n- Setup completion rate: % who complete onboarding steps\n- First value moment: when users first experience the core product value\n\n**Engagement**: Are active users getting value?\n- DAU \u002F WAU \u002F MAU: active users at different timeframes\n- DAU\u002FMAU ratio (stickiness): what fraction of monthly users come back daily\n- Core action frequency: how often users do the thing that matters most\n- Session depth: how much users do per session\n- Feature adoption: % of users using key features\n\n**Retention**: Are users coming back?\n- D1, D7, D30 retention: % of users who return after 1 day, 7 days, 30 days\n- Cohort retention curves: how retention evolves for each signup cohort\n- Churn rate: % of users or revenue lost per period\n- Resurrection rate: % of churned users who come back\n\n**Monetization**: Is value translating to revenue?\n- Conversion rate: free to paid (for freemium)\n- MRR \u002F ARR: monthly or annual recurring revenue\n- ARPU \u002F ARPA: average revenue per user or account\n- Expansion revenue: revenue growth from existing customers\n- Net revenue retention: revenue retention including expansion and contraction\n\n**Satisfaction**: How do users feel about the product?\n- NPS: Net Promoter Score\n- CSAT: Customer Satisfaction Score\n- Support ticket volume and resolution time\n- App store ratings and review sentiment\n\n### L2 Metrics (Diagnostic)\nDetailed metrics used to investigate changes in L1 metrics:\n\n- Funnel conversion at each step\n- Feature-level usage and adoption\n- Segment-specific breakdowns (by plan, company size, geography, user role)\n- Performance metrics (page load time, error rate, API latency)\n- Content-specific engagement (which features, pages, or content types drive engagement)\n\n## Common Product Metrics\n\n### DAU \u002F WAU \u002F MAU\n**What they measure**: Unique users who perform a qualifying action in a day, week, or month.\n\n**Key decisions**:\n- What counts as \"active\"? A login? A page view? A core action? Define this carefully — different definitions tell different stories.\n- Which timeframe matters most? DAU for daily-use products (messaging, email). WAU for weekly-use products (project management). MAU for less frequent products (tax software, travel booking).\n\n**How to use them**:\n- DAU\u002FMAU ratio (stickiness): values above 0.5 indicate a daily habit. Below 0.2 suggests infrequent usage.\n- Trend matters more than absolute number. Is active usage growing, flat, or declining?\n- Segment by user type. Power users and casual users behave very differently.\n\n### Retention\n**What it measures**: Of users who started in period X, what % are still active in period Y?\n\n**Common retention timeframes**:\n- D1 (next day): Was the first experience good enough to come back?\n- D7 (one week): Did the user establish a habit?\n- D30 (one month): Is the user retained long-term?\n- D90 (three months): Is this a durable user?\n\n**How to use retention**:\n- Plot retention curves by cohort. Look for: initial drop-off (activation problem), steady decline (engagement problem), or flattening (good — you have a stable retained base).\n- Compare cohorts over time. Are newer cohorts retaining better than older ones? That means product improvements are working.\n- Segment retention by activation behavior. Users who completed onboarding vs those who did not. Users who used feature X vs those who did not.\n\n### Conversion\n**What it measures**: % of users who move from one stage to the next.\n\n**Common conversion funnels**:\n- Visitor to signup\n- Signup to activation (key value moment)\n- Free to paid (trial conversion)\n- Trial to paid subscription\n- Monthly to annual plan\n\n**How to use conversion**:\n- Map the full funnel and measure conversion at each step\n- Identify the biggest drop-off points — these are your highest-leverage improvement opportunities\n- Segment conversion by source, plan, user type. Different segments convert very differently.\n- Track conversion over time. Is it improving as you iterate on the experience?\n\n### Activation\n**What it measures**: % of new users who reach the moment where they first experience the product's core value.\n\n**Defining activation**:\n- Look at retained users vs churned users. What actions did retained users take that churned users did not?\n- The activation event should be strongly predictive of long-term retention\n- It should be achievable within the first session or first few days\n- Examples: created first project, invited a teammate, completed first workflow, connected an integration\n\n**How to use activation**:\n- Track activation rate for every signup cohort\n- Measure time to activate — faster is almost always better\n- Build onboarding flows that guide users to the activation moment\n- A\u002FB test activation flows and measure impact on retention, not just activation rate\n\n## Goal Setting Frameworks\n\n### OKRs (Objectives and Key Results)\n\n**Objectives**: Qualitative, aspirational goals that describe what you want to achieve.\n- Inspiring and memorable\n- Time-bound (quarterly or annually)\n- Directional, not metric-specific\n\n**Key Results**: Quantitative measures that tell you if you achieved the objective.\n- Specific and measurable\n- Time-bound with a clear target\n- Outcome-based, not output-based\n- 2-4 Key Results per Objective\n\n**Example**:\n```\nObjective: Make our product indispensable for daily workflows\n\nKey Results:\n- Increase DAU\u002FMAU ratio from 0.35 to 0.50\n- Increase D30 retention for new users from 40% to 55%\n- 3 core workflows with >80% task completion rate\n```\n\n### OKR Best Practices\n- Set OKRs that are ambitious but achievable. 70% completion is the target for stretch OKRs.\n- Key Results should measure outcomes (user behavior, business results), not outputs (features shipped, tasks completed).\n- Do not have too many OKRs. 2-3 objectives with 2-4 KRs each is plenty.\n- OKRs should be uncomfortable. If you are confident you will hit all of them, they are not ambitious enough.\n- Review OKRs at mid-period. Adjust effort allocation if some KRs are clearly off track.\n- Grade OKRs honestly at end of period. 0.0-0.3 = missed, 0.4-0.6 = progress, 0.7-1.0 = achieved.\n\n### Setting Metric Targets\n- **Baseline**: What is the current value? You need a reliable baseline before setting a target.\n- **Benchmark**: What do comparable products achieve? Industry benchmarks provide context.\n- **Trajectory**: What is the current trend? If the metric is already improving at 5% per month, a 6% target is not ambitious.\n- **Effort**: How much investment are you putting behind this? Bigger bets warrant more ambitious targets.\n- **Confidence**: How confident are you in hitting the target? Set a \"commit\" (high confidence) and a \"stretch\" (ambitious).\n\n## Metric Review Cadences\n\n### Weekly Metrics Check\n**Purpose**: Catch issues quickly, monitor experiments, stay in touch with product health.\n**Duration**: 15-30 minutes.\n**Attendees**: Product manager, maybe engineering lead.\n\n**What to review**:\n- North Star metric: current value, week-over-week change\n- Key L1 metrics: any notable movements\n- Active experiments: results and statistical significance\n- Anomalies: any unexpected spikes or drops\n- Alerts: anything that triggered a monitoring alert\n\n**Action**: If something looks off, investigate. Otherwise, note it and move on.\n\n### Monthly Metrics Review\n**Purpose**: Deeper analysis of trends, progress against goals, strategic implications.\n**Duration**: 30-60 minutes.\n**Attendees**: Product team, key stakeholders.\n\n**What to review**:\n- Full L1 metric scorecard with month-over-month trends\n- Progress against quarterly OKR targets\n- Cohort analysis: are newer cohorts performing better?\n- Feature adoption: how are recent launches performing?\n- Segment analysis: any divergence between user segments?\n\n**Action**: Identify 1-3 areas to investigate or invest in. Update priorities if metrics reveal new information.\n\n### Quarterly Business Review\n**Purpose**: Strategic assessment of product performance, goal-setting for next quarter.\n**Duration**: 60-90 minutes.\n**Attendees**: Product, engineering, design, leadership.\n\n**What to review**:\n- OKR scoring for the quarter\n- Trend analysis for all L1 metrics over the quarter\n- Year-over-year comparisons\n- Competitive context: market changes and competitor movements\n- What worked and what did not\n\n**Action**: Set OKRs for next quarter. Adjust product strategy based on what the data shows.\n\n## Dashboard Design Principles\n\n### Effective Product Dashboards\nA good dashboard answers the question \"How is the product doing?\" at a glance.\n\n**Principles**:\n\n1. **Start with the question, not the data**. What decisions does this dashboard support? Design backwards from the decision.\n\n2. **Hierarchy of information**. The most important metric should be the most visually prominent. North Star at the top, L1 metrics next, L2 metrics available on drill-down.\n\n3. **Context over numbers**. A number without context is meaningless. Always show: current value, comparison (previous period, target, benchmark), trend direction.\n\n4. **Fewer metrics, more insight**. A dashboard with 50 metrics helps no one. Focus on 5-10 that matter. Put everything else in a detailed report.\n\n5. **Consistent time periods**. Use the same time period for all metrics on a dashboard. Mixing daily and monthly metrics creates confusion.\n\n6. **Visual status indicators**. Use color to indicate health at a glance:\n   - Green: on track or improving\n   - Yellow: needs attention or flat\n   - Red: off track or declining\n\n7. **Actionability**. Every metric on the dashboard should be something the team can influence. If you cannot act on it, it does not belong on the product dashboard.\n\n### Dashboard Layout\n\n**Top row**: North Star metric with trend line and target.\n\n**Second row**: L1 metrics scorecard — current value, change, target, status for each key metric.\n\n**Third row**: Key funnels or conversion metrics — visual funnel showing drop-off at each stage.\n\n**Fourth row**: Recent experiments and launches — active A\u002FB tests, recent feature launches with early metrics.\n\n**Bottom \u002F drill-down**: L2 metrics, segment breakdowns, and detailed time series for investigation.\n\n### Dashboard Anti-Patterns\n- **Vanity metrics**: Metrics that always go up but do not indicate health (total signups ever, total page views)\n- **Too many metrics**: Dashboards that require scrolling to see. If it does not fit on one screen, cut metrics.\n- **No comparison**: Raw numbers without context (current value with no previous period or target)\n- **Stale dashboards**: Metrics that have not been updated or reviewed in months\n- **Output dashboards**: Measuring team activity (tickets closed, PRs merged) instead of user and business outcomes\n- **One dashboard for all audiences**: Executives, PMs, and engineers need different views. One size does not fit all.\n\n### Alerting\nSet alerts for metrics that require immediate attention:\n\n- **Threshold alerts**: Metric drops below or rises above a critical threshold (error rate > 1%, conversion \u003C 5%)\n- **Trend alerts**: Metric shows sustained decline over multiple days\u002Fweeks\n- **Anomaly alerts**: Metric deviates significantly from expected range\n\n**Alert hygiene**:\n- Every alert should be actionable. If you cannot do anything about it, do not alert on it.\n- Review and tune alerts regularly. Too many false positives and people ignore all alerts.\n- Define an owner for each alert. Who responds when it fires?\n- Set appropriate severity levels. Not everything is P0.\n\n## Output Format\n\nUse tables for the scorecard. Use clear status indicators. Keep the summary tight — the reader should get the essential story in 30 seconds.\n\n## Tips\n\n- Start with the \"so what\" — what is the most important thing in this metrics review? Lead with that.\n- Absolute numbers without context are useless. Always show comparisons (vs previous period, vs target, vs benchmark).\n- Be careful about attribution. Correlation is not causation. If a metric moved, acknowledge uncertainty about why.\n- Segment analysis often reveals that an aggregate metric masks important differences. A flat overall number might hide one segment growing and another shrinking.\n- Not all metric movements matter. Small fluctuations are noise. Focus attention on meaningful changes.\n- If a metric is missing its target, do not just report the miss — recommend what to do about it.\n- Metrics reviews should drive decisions. If the review does not lead to at least one action, it was not useful.\n",{"data":35,"body":37},{"name":4,"description":6,"argument-hint":36},"\u003Ctime period or metric focus>",{"type":38,"children":39},"root",[40,48,67,72,79,92,98,105,118,138,143,161,166,189,195,207,212,218,223,276,281,299,305,312,317,323,328,425,431,436,454,460,465,483,489,494,512,518,523,546,552,570,576,581,604,609,615,620,663,673,701,707,712,722,745,755,778,788,816,826,849,859,887,897,920,926,931,959,965,971,981,990,1003,1012,1030,1035,1045,1054,1077,1086,1104,1110,1119,1128,1156,1165,1188,1193,1202,1211,1234,1243,1266,1272,1278,1288,1306,1316,1339,1348,1357,1363,1396,1402,1455,1461,1467,1491,1500,1528,1538,1544,1565,1573,1601,1610,1616,1637,1645,1673,1682,1688,1694,1699,1708,1800,1806,1816,1826,1836,1846,1856,1862,1925,1931,1936,1969,1978,2001,2007,2012,2018],{"type":41,"tag":42,"props":43,"children":44},"element","h1",{"id":4},[45],{"type":46,"value":47},"text","Metrics Review",{"type":41,"tag":49,"props":50,"children":51},"blockquote",{},[52],{"type":41,"tag":53,"props":54,"children":55},"p",{},[56,58,65],{"type":46,"value":57},"If you see unfamiliar placeholders or need to check which tools are connected, see ",{"type":41,"tag":59,"props":60,"children":62},"a",{"href":61},"..\u002F..\u002FCONNECTORS.md",[63],{"type":46,"value":64},"CONNECTORS.md",{"type":46,"value":66},".",{"type":41,"tag":53,"props":68,"children":69},{},[70],{"type":46,"value":71},"Review and analyze product metrics, identify trends, and surface actionable insights.",{"type":41,"tag":73,"props":74,"children":76},"h2",{"id":75},"usage",[77],{"type":46,"value":78},"Usage",{"type":41,"tag":80,"props":81,"children":85},"pre",{"className":82,"code":84,"language":46},[83],"language-text","\u002Fmetrics-review $ARGUMENTS\n",[86],{"type":41,"tag":87,"props":88,"children":90},"code",{"__ignoreMap":89},"",[91],{"type":46,"value":84},{"type":41,"tag":73,"props":93,"children":95},{"id":94},"workflow",[96],{"type":46,"value":97},"Workflow",{"type":41,"tag":99,"props":100,"children":102},"h3",{"id":101},"_1-gather-metrics-data",[103],{"type":46,"value":104},"1. Gather Metrics Data",{"type":41,"tag":53,"props":106,"children":107},{},[108,110,116],{"type":46,"value":109},"If ",{"type":41,"tag":111,"props":112,"children":113},"strong",{},[114],{"type":46,"value":115},"~~product analytics",{"type":46,"value":117}," is connected:",{"type":41,"tag":119,"props":120,"children":121},"ul",{},[122,128,133],{"type":41,"tag":123,"props":124,"children":125},"li",{},[126],{"type":46,"value":127},"Pull key product metrics for the relevant time period",{"type":41,"tag":123,"props":129,"children":130},{},[131],{"type":46,"value":132},"Get comparison data (previous period, same period last year, targets)",{"type":41,"tag":123,"props":134,"children":135},{},[136],{"type":46,"value":137},"Pull segment breakdowns if available",{"type":41,"tag":53,"props":139,"children":140},{},[141],{"type":46,"value":142},"If no analytics tool is connected, ask the user to provide:",{"type":41,"tag":119,"props":144,"children":145},{},[146,151,156],{"type":41,"tag":123,"props":147,"children":148},{},[149],{"type":46,"value":150},"The metrics and their values (paste a table, screenshot, or describe)",{"type":41,"tag":123,"props":152,"children":153},{},[154],{"type":46,"value":155},"Comparison data (previous period, targets)",{"type":41,"tag":123,"props":157,"children":158},{},[159],{"type":46,"value":160},"Any context on recent changes (launches, incidents, seasonality)",{"type":41,"tag":53,"props":162,"children":163},{},[164],{"type":46,"value":165},"Ask the user:",{"type":41,"tag":119,"props":167,"children":168},{},[169,174,179,184],{"type":41,"tag":123,"props":170,"children":171},{},[172],{"type":46,"value":173},"What time period to review? (last week, last month, last quarter)",{"type":41,"tag":123,"props":175,"children":176},{},[177],{"type":46,"value":178},"What metrics to focus on? Or should we review the full product metrics suite?",{"type":41,"tag":123,"props":180,"children":181},{},[182],{"type":46,"value":183},"Are there specific targets or goals to compare against?",{"type":41,"tag":123,"props":185,"children":186},{},[187],{"type":46,"value":188},"Any known events that might explain changes (launches, outages, marketing campaigns, seasonality)?",{"type":41,"tag":99,"props":190,"children":192},{"id":191},"_2-organize-the-metrics",[193],{"type":46,"value":194},"2. Organize the Metrics",{"type":41,"tag":53,"props":196,"children":197},{},[198,200,205],{"type":46,"value":199},"Structure the review using a metrics hierarchy: North Star metric at the top, L1 health indicators (acquisition, activation, engagement, retention, revenue, satisfaction), and L2 diagnostic metrics for drill-down. See ",{"type":41,"tag":111,"props":201,"children":202},{},[203],{"type":46,"value":204},"Product Metrics Hierarchy",{"type":46,"value":206}," below for full definitions.",{"type":41,"tag":53,"props":208,"children":209},{},[210],{"type":46,"value":211},"If the user has not defined their metrics hierarchy, help them identify their North Star and key L1 metrics before proceeding.",{"type":41,"tag":99,"props":213,"children":215},{"id":214},"_3-analyze-trends",[216],{"type":46,"value":217},"3. Analyze Trends",{"type":41,"tag":53,"props":219,"children":220},{},[221],{"type":46,"value":222},"For each key metric:",{"type":41,"tag":119,"props":224,"children":225},{},[226,236,246,256,266],{"type":41,"tag":123,"props":227,"children":228},{},[229,234],{"type":41,"tag":111,"props":230,"children":231},{},[232],{"type":46,"value":233},"Current value",{"type":46,"value":235},": What is the metric today?",{"type":41,"tag":123,"props":237,"children":238},{},[239,244],{"type":41,"tag":111,"props":240,"children":241},{},[242],{"type":46,"value":243},"Trend",{"type":46,"value":245},": Up, down, or flat compared to previous period? Over what timeframe?",{"type":41,"tag":123,"props":247,"children":248},{},[249,254],{"type":41,"tag":111,"props":250,"children":251},{},[252],{"type":46,"value":253},"vs Target",{"type":46,"value":255},": How does it compare to the goal or target?",{"type":41,"tag":123,"props":257,"children":258},{},[259,264],{"type":41,"tag":111,"props":260,"children":261},{},[262],{"type":46,"value":263},"Rate of change",{"type":46,"value":265},": Is the trend accelerating or decelerating?",{"type":41,"tag":123,"props":267,"children":268},{},[269,274],{"type":41,"tag":111,"props":270,"children":271},{},[272],{"type":46,"value":273},"Anomalies",{"type":46,"value":275},": Any sudden changes, spikes, or drops?",{"type":41,"tag":53,"props":277,"children":278},{},[279],{"type":46,"value":280},"Identify correlations:",{"type":41,"tag":119,"props":282,"children":283},{},[284,289,294],{"type":41,"tag":123,"props":285,"children":286},{},[287],{"type":46,"value":288},"Do changes in one metric correlate with changes in another?",{"type":41,"tag":123,"props":290,"children":291},{},[292],{"type":46,"value":293},"Are there leading indicators that predict lagging metric changes?",{"type":41,"tag":123,"props":295,"children":296},{},[297],{"type":46,"value":298},"Do segment breakdowns reveal that an aggregate trend is driven by a specific cohort?",{"type":41,"tag":99,"props":300,"children":302},{"id":301},"_4-generate-the-review",[303],{"type":46,"value":304},"4. Generate the Review",{"type":41,"tag":306,"props":307,"children":309},"h4",{"id":308},"summary",[310],{"type":46,"value":311},"Summary",{"type":41,"tag":53,"props":313,"children":314},{},[315],{"type":46,"value":316},"2-3 sentences: overall product health, most notable changes, key callout.",{"type":41,"tag":306,"props":318,"children":320},{"id":319},"metric-scorecard",[321],{"type":46,"value":322},"Metric Scorecard",{"type":41,"tag":53,"props":324,"children":325},{},[326],{"type":46,"value":327},"Table format for quick scanning:",{"type":41,"tag":329,"props":330,"children":331},"table",{},[332,371],{"type":41,"tag":333,"props":334,"children":335},"thead",{},[336],{"type":41,"tag":337,"props":338,"children":339},"tr",{},[340,346,351,356,361,366],{"type":41,"tag":341,"props":342,"children":343},"th",{},[344],{"type":46,"value":345},"Metric",{"type":41,"tag":341,"props":347,"children":348},{},[349],{"type":46,"value":350},"Current",{"type":41,"tag":341,"props":352,"children":353},{},[354],{"type":46,"value":355},"Previous",{"type":41,"tag":341,"props":357,"children":358},{},[359],{"type":46,"value":360},"Change",{"type":41,"tag":341,"props":362,"children":363},{},[364],{"type":46,"value":365},"Target",{"type":41,"tag":341,"props":367,"children":368},{},[369],{"type":46,"value":370},"Status",{"type":41,"tag":372,"props":373,"children":374},"tbody",{},[375],{"type":41,"tag":337,"props":376,"children":377},{},[378,387,395,402,410,417],{"type":41,"tag":379,"props":380,"children":381},"td",{},[382],{"type":41,"tag":383,"props":384,"children":385},"span",{},[386],{"type":46,"value":345},{"type":41,"tag":379,"props":388,"children":389},{},[390],{"type":41,"tag":383,"props":391,"children":392},{},[393],{"type":46,"value":394},"Value",{"type":41,"tag":379,"props":396,"children":397},{},[398],{"type":41,"tag":383,"props":399,"children":400},{},[401],{"type":46,"value":394},{"type":41,"tag":379,"props":403,"children":404},{},[405],{"type":41,"tag":383,"props":406,"children":407},{},[408],{"type":46,"value":409},"+\u002F- %",{"type":41,"tag":379,"props":411,"children":412},{},[413],{"type":41,"tag":383,"props":414,"children":415},{},[416],{"type":46,"value":365},{"type":41,"tag":379,"props":418,"children":419},{},[420],{"type":41,"tag":383,"props":421,"children":422},{},[423],{"type":46,"value":424},"On track \u002F At risk \u002F Miss",{"type":41,"tag":306,"props":426,"children":428},{"id":427},"trend-analysis",[429],{"type":46,"value":430},"Trend Analysis",{"type":41,"tag":53,"props":432,"children":433},{},[434],{"type":46,"value":435},"For each metric worth discussing:",{"type":41,"tag":119,"props":437,"children":438},{},[439,444,449],{"type":41,"tag":123,"props":440,"children":441},{},[442],{"type":46,"value":443},"What happened and how significant is the change",{"type":41,"tag":123,"props":445,"children":446},{},[447],{"type":46,"value":448},"Why it likely happened (attribution based on known events, correlated metrics, segment analysis)",{"type":41,"tag":123,"props":450,"children":451},{},[452],{"type":46,"value":453},"Whether this is a one-time event or a sustained trend",{"type":41,"tag":306,"props":455,"children":457},{"id":456},"bright-spots",[458],{"type":46,"value":459},"Bright Spots",{"type":41,"tag":53,"props":461,"children":462},{},[463],{"type":46,"value":464},"What is going well:",{"type":41,"tag":119,"props":466,"children":467},{},[468,473,478],{"type":41,"tag":123,"props":469,"children":470},{},[471],{"type":46,"value":472},"Metrics beating targets",{"type":41,"tag":123,"props":474,"children":475},{},[476],{"type":46,"value":477},"Positive trends to sustain",{"type":41,"tag":123,"props":479,"children":480},{},[481],{"type":46,"value":482},"Segments or features showing strong performance",{"type":41,"tag":306,"props":484,"children":486},{"id":485},"areas-of-concern",[487],{"type":46,"value":488},"Areas of Concern",{"type":41,"tag":53,"props":490,"children":491},{},[492],{"type":46,"value":493},"What needs attention:",{"type":41,"tag":119,"props":495,"children":496},{},[497,502,507],{"type":41,"tag":123,"props":498,"children":499},{},[500],{"type":46,"value":501},"Metrics missing targets or trending negatively",{"type":41,"tag":123,"props":503,"children":504},{},[505],{"type":46,"value":506},"Early warning signals before they become problems",{"type":41,"tag":123,"props":508,"children":509},{},[510],{"type":46,"value":511},"Metrics where we lack visibility or understanding",{"type":41,"tag":306,"props":513,"children":515},{"id":514},"recommended-actions",[516],{"type":46,"value":517},"Recommended Actions",{"type":41,"tag":53,"props":519,"children":520},{},[521],{"type":46,"value":522},"Specific next steps based on the analysis:",{"type":41,"tag":119,"props":524,"children":525},{},[526,531,536,541],{"type":41,"tag":123,"props":527,"children":528},{},[529],{"type":46,"value":530},"Investigations to run (dig deeper into a concerning trend)",{"type":41,"tag":123,"props":532,"children":533},{},[534],{"type":46,"value":535},"Experiments to launch (test hypotheses about what could improve a metric)",{"type":41,"tag":123,"props":537,"children":538},{},[539],{"type":46,"value":540},"Investments to make (double down on what is working)",{"type":41,"tag":123,"props":542,"children":543},{},[544],{"type":46,"value":545},"Alerts to set (monitor a metric more closely)",{"type":41,"tag":306,"props":547,"children":549},{"id":548},"context-and-caveats",[550],{"type":46,"value":551},"Context and Caveats",{"type":41,"tag":119,"props":553,"children":554},{},[555,560,565],{"type":41,"tag":123,"props":556,"children":557},{},[558],{"type":46,"value":559},"Known data quality issues",{"type":41,"tag":123,"props":561,"children":562},{},[563],{"type":46,"value":564},"Events that affect comparability (outages, holidays, launches)",{"type":41,"tag":123,"props":566,"children":567},{},[568],{"type":46,"value":569},"Metrics we should be tracking but are not yet",{"type":41,"tag":99,"props":571,"children":573},{"id":572},"_5-follow-up",[574],{"type":46,"value":575},"5. Follow Up",{"type":41,"tag":53,"props":577,"children":578},{},[579],{"type":46,"value":580},"After generating the review:",{"type":41,"tag":119,"props":582,"children":583},{},[584,589,594,599],{"type":41,"tag":123,"props":585,"children":586},{},[587],{"type":46,"value":588},"Ask if any metric needs deeper investigation",{"type":41,"tag":123,"props":590,"children":591},{},[592],{"type":46,"value":593},"Offer to create a dashboard spec for ongoing monitoring",{"type":41,"tag":123,"props":595,"children":596},{},[597],{"type":46,"value":598},"Offer to draft experiment proposals for areas of concern",{"type":41,"tag":123,"props":600,"children":601},{},[602],{"type":46,"value":603},"Offer to set up a metrics review template for recurring use",{"type":41,"tag":73,"props":605,"children":607},{"id":606},"product-metrics-hierarchy",[608],{"type":46,"value":204},{"type":41,"tag":99,"props":610,"children":612},{"id":611},"north-star-metric",[613],{"type":46,"value":614},"North Star Metric",{"type":41,"tag":53,"props":616,"children":617},{},[618],{"type":46,"value":619},"The single metric that best captures the core value your product delivers to users. It should be:",{"type":41,"tag":119,"props":621,"children":622},{},[623,633,643,653],{"type":41,"tag":123,"props":624,"children":625},{},[626,631],{"type":41,"tag":111,"props":627,"children":628},{},[629],{"type":46,"value":630},"Value-aligned",{"type":46,"value":632},": Moves when users get more value from the product",{"type":41,"tag":123,"props":634,"children":635},{},[636,641],{"type":41,"tag":111,"props":637,"children":638},{},[639],{"type":46,"value":640},"Leading",{"type":46,"value":642},": Predicts long-term business success (revenue, retention)",{"type":41,"tag":123,"props":644,"children":645},{},[646,651],{"type":41,"tag":111,"props":647,"children":648},{},[649],{"type":46,"value":650},"Actionable",{"type":46,"value":652},": The product team can influence it through their work",{"type":41,"tag":123,"props":654,"children":655},{},[656,661],{"type":41,"tag":111,"props":657,"children":658},{},[659],{"type":46,"value":660},"Understandable",{"type":46,"value":662},": Everyone in the company can understand what it means and why it matters",{"type":41,"tag":53,"props":664,"children":665},{},[666,671],{"type":41,"tag":111,"props":667,"children":668},{},[669],{"type":46,"value":670},"Examples by product type",{"type":46,"value":672},":",{"type":41,"tag":119,"props":674,"children":675},{},[676,681,686,691,696],{"type":41,"tag":123,"props":677,"children":678},{},[679],{"type":46,"value":680},"Collaboration tool: Weekly active teams with 3+ members contributing",{"type":41,"tag":123,"props":682,"children":683},{},[684],{"type":46,"value":685},"Marketplace: Weekly transactions completed",{"type":41,"tag":123,"props":687,"children":688},{},[689],{"type":46,"value":690},"SaaS platform: Weekly active users completing core workflow",{"type":41,"tag":123,"props":692,"children":693},{},[694],{"type":46,"value":695},"Content platform: Weekly engaged reading\u002Fviewing time",{"type":41,"tag":123,"props":697,"children":698},{},[699],{"type":46,"value":700},"Developer tool: Weekly deployments using the tool",{"type":41,"tag":99,"props":702,"children":704},{"id":703},"l1-metrics-health-indicators",[705],{"type":46,"value":706},"L1 Metrics (Health Indicators)",{"type":41,"tag":53,"props":708,"children":709},{},[710],{"type":46,"value":711},"The 5-7 metrics that together paint a complete picture of product health. These map to the key stages of the user lifecycle:",{"type":41,"tag":53,"props":713,"children":714},{},[715,720],{"type":41,"tag":111,"props":716,"children":717},{},[718],{"type":46,"value":719},"Acquisition",{"type":46,"value":721},": Are new users finding the product?",{"type":41,"tag":119,"props":723,"children":724},{},[725,730,735,740],{"type":41,"tag":123,"props":726,"children":727},{},[728],{"type":46,"value":729},"New signups or trial starts (volume and trend)",{"type":41,"tag":123,"props":731,"children":732},{},[733],{"type":46,"value":734},"Signup conversion rate (visitors to signups)",{"type":41,"tag":123,"props":736,"children":737},{},[738],{"type":46,"value":739},"Channel mix (where are new users coming from)",{"type":41,"tag":123,"props":741,"children":742},{},[743],{"type":46,"value":744},"Cost per acquisition (for paid channels)",{"type":41,"tag":53,"props":746,"children":747},{},[748,753],{"type":41,"tag":111,"props":749,"children":750},{},[751],{"type":46,"value":752},"Activation",{"type":46,"value":754},": Are new users reaching the value moment?",{"type":41,"tag":119,"props":756,"children":757},{},[758,763,768,773],{"type":41,"tag":123,"props":759,"children":760},{},[761],{"type":46,"value":762},"Activation rate: % of new users who complete the key action that predicts retention",{"type":41,"tag":123,"props":764,"children":765},{},[766],{"type":46,"value":767},"Time to activate: how long from signup to activation",{"type":41,"tag":123,"props":769,"children":770},{},[771],{"type":46,"value":772},"Setup completion rate: % who complete onboarding steps",{"type":41,"tag":123,"props":774,"children":775},{},[776],{"type":46,"value":777},"First value moment: when users first experience the core product value",{"type":41,"tag":53,"props":779,"children":780},{},[781,786],{"type":41,"tag":111,"props":782,"children":783},{},[784],{"type":46,"value":785},"Engagement",{"type":46,"value":787},": Are active users getting value?",{"type":41,"tag":119,"props":789,"children":790},{},[791,796,801,806,811],{"type":41,"tag":123,"props":792,"children":793},{},[794],{"type":46,"value":795},"DAU \u002F WAU \u002F MAU: active users at different timeframes",{"type":41,"tag":123,"props":797,"children":798},{},[799],{"type":46,"value":800},"DAU\u002FMAU ratio (stickiness): what fraction of monthly users come back daily",{"type":41,"tag":123,"props":802,"children":803},{},[804],{"type":46,"value":805},"Core action frequency: how often users do the thing that matters most",{"type":41,"tag":123,"props":807,"children":808},{},[809],{"type":46,"value":810},"Session depth: how much users do per session",{"type":41,"tag":123,"props":812,"children":813},{},[814],{"type":46,"value":815},"Feature adoption: % of users using key features",{"type":41,"tag":53,"props":817,"children":818},{},[819,824],{"type":41,"tag":111,"props":820,"children":821},{},[822],{"type":46,"value":823},"Retention",{"type":46,"value":825},": Are users coming back?",{"type":41,"tag":119,"props":827,"children":828},{},[829,834,839,844],{"type":41,"tag":123,"props":830,"children":831},{},[832],{"type":46,"value":833},"D1, D7, D30 retention: % of users who return after 1 day, 7 days, 30 days",{"type":41,"tag":123,"props":835,"children":836},{},[837],{"type":46,"value":838},"Cohort retention curves: how retention evolves for each signup cohort",{"type":41,"tag":123,"props":840,"children":841},{},[842],{"type":46,"value":843},"Churn rate: % of users or revenue lost per period",{"type":41,"tag":123,"props":845,"children":846},{},[847],{"type":46,"value":848},"Resurrection rate: % of churned users who come back",{"type":41,"tag":53,"props":850,"children":851},{},[852,857],{"type":41,"tag":111,"props":853,"children":854},{},[855],{"type":46,"value":856},"Monetization",{"type":46,"value":858},": Is value translating to revenue?",{"type":41,"tag":119,"props":860,"children":861},{},[862,867,872,877,882],{"type":41,"tag":123,"props":863,"children":864},{},[865],{"type":46,"value":866},"Conversion rate: free to paid (for freemium)",{"type":41,"tag":123,"props":868,"children":869},{},[870],{"type":46,"value":871},"MRR \u002F ARR: monthly or annual recurring revenue",{"type":41,"tag":123,"props":873,"children":874},{},[875],{"type":46,"value":876},"ARPU \u002F ARPA: average revenue per user or account",{"type":41,"tag":123,"props":878,"children":879},{},[880],{"type":46,"value":881},"Expansion revenue: revenue growth from existing customers",{"type":41,"tag":123,"props":883,"children":884},{},[885],{"type":46,"value":886},"Net revenue retention: revenue retention including expansion and contraction",{"type":41,"tag":53,"props":888,"children":889},{},[890,895],{"type":41,"tag":111,"props":891,"children":892},{},[893],{"type":46,"value":894},"Satisfaction",{"type":46,"value":896},": How do users feel about the product?",{"type":41,"tag":119,"props":898,"children":899},{},[900,905,910,915],{"type":41,"tag":123,"props":901,"children":902},{},[903],{"type":46,"value":904},"NPS: Net Promoter Score",{"type":41,"tag":123,"props":906,"children":907},{},[908],{"type":46,"value":909},"CSAT: Customer Satisfaction Score",{"type":41,"tag":123,"props":911,"children":912},{},[913],{"type":46,"value":914},"Support ticket volume and resolution time",{"type":41,"tag":123,"props":916,"children":917},{},[918],{"type":46,"value":919},"App store ratings and review sentiment",{"type":41,"tag":99,"props":921,"children":923},{"id":922},"l2-metrics-diagnostic",[924],{"type":46,"value":925},"L2 Metrics (Diagnostic)",{"type":41,"tag":53,"props":927,"children":928},{},[929],{"type":46,"value":930},"Detailed metrics used to investigate changes in L1 metrics:",{"type":41,"tag":119,"props":932,"children":933},{},[934,939,944,949,954],{"type":41,"tag":123,"props":935,"children":936},{},[937],{"type":46,"value":938},"Funnel conversion at each step",{"type":41,"tag":123,"props":940,"children":941},{},[942],{"type":46,"value":943},"Feature-level usage and adoption",{"type":41,"tag":123,"props":945,"children":946},{},[947],{"type":46,"value":948},"Segment-specific breakdowns (by plan, company size, geography, user role)",{"type":41,"tag":123,"props":950,"children":951},{},[952],{"type":46,"value":953},"Performance metrics (page load time, error rate, API latency)",{"type":41,"tag":123,"props":955,"children":956},{},[957],{"type":46,"value":958},"Content-specific engagement (which features, pages, or content types drive engagement)",{"type":41,"tag":73,"props":960,"children":962},{"id":961},"common-product-metrics",[963],{"type":46,"value":964},"Common Product Metrics",{"type":41,"tag":99,"props":966,"children":968},{"id":967},"dau-wau-mau",[969],{"type":46,"value":970},"DAU \u002F WAU \u002F MAU",{"type":41,"tag":53,"props":972,"children":973},{},[974,979],{"type":41,"tag":111,"props":975,"children":976},{},[977],{"type":46,"value":978},"What they measure",{"type":46,"value":980},": Unique users who perform a qualifying action in a day, week, or month.",{"type":41,"tag":53,"props":982,"children":983},{},[984,989],{"type":41,"tag":111,"props":985,"children":986},{},[987],{"type":46,"value":988},"Key decisions",{"type":46,"value":672},{"type":41,"tag":119,"props":991,"children":992},{},[993,998],{"type":41,"tag":123,"props":994,"children":995},{},[996],{"type":46,"value":997},"What counts as \"active\"? A login? A page view? A core action? Define this carefully — different definitions tell different stories.",{"type":41,"tag":123,"props":999,"children":1000},{},[1001],{"type":46,"value":1002},"Which timeframe matters most? DAU for daily-use products (messaging, email). WAU for weekly-use products (project management). MAU for less frequent products (tax software, travel booking).",{"type":41,"tag":53,"props":1004,"children":1005},{},[1006,1011],{"type":41,"tag":111,"props":1007,"children":1008},{},[1009],{"type":46,"value":1010},"How to use them",{"type":46,"value":672},{"type":41,"tag":119,"props":1013,"children":1014},{},[1015,1020,1025],{"type":41,"tag":123,"props":1016,"children":1017},{},[1018],{"type":46,"value":1019},"DAU\u002FMAU ratio (stickiness): values above 0.5 indicate a daily habit. Below 0.2 suggests infrequent usage.",{"type":41,"tag":123,"props":1021,"children":1022},{},[1023],{"type":46,"value":1024},"Trend matters more than absolute number. Is active usage growing, flat, or declining?",{"type":41,"tag":123,"props":1026,"children":1027},{},[1028],{"type":46,"value":1029},"Segment by user type. Power users and casual users behave very differently.",{"type":41,"tag":99,"props":1031,"children":1033},{"id":1032},"retention",[1034],{"type":46,"value":823},{"type":41,"tag":53,"props":1036,"children":1037},{},[1038,1043],{"type":41,"tag":111,"props":1039,"children":1040},{},[1041],{"type":46,"value":1042},"What it measures",{"type":46,"value":1044},": Of users who started in period X, what % are still active in period Y?",{"type":41,"tag":53,"props":1046,"children":1047},{},[1048,1053],{"type":41,"tag":111,"props":1049,"children":1050},{},[1051],{"type":46,"value":1052},"Common retention timeframes",{"type":46,"value":672},{"type":41,"tag":119,"props":1055,"children":1056},{},[1057,1062,1067,1072],{"type":41,"tag":123,"props":1058,"children":1059},{},[1060],{"type":46,"value":1061},"D1 (next day): Was the first experience good enough to come back?",{"type":41,"tag":123,"props":1063,"children":1064},{},[1065],{"type":46,"value":1066},"D7 (one week): Did the user establish a habit?",{"type":41,"tag":123,"props":1068,"children":1069},{},[1070],{"type":46,"value":1071},"D30 (one month): Is the user retained long-term?",{"type":41,"tag":123,"props":1073,"children":1074},{},[1075],{"type":46,"value":1076},"D90 (three months): Is this a durable user?",{"type":41,"tag":53,"props":1078,"children":1079},{},[1080,1085],{"type":41,"tag":111,"props":1081,"children":1082},{},[1083],{"type":46,"value":1084},"How to use retention",{"type":46,"value":672},{"type":41,"tag":119,"props":1087,"children":1088},{},[1089,1094,1099],{"type":41,"tag":123,"props":1090,"children":1091},{},[1092],{"type":46,"value":1093},"Plot retention curves by cohort. Look for: initial drop-off (activation problem), steady decline (engagement problem), or flattening (good — you have a stable retained base).",{"type":41,"tag":123,"props":1095,"children":1096},{},[1097],{"type":46,"value":1098},"Compare cohorts over time. Are newer cohorts retaining better than older ones? That means product improvements are working.",{"type":41,"tag":123,"props":1100,"children":1101},{},[1102],{"type":46,"value":1103},"Segment retention by activation behavior. Users who completed onboarding vs those who did not. Users who used feature X vs those who did not.",{"type":41,"tag":99,"props":1105,"children":1107},{"id":1106},"conversion",[1108],{"type":46,"value":1109},"Conversion",{"type":41,"tag":53,"props":1111,"children":1112},{},[1113,1117],{"type":41,"tag":111,"props":1114,"children":1115},{},[1116],{"type":46,"value":1042},{"type":46,"value":1118},": % of users who move from one stage to the next.",{"type":41,"tag":53,"props":1120,"children":1121},{},[1122,1127],{"type":41,"tag":111,"props":1123,"children":1124},{},[1125],{"type":46,"value":1126},"Common conversion funnels",{"type":46,"value":672},{"type":41,"tag":119,"props":1129,"children":1130},{},[1131,1136,1141,1146,1151],{"type":41,"tag":123,"props":1132,"children":1133},{},[1134],{"type":46,"value":1135},"Visitor to signup",{"type":41,"tag":123,"props":1137,"children":1138},{},[1139],{"type":46,"value":1140},"Signup to activation (key value moment)",{"type":41,"tag":123,"props":1142,"children":1143},{},[1144],{"type":46,"value":1145},"Free to paid (trial conversion)",{"type":41,"tag":123,"props":1147,"children":1148},{},[1149],{"type":46,"value":1150},"Trial to paid subscription",{"type":41,"tag":123,"props":1152,"children":1153},{},[1154],{"type":46,"value":1155},"Monthly to annual plan",{"type":41,"tag":53,"props":1157,"children":1158},{},[1159,1164],{"type":41,"tag":111,"props":1160,"children":1161},{},[1162],{"type":46,"value":1163},"How to use conversion",{"type":46,"value":672},{"type":41,"tag":119,"props":1166,"children":1167},{},[1168,1173,1178,1183],{"type":41,"tag":123,"props":1169,"children":1170},{},[1171],{"type":46,"value":1172},"Map the full funnel and measure conversion at each step",{"type":41,"tag":123,"props":1174,"children":1175},{},[1176],{"type":46,"value":1177},"Identify the biggest drop-off points — these are your highest-leverage improvement opportunities",{"type":41,"tag":123,"props":1179,"children":1180},{},[1181],{"type":46,"value":1182},"Segment conversion by source, plan, user type. Different segments convert very differently.",{"type":41,"tag":123,"props":1184,"children":1185},{},[1186],{"type":46,"value":1187},"Track conversion over time. Is it improving as you iterate on the experience?",{"type":41,"tag":99,"props":1189,"children":1191},{"id":1190},"activation",[1192],{"type":46,"value":752},{"type":41,"tag":53,"props":1194,"children":1195},{},[1196,1200],{"type":41,"tag":111,"props":1197,"children":1198},{},[1199],{"type":46,"value":1042},{"type":46,"value":1201},": % of new users who reach the moment where they first experience the product's core value.",{"type":41,"tag":53,"props":1203,"children":1204},{},[1205,1210],{"type":41,"tag":111,"props":1206,"children":1207},{},[1208],{"type":46,"value":1209},"Defining activation",{"type":46,"value":672},{"type":41,"tag":119,"props":1212,"children":1213},{},[1214,1219,1224,1229],{"type":41,"tag":123,"props":1215,"children":1216},{},[1217],{"type":46,"value":1218},"Look at retained users vs churned users. What actions did retained users take that churned users did not?",{"type":41,"tag":123,"props":1220,"children":1221},{},[1222],{"type":46,"value":1223},"The activation event should be strongly predictive of long-term retention",{"type":41,"tag":123,"props":1225,"children":1226},{},[1227],{"type":46,"value":1228},"It should be achievable within the first session or first few days",{"type":41,"tag":123,"props":1230,"children":1231},{},[1232],{"type":46,"value":1233},"Examples: created first project, invited a teammate, completed first workflow, connected an integration",{"type":41,"tag":53,"props":1235,"children":1236},{},[1237,1242],{"type":41,"tag":111,"props":1238,"children":1239},{},[1240],{"type":46,"value":1241},"How to use activation",{"type":46,"value":672},{"type":41,"tag":119,"props":1244,"children":1245},{},[1246,1251,1256,1261],{"type":41,"tag":123,"props":1247,"children":1248},{},[1249],{"type":46,"value":1250},"Track activation rate for every signup cohort",{"type":41,"tag":123,"props":1252,"children":1253},{},[1254],{"type":46,"value":1255},"Measure time to activate — faster is almost always better",{"type":41,"tag":123,"props":1257,"children":1258},{},[1259],{"type":46,"value":1260},"Build onboarding flows that guide users to the activation moment",{"type":41,"tag":123,"props":1262,"children":1263},{},[1264],{"type":46,"value":1265},"A\u002FB test activation flows and measure impact on retention, not just activation rate",{"type":41,"tag":73,"props":1267,"children":1269},{"id":1268},"goal-setting-frameworks",[1270],{"type":46,"value":1271},"Goal Setting Frameworks",{"type":41,"tag":99,"props":1273,"children":1275},{"id":1274},"okrs-objectives-and-key-results",[1276],{"type":46,"value":1277},"OKRs (Objectives and Key Results)",{"type":41,"tag":53,"props":1279,"children":1280},{},[1281,1286],{"type":41,"tag":111,"props":1282,"children":1283},{},[1284],{"type":46,"value":1285},"Objectives",{"type":46,"value":1287},": Qualitative, aspirational goals that describe what you want to achieve.",{"type":41,"tag":119,"props":1289,"children":1290},{},[1291,1296,1301],{"type":41,"tag":123,"props":1292,"children":1293},{},[1294],{"type":46,"value":1295},"Inspiring and memorable",{"type":41,"tag":123,"props":1297,"children":1298},{},[1299],{"type":46,"value":1300},"Time-bound (quarterly or annually)",{"type":41,"tag":123,"props":1302,"children":1303},{},[1304],{"type":46,"value":1305},"Directional, not metric-specific",{"type":41,"tag":53,"props":1307,"children":1308},{},[1309,1314],{"type":41,"tag":111,"props":1310,"children":1311},{},[1312],{"type":46,"value":1313},"Key Results",{"type":46,"value":1315},": Quantitative measures that tell you if you achieved the objective.",{"type":41,"tag":119,"props":1317,"children":1318},{},[1319,1324,1329,1334],{"type":41,"tag":123,"props":1320,"children":1321},{},[1322],{"type":46,"value":1323},"Specific and measurable",{"type":41,"tag":123,"props":1325,"children":1326},{},[1327],{"type":46,"value":1328},"Time-bound with a clear target",{"type":41,"tag":123,"props":1330,"children":1331},{},[1332],{"type":46,"value":1333},"Outcome-based, not output-based",{"type":41,"tag":123,"props":1335,"children":1336},{},[1337],{"type":46,"value":1338},"2-4 Key Results per Objective",{"type":41,"tag":53,"props":1340,"children":1341},{},[1342,1347],{"type":41,"tag":111,"props":1343,"children":1344},{},[1345],{"type":46,"value":1346},"Example",{"type":46,"value":672},{"type":41,"tag":80,"props":1349,"children":1352},{"className":1350,"code":1351,"language":46},[83],"Objective: Make our product indispensable for daily workflows\n\nKey Results:\n- Increase DAU\u002FMAU ratio from 0.35 to 0.50\n- Increase D30 retention for new users from 40% to 55%\n- 3 core workflows with >80% task completion rate\n",[1353],{"type":41,"tag":87,"props":1354,"children":1355},{"__ignoreMap":89},[1356],{"type":46,"value":1351},{"type":41,"tag":99,"props":1358,"children":1360},{"id":1359},"okr-best-practices",[1361],{"type":46,"value":1362},"OKR Best Practices",{"type":41,"tag":119,"props":1364,"children":1365},{},[1366,1371,1376,1381,1386,1391],{"type":41,"tag":123,"props":1367,"children":1368},{},[1369],{"type":46,"value":1370},"Set OKRs that are ambitious but achievable. 70% completion is the target for stretch OKRs.",{"type":41,"tag":123,"props":1372,"children":1373},{},[1374],{"type":46,"value":1375},"Key Results should measure outcomes (user behavior, business results), not outputs (features shipped, tasks completed).",{"type":41,"tag":123,"props":1377,"children":1378},{},[1379],{"type":46,"value":1380},"Do not have too many OKRs. 2-3 objectives with 2-4 KRs each is plenty.",{"type":41,"tag":123,"props":1382,"children":1383},{},[1384],{"type":46,"value":1385},"OKRs should be uncomfortable. If you are confident you will hit all of them, they are not ambitious enough.",{"type":41,"tag":123,"props":1387,"children":1388},{},[1389],{"type":46,"value":1390},"Review OKRs at mid-period. Adjust effort allocation if some KRs are clearly off track.",{"type":41,"tag":123,"props":1392,"children":1393},{},[1394],{"type":46,"value":1395},"Grade OKRs honestly at end of period. 0.0-0.3 = missed, 0.4-0.6 = progress, 0.7-1.0 = achieved.",{"type":41,"tag":99,"props":1397,"children":1399},{"id":1398},"setting-metric-targets",[1400],{"type":46,"value":1401},"Setting Metric Targets",{"type":41,"tag":119,"props":1403,"children":1404},{},[1405,1415,1425,1435,1445],{"type":41,"tag":123,"props":1406,"children":1407},{},[1408,1413],{"type":41,"tag":111,"props":1409,"children":1410},{},[1411],{"type":46,"value":1412},"Baseline",{"type":46,"value":1414},": What is the current value? You need a reliable baseline before setting a target.",{"type":41,"tag":123,"props":1416,"children":1417},{},[1418,1423],{"type":41,"tag":111,"props":1419,"children":1420},{},[1421],{"type":46,"value":1422},"Benchmark",{"type":46,"value":1424},": What do comparable products achieve? Industry benchmarks provide context.",{"type":41,"tag":123,"props":1426,"children":1427},{},[1428,1433],{"type":41,"tag":111,"props":1429,"children":1430},{},[1431],{"type":46,"value":1432},"Trajectory",{"type":46,"value":1434},": What is the current trend? If the metric is already improving at 5% per month, a 6% target is not ambitious.",{"type":41,"tag":123,"props":1436,"children":1437},{},[1438,1443],{"type":41,"tag":111,"props":1439,"children":1440},{},[1441],{"type":46,"value":1442},"Effort",{"type":46,"value":1444},": How much investment are you putting behind this? Bigger bets warrant more ambitious targets.",{"type":41,"tag":123,"props":1446,"children":1447},{},[1448,1453],{"type":41,"tag":111,"props":1449,"children":1450},{},[1451],{"type":46,"value":1452},"Confidence",{"type":46,"value":1454},": How confident are you in hitting the target? Set a \"commit\" (high confidence) and a \"stretch\" (ambitious).",{"type":41,"tag":73,"props":1456,"children":1458},{"id":1457},"metric-review-cadences",[1459],{"type":46,"value":1460},"Metric Review Cadences",{"type":41,"tag":99,"props":1462,"children":1464},{"id":1463},"weekly-metrics-check",[1465],{"type":46,"value":1466},"Weekly Metrics Check",{"type":41,"tag":53,"props":1468,"children":1469},{},[1470,1475,1477,1482,1484,1489],{"type":41,"tag":111,"props":1471,"children":1472},{},[1473],{"type":46,"value":1474},"Purpose",{"type":46,"value":1476},": Catch issues quickly, monitor experiments, stay in touch with product health.\n",{"type":41,"tag":111,"props":1478,"children":1479},{},[1480],{"type":46,"value":1481},"Duration",{"type":46,"value":1483},": 15-30 minutes.\n",{"type":41,"tag":111,"props":1485,"children":1486},{},[1487],{"type":46,"value":1488},"Attendees",{"type":46,"value":1490},": Product manager, maybe engineering lead.",{"type":41,"tag":53,"props":1492,"children":1493},{},[1494,1499],{"type":41,"tag":111,"props":1495,"children":1496},{},[1497],{"type":46,"value":1498},"What to review",{"type":46,"value":672},{"type":41,"tag":119,"props":1501,"children":1502},{},[1503,1508,1513,1518,1523],{"type":41,"tag":123,"props":1504,"children":1505},{},[1506],{"type":46,"value":1507},"North Star metric: current value, week-over-week change",{"type":41,"tag":123,"props":1509,"children":1510},{},[1511],{"type":46,"value":1512},"Key L1 metrics: any notable movements",{"type":41,"tag":123,"props":1514,"children":1515},{},[1516],{"type":46,"value":1517},"Active experiments: results and statistical significance",{"type":41,"tag":123,"props":1519,"children":1520},{},[1521],{"type":46,"value":1522},"Anomalies: any unexpected spikes or drops",{"type":41,"tag":123,"props":1524,"children":1525},{},[1526],{"type":46,"value":1527},"Alerts: anything that triggered a monitoring alert",{"type":41,"tag":53,"props":1529,"children":1530},{},[1531,1536],{"type":41,"tag":111,"props":1532,"children":1533},{},[1534],{"type":46,"value":1535},"Action",{"type":46,"value":1537},": If something looks off, investigate. Otherwise, note it and move on.",{"type":41,"tag":99,"props":1539,"children":1541},{"id":1540},"monthly-metrics-review",[1542],{"type":46,"value":1543},"Monthly Metrics Review",{"type":41,"tag":53,"props":1545,"children":1546},{},[1547,1551,1553,1557,1559,1563],{"type":41,"tag":111,"props":1548,"children":1549},{},[1550],{"type":46,"value":1474},{"type":46,"value":1552},": Deeper analysis of trends, progress against goals, strategic implications.\n",{"type":41,"tag":111,"props":1554,"children":1555},{},[1556],{"type":46,"value":1481},{"type":46,"value":1558},": 30-60 minutes.\n",{"type":41,"tag":111,"props":1560,"children":1561},{},[1562],{"type":46,"value":1488},{"type":46,"value":1564},": Product team, key stakeholders.",{"type":41,"tag":53,"props":1566,"children":1567},{},[1568,1572],{"type":41,"tag":111,"props":1569,"children":1570},{},[1571],{"type":46,"value":1498},{"type":46,"value":672},{"type":41,"tag":119,"props":1574,"children":1575},{},[1576,1581,1586,1591,1596],{"type":41,"tag":123,"props":1577,"children":1578},{},[1579],{"type":46,"value":1580},"Full L1 metric scorecard with month-over-month trends",{"type":41,"tag":123,"props":1582,"children":1583},{},[1584],{"type":46,"value":1585},"Progress against quarterly OKR targets",{"type":41,"tag":123,"props":1587,"children":1588},{},[1589],{"type":46,"value":1590},"Cohort analysis: are newer cohorts performing better?",{"type":41,"tag":123,"props":1592,"children":1593},{},[1594],{"type":46,"value":1595},"Feature adoption: how are recent launches performing?",{"type":41,"tag":123,"props":1597,"children":1598},{},[1599],{"type":46,"value":1600},"Segment analysis: any divergence between user segments?",{"type":41,"tag":53,"props":1602,"children":1603},{},[1604,1608],{"type":41,"tag":111,"props":1605,"children":1606},{},[1607],{"type":46,"value":1535},{"type":46,"value":1609},": Identify 1-3 areas to investigate or invest in. Update priorities if metrics reveal new information.",{"type":41,"tag":99,"props":1611,"children":1613},{"id":1612},"quarterly-business-review",[1614],{"type":46,"value":1615},"Quarterly Business Review",{"type":41,"tag":53,"props":1617,"children":1618},{},[1619,1623,1625,1629,1631,1635],{"type":41,"tag":111,"props":1620,"children":1621},{},[1622],{"type":46,"value":1474},{"type":46,"value":1624},": Strategic assessment of product performance, goal-setting for next quarter.\n",{"type":41,"tag":111,"props":1626,"children":1627},{},[1628],{"type":46,"value":1481},{"type":46,"value":1630},": 60-90 minutes.\n",{"type":41,"tag":111,"props":1632,"children":1633},{},[1634],{"type":46,"value":1488},{"type":46,"value":1636},": Product, engineering, design, leadership.",{"type":41,"tag":53,"props":1638,"children":1639},{},[1640,1644],{"type":41,"tag":111,"props":1641,"children":1642},{},[1643],{"type":46,"value":1498},{"type":46,"value":672},{"type":41,"tag":119,"props":1646,"children":1647},{},[1648,1653,1658,1663,1668],{"type":41,"tag":123,"props":1649,"children":1650},{},[1651],{"type":46,"value":1652},"OKR scoring for the quarter",{"type":41,"tag":123,"props":1654,"children":1655},{},[1656],{"type":46,"value":1657},"Trend analysis for all L1 metrics over the quarter",{"type":41,"tag":123,"props":1659,"children":1660},{},[1661],{"type":46,"value":1662},"Year-over-year comparisons",{"type":41,"tag":123,"props":1664,"children":1665},{},[1666],{"type":46,"value":1667},"Competitive context: market changes and competitor movements",{"type":41,"tag":123,"props":1669,"children":1670},{},[1671],{"type":46,"value":1672},"What worked and what did not",{"type":41,"tag":53,"props":1674,"children":1675},{},[1676,1680],{"type":41,"tag":111,"props":1677,"children":1678},{},[1679],{"type":46,"value":1535},{"type":46,"value":1681},": Set OKRs for next quarter. Adjust product strategy based on what the data shows.",{"type":41,"tag":73,"props":1683,"children":1685},{"id":1684},"dashboard-design-principles",[1686],{"type":46,"value":1687},"Dashboard Design Principles",{"type":41,"tag":99,"props":1689,"children":1691},{"id":1690},"effective-product-dashboards",[1692],{"type":46,"value":1693},"Effective Product Dashboards",{"type":41,"tag":53,"props":1695,"children":1696},{},[1697],{"type":46,"value":1698},"A good dashboard answers the question \"How is the product doing?\" at a glance.",{"type":41,"tag":53,"props":1700,"children":1701},{},[1702,1707],{"type":41,"tag":111,"props":1703,"children":1704},{},[1705],{"type":46,"value":1706},"Principles",{"type":46,"value":672},{"type":41,"tag":1709,"props":1710,"children":1711},"ol",{},[1712,1722,1732,1742,1752,1762,1790],{"type":41,"tag":123,"props":1713,"children":1714},{},[1715,1720],{"type":41,"tag":111,"props":1716,"children":1717},{},[1718],{"type":46,"value":1719},"Start with the question, not the data",{"type":46,"value":1721},". What decisions does this dashboard support? Design backwards from the decision.",{"type":41,"tag":123,"props":1723,"children":1724},{},[1725,1730],{"type":41,"tag":111,"props":1726,"children":1727},{},[1728],{"type":46,"value":1729},"Hierarchy of information",{"type":46,"value":1731},". The most important metric should be the most visually prominent. North Star at the top, L1 metrics next, L2 metrics available on drill-down.",{"type":41,"tag":123,"props":1733,"children":1734},{},[1735,1740],{"type":41,"tag":111,"props":1736,"children":1737},{},[1738],{"type":46,"value":1739},"Context over numbers",{"type":46,"value":1741},". A number without context is meaningless. Always show: current value, comparison (previous period, target, benchmark), trend direction.",{"type":41,"tag":123,"props":1743,"children":1744},{},[1745,1750],{"type":41,"tag":111,"props":1746,"children":1747},{},[1748],{"type":46,"value":1749},"Fewer metrics, more insight",{"type":46,"value":1751},". A dashboard with 50 metrics helps no one. Focus on 5-10 that matter. Put everything else in a detailed report.",{"type":41,"tag":123,"props":1753,"children":1754},{},[1755,1760],{"type":41,"tag":111,"props":1756,"children":1757},{},[1758],{"type":46,"value":1759},"Consistent time periods",{"type":46,"value":1761},". Use the same time period for all metrics on a dashboard. Mixing daily and monthly metrics creates confusion.",{"type":41,"tag":123,"props":1763,"children":1764},{},[1765,1770,1772],{"type":41,"tag":111,"props":1766,"children":1767},{},[1768],{"type":46,"value":1769},"Visual status indicators",{"type":46,"value":1771},". Use color to indicate health at a glance:",{"type":41,"tag":119,"props":1773,"children":1774},{},[1775,1780,1785],{"type":41,"tag":123,"props":1776,"children":1777},{},[1778],{"type":46,"value":1779},"Green: on track or improving",{"type":41,"tag":123,"props":1781,"children":1782},{},[1783],{"type":46,"value":1784},"Yellow: needs attention or flat",{"type":41,"tag":123,"props":1786,"children":1787},{},[1788],{"type":46,"value":1789},"Red: off track or declining",{"type":41,"tag":123,"props":1791,"children":1792},{},[1793,1798],{"type":41,"tag":111,"props":1794,"children":1795},{},[1796],{"type":46,"value":1797},"Actionability",{"type":46,"value":1799},". Every metric on the dashboard should be something the team can influence. If you cannot act on it, it does not belong on the product dashboard.",{"type":41,"tag":99,"props":1801,"children":1803},{"id":1802},"dashboard-layout",[1804],{"type":46,"value":1805},"Dashboard Layout",{"type":41,"tag":53,"props":1807,"children":1808},{},[1809,1814],{"type":41,"tag":111,"props":1810,"children":1811},{},[1812],{"type":46,"value":1813},"Top row",{"type":46,"value":1815},": North Star metric with trend line and target.",{"type":41,"tag":53,"props":1817,"children":1818},{},[1819,1824],{"type":41,"tag":111,"props":1820,"children":1821},{},[1822],{"type":46,"value":1823},"Second row",{"type":46,"value":1825},": L1 metrics scorecard — current value, change, target, status for each key metric.",{"type":41,"tag":53,"props":1827,"children":1828},{},[1829,1834],{"type":41,"tag":111,"props":1830,"children":1831},{},[1832],{"type":46,"value":1833},"Third row",{"type":46,"value":1835},": Key funnels or conversion metrics — visual funnel showing drop-off at each stage.",{"type":41,"tag":53,"props":1837,"children":1838},{},[1839,1844],{"type":41,"tag":111,"props":1840,"children":1841},{},[1842],{"type":46,"value":1843},"Fourth row",{"type":46,"value":1845},": Recent experiments and launches — active A\u002FB tests, recent feature launches with early metrics.",{"type":41,"tag":53,"props":1847,"children":1848},{},[1849,1854],{"type":41,"tag":111,"props":1850,"children":1851},{},[1852],{"type":46,"value":1853},"Bottom \u002F drill-down",{"type":46,"value":1855},": L2 metrics, segment breakdowns, and detailed time series for investigation.",{"type":41,"tag":99,"props":1857,"children":1859},{"id":1858},"dashboard-anti-patterns",[1860],{"type":46,"value":1861},"Dashboard Anti-Patterns",{"type":41,"tag":119,"props":1863,"children":1864},{},[1865,1875,1885,1895,1905,1915],{"type":41,"tag":123,"props":1866,"children":1867},{},[1868,1873],{"type":41,"tag":111,"props":1869,"children":1870},{},[1871],{"type":46,"value":1872},"Vanity metrics",{"type":46,"value":1874},": Metrics that always go up but do not indicate health (total signups ever, total page views)",{"type":41,"tag":123,"props":1876,"children":1877},{},[1878,1883],{"type":41,"tag":111,"props":1879,"children":1880},{},[1881],{"type":46,"value":1882},"Too many metrics",{"type":46,"value":1884},": Dashboards that require scrolling to see. If it does not fit on one screen, cut metrics.",{"type":41,"tag":123,"props":1886,"children":1887},{},[1888,1893],{"type":41,"tag":111,"props":1889,"children":1890},{},[1891],{"type":46,"value":1892},"No comparison",{"type":46,"value":1894},": Raw numbers without context (current value with no previous period or target)",{"type":41,"tag":123,"props":1896,"children":1897},{},[1898,1903],{"type":41,"tag":111,"props":1899,"children":1900},{},[1901],{"type":46,"value":1902},"Stale dashboards",{"type":46,"value":1904},": Metrics that have not been updated or reviewed in months",{"type":41,"tag":123,"props":1906,"children":1907},{},[1908,1913],{"type":41,"tag":111,"props":1909,"children":1910},{},[1911],{"type":46,"value":1912},"Output dashboards",{"type":46,"value":1914},": Measuring team activity (tickets closed, PRs merged) instead of user and business outcomes",{"type":41,"tag":123,"props":1916,"children":1917},{},[1918,1923],{"type":41,"tag":111,"props":1919,"children":1920},{},[1921],{"type":46,"value":1922},"One dashboard for all audiences",{"type":46,"value":1924},": Executives, PMs, and engineers need different views. One size does not fit all.",{"type":41,"tag":99,"props":1926,"children":1928},{"id":1927},"alerting",[1929],{"type":46,"value":1930},"Alerting",{"type":41,"tag":53,"props":1932,"children":1933},{},[1934],{"type":46,"value":1935},"Set alerts for metrics that require immediate attention:",{"type":41,"tag":119,"props":1937,"children":1938},{},[1939,1949,1959],{"type":41,"tag":123,"props":1940,"children":1941},{},[1942,1947],{"type":41,"tag":111,"props":1943,"children":1944},{},[1945],{"type":46,"value":1946},"Threshold alerts",{"type":46,"value":1948},": Metric drops below or rises above a critical threshold (error rate > 1%, conversion \u003C 5%)",{"type":41,"tag":123,"props":1950,"children":1951},{},[1952,1957],{"type":41,"tag":111,"props":1953,"children":1954},{},[1955],{"type":46,"value":1956},"Trend alerts",{"type":46,"value":1958},": Metric shows sustained decline over multiple days\u002Fweeks",{"type":41,"tag":123,"props":1960,"children":1961},{},[1962,1967],{"type":41,"tag":111,"props":1963,"children":1964},{},[1965],{"type":46,"value":1966},"Anomaly alerts",{"type":46,"value":1968},": Metric deviates significantly from expected range",{"type":41,"tag":53,"props":1970,"children":1971},{},[1972,1977],{"type":41,"tag":111,"props":1973,"children":1974},{},[1975],{"type":46,"value":1976},"Alert hygiene",{"type":46,"value":672},{"type":41,"tag":119,"props":1979,"children":1980},{},[1981,1986,1991,1996],{"type":41,"tag":123,"props":1982,"children":1983},{},[1984],{"type":46,"value":1985},"Every alert should be actionable. If you cannot do anything about it, do not alert on it.",{"type":41,"tag":123,"props":1987,"children":1988},{},[1989],{"type":46,"value":1990},"Review and tune alerts regularly. Too many false positives and people ignore all alerts.",{"type":41,"tag":123,"props":1992,"children":1993},{},[1994],{"type":46,"value":1995},"Define an owner for each alert. Who responds when it fires?",{"type":41,"tag":123,"props":1997,"children":1998},{},[1999],{"type":46,"value":2000},"Set appropriate severity levels. Not everything is P0.",{"type":41,"tag":73,"props":2002,"children":2004},{"id":2003},"output-format",[2005],{"type":46,"value":2006},"Output Format",{"type":41,"tag":53,"props":2008,"children":2009},{},[2010],{"type":46,"value":2011},"Use tables for the scorecard. Use clear status indicators. Keep the summary tight — the reader should get the essential story in 30 seconds.",{"type":41,"tag":73,"props":2013,"children":2015},{"id":2014},"tips",[2016],{"type":46,"value":2017},"Tips",{"type":41,"tag":119,"props":2019,"children":2020},{},[2021,2026,2031,2036,2041,2046,2051],{"type":41,"tag":123,"props":2022,"children":2023},{},[2024],{"type":46,"value":2025},"Start with the \"so what\" — what is the most important thing in this metrics review? Lead with that.",{"type":41,"tag":123,"props":2027,"children":2028},{},[2029],{"type":46,"value":2030},"Absolute numbers without context are useless. Always show comparisons (vs previous period, vs target, vs benchmark).",{"type":41,"tag":123,"props":2032,"children":2033},{},[2034],{"type":46,"value":2035},"Be careful about attribution. Correlation is not causation. If a metric moved, acknowledge uncertainty about why.",{"type":41,"tag":123,"props":2037,"children":2038},{},[2039],{"type":46,"value":2040},"Segment analysis often reveals that an aggregate metric masks important differences. A flat overall number might hide one segment growing and another shrinking.",{"type":41,"tag":123,"props":2042,"children":2043},{},[2044],{"type":46,"value":2045},"Not all metric movements matter. Small fluctuations are noise. Focus attention on meaningful changes.",{"type":41,"tag":123,"props":2047,"children":2048},{},[2049],{"type":46,"value":2050},"If a metric is missing its target, do not just report the miss — recommend what to do about it.",{"type":41,"tag":123,"props":2052,"children":2053},{},[2054],{"type":46,"value":2055},"Metrics reviews should drive decisions. If the review does not lead to at least one action, it was not useful.",{"items":2057,"total":2244},[2058,2079,2093,2105,2124,2137,2158,2178,2192,2207,2215,2228],{"slug":2059,"name":2059,"fn":2060,"description":2061,"org":2062,"tags":2063,"stars":2076,"repoUrl":2077,"updatedAt":2078},"algorithmic-art","create algorithmic art with p5.js","Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2064,2067,2070,2073],{"name":2065,"slug":2066,"type":16},"Creative","creative",{"name":2068,"slug":2069,"type":16},"Design","design",{"name":2071,"slug":2072,"type":16},"Generative Art","generative-art",{"name":2074,"slug":2075,"type":16},"JavaScript","javascript",161831,"https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fskills","2026-04-06T17:56:15.455818",{"slug":2080,"name":2080,"fn":2081,"description":2082,"org":2083,"tags":2084,"stars":2076,"repoUrl":2077,"updatedAt":2092},"brand-guidelines","apply Anthropic brand colors and typography","Applies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand colors or style guidelines, visual formatting, or company design standards apply.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2085,2088,2089],{"name":2086,"slug":2087,"type":16},"Branding","branding",{"name":2068,"slug":2069,"type":16},{"name":2090,"slug":2091,"type":16},"Typography","typography","2026-04-06T17:56:05.042852",{"slug":2094,"name":2094,"fn":2095,"description":2096,"org":2097,"tags":2098,"stars":2076,"repoUrl":2077,"updatedAt":2104},"canvas-design","create posters and visual art as PNG or PDF","Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2099,2100,2101],{"name":2065,"slug":2066,"type":16},{"name":2068,"slug":2069,"type":16},{"name":2102,"slug":2103,"type":16},"PDF","pdf","2026-04-06T17:56:03.794732",{"slug":2106,"name":2106,"fn":2107,"description":2108,"org":2109,"tags":2110,"stars":2076,"repoUrl":2077,"updatedAt":2123},"claude-api","build apps with the Claude API","Reference for the Claude API \u002F Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration.\nTRIGGER — read BEFORE opening the target file; don't skip because it \"looks like a one-liner\" — whenever: the prompt names Claude\u002FAnthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing\u002Fmodel choice\u002Flimits\u002Fcaching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent\u002FMCP\u002Ftool-definition\u002Fmulti-agent\u002FRAG\u002FLLM-judge\u002Fcomputer-use; generate\u002Fsummarize\u002Fextract\u002Fclassify\u002Frewrite\u002Fconverse over NL; debugging refusals\u002Fcutoffs\u002Fstreaming\u002Ftool-calls\u002Ftokens).\nSKIP only when another provider is being worked on (overrides all triggers): OpenAI\u002FGPT\u002FGemini\u002FLlama\u002FMistral\u002FCohere\u002FOllama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2111,2114,2115,2118,2120],{"name":2112,"slug":2113,"type":16},"Agents","agents",{"name":9,"slug":8,"type":16},{"name":2116,"slug":2117,"type":16},"Anthropic SDK","anthropic-sdk",{"name":2119,"slug":2106,"type":16},"Claude API",{"name":2121,"slug":2122,"type":16},"LLM","llm","2026-07-28T05:36:08.213335",{"slug":2125,"name":2125,"fn":2126,"description":2127,"org":2128,"tags":2129,"stars":2076,"repoUrl":2077,"updatedAt":2136},"doc-coauthoring","co-author documentation and technical specs","Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2130,2133],{"name":2131,"slug":2132,"type":16},"Documentation","documentation",{"name":2134,"slug":2135,"type":16},"Technical Writing","technical-writing","2026-04-06T17:56:14.18897",{"slug":2138,"name":2138,"fn":2139,"description":2140,"org":2141,"tags":2142,"stars":2076,"repoUrl":2077,"updatedAt":2157},"docx","create and edit Word documents","Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files) or Word templates (.dotx files). Triggers include: any mention of 'Word doc', 'word document', '.docx', '.dotx', or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx or .dotx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a 'report', 'memo', 'letter', 'template', or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2143,2146,2148,2151,2154],{"name":2144,"slug":2145,"type":16},"Documents","documents",{"name":2147,"slug":2138,"type":16},"DOCX",{"name":2149,"slug":2150,"type":16},"Office","office",{"name":2152,"slug":2153,"type":16},"Templates","templates",{"name":2155,"slug":2156,"type":16},"Word","word","2026-07-18T05:16:23.136271",{"slug":2159,"name":2159,"fn":2160,"description":2161,"org":2162,"tags":2163,"stars":2076,"repoUrl":2077,"updatedAt":2177},"frontend-design","design production-grade frontend interfaces","Guidance for distinctive, intentional visual design when building new UI or reshaping an existing one. Helps with aesthetic direction, typography, and making choices that don't read as templated defaults.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2164,2165,2168,2171,2174],{"name":2068,"slug":2069,"type":16},{"name":2166,"slug":2167,"type":16},"Frontend","frontend",{"name":2169,"slug":2170,"type":16},"React","react",{"name":2172,"slug":2173,"type":16},"Tailwind CSS","tailwind-css",{"name":2175,"slug":2176,"type":16},"UI Components","ui-components","2026-04-06T17:56:16.723469",{"slug":2179,"name":2179,"fn":2180,"description":2181,"org":2182,"tags":2183,"stars":2076,"repoUrl":2077,"updatedAt":2191},"internal-comms","write internal company communications","A set of resources to help me write all kinds of internal communications, using the formats that my company likes to use. Claude should use this skill whenever asked to write some sort of internal communications (status reports, leadership updates, 3P updates, company newsletters, FAQs, incident reports, project updates, etc.).",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2184,2187,2188],{"name":2185,"slug":2186,"type":16},"Communications","communications",{"name":2152,"slug":2153,"type":16},{"name":2189,"slug":2190,"type":16},"Writing","writing","2026-04-06T17:56:20.695522",{"slug":2193,"name":2193,"fn":2194,"description":2195,"org":2196,"tags":2197,"stars":2076,"repoUrl":2077,"updatedAt":2206},"mcp-builder","build MCP servers","Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node\u002FTypeScript (MCP SDK).",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2198,2199,2202,2203],{"name":2112,"slug":2113,"type":16},{"name":2200,"slug":2201,"type":16},"API Development","api-development",{"name":2121,"slug":2122,"type":16},{"name":2204,"slug":2205,"type":16},"MCP","mcp","2026-04-06T17:56:10.357665",{"slug":2103,"name":2103,"fn":2208,"description":2209,"org":2210,"tags":2211,"stars":2076,"repoUrl":2077,"updatedAt":2214},"read edit and manipulate PDF files","Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text\u002Ftables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting\u002Fdecrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this skill.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2212,2213],{"name":2144,"slug":2145,"type":16},{"name":2102,"slug":2103,"type":16},"2026-04-06T17:56:02.483316",{"slug":2216,"name":2216,"fn":2217,"description":2218,"org":2219,"tags":2220,"stars":2076,"repoUrl":2077,"updatedAt":2227},"pptx","create and edit PowerPoint presentations","Use this skill any time a .pptx or .potx file is involved in any way — as input, output, or both. This includes: creating slide decks, pitch decks, or presentations; reading, parsing, or extracting text from any .pptx or .potx file (even if the extracted content will be used elsewhere, like in an email or summary); editing, modifying, or updating existing presentations; combining or splitting slide files; working with templates (.potx), layouts, speaker notes, or comments. Trigger whenever the user mentions \"deck,\" \"slides,\" \"presentation,\" or references a .pptx or .potx filename, regardless of what they plan to do with the content afterward. If a .pptx or .potx file needs to be opened, created, or touched, use this skill.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2221,2224],{"name":2222,"slug":2223,"type":16},"PowerPoint","powerpoint",{"name":2225,"slug":2226,"type":16},"Presentations","presentations","2026-07-18T05:16:24.1471",{"slug":2229,"name":2229,"fn":2230,"description":2231,"org":2232,"tags":2233,"stars":2076,"repoUrl":2077,"updatedAt":2243},"skill-creator","create and optimize agent skills","Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2234,2235,2236,2239,2242],{"name":2112,"slug":2113,"type":16},{"name":2131,"slug":2132,"type":16},{"name":2237,"slug":2238,"type":16},"Evals","evals",{"name":2240,"slug":2241,"type":16},"Performance","performance",{"name":2134,"slug":2135,"type":16},"2026-04-19T06:45:40.804",490,{"items":2246,"total":2351},[2247,2261,2277,2291,2307,2326,2338],{"slug":2248,"name":2248,"fn":2249,"description":2250,"org":2251,"tags":2252,"stars":23,"repoUrl":24,"updatedAt":2260},"accessibility-review","run WCAG accessibility audits","Run a WCAG 2.1 AA accessibility audit on a design or page. Trigger with \"audit accessibility\", \"check a11y\", \"is this accessible?\", or when reviewing a design for color contrast, keyboard navigation, touch target size, or screen reader behavior before handoff.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2253,2256,2257],{"name":2254,"slug":2255,"type":16},"Accessibility","accessibility",{"name":2068,"slug":2069,"type":16},{"name":2258,"slug":2259,"type":16},"WCAG","wcag","2026-04-06T17:58:05.682394",{"slug":2262,"name":2262,"fn":2263,"description":2264,"org":2265,"tags":2266,"stars":23,"repoUrl":24,"updatedAt":2276},"account-research","research accounts for sales intel","Research a company or person and get actionable sales intel. Works standalone with web search, supercharged when you connect enrichment tools or your CRM. Trigger with \"research [company]\", \"look up [person]\", \"intel on [prospect]\", \"who is [name] at [company]\", or \"tell me about [company]\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2267,2270,2273],{"name":2268,"slug":2269,"type":16},"CRM","crm",{"name":2271,"slug":2272,"type":16},"Research","research",{"name":2274,"slug":2275,"type":16},"Sales","sales","2026-04-06T17:56:41.410418",{"slug":2278,"name":2278,"fn":2279,"description":2280,"org":2281,"tags":2282,"stars":23,"repoUrl":24,"updatedAt":2290},"analyze","answer data questions and run analyses","Answer data questions -- from quick lookups to full analyses. Use when looking up a single metric, investigating what's driving a trend or drop, comparing segments over time, or preparing a formal data report for stakeholders.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2283,2284,2287],{"name":21,"slug":22,"type":16},{"name":2285,"slug":2286,"type":16},"Data Analysis","data-analysis",{"name":2288,"slug":2289,"type":16},"SQL","sql","2026-04-06T17:57:21.593647",{"slug":2292,"name":2292,"fn":2293,"description":2294,"org":2295,"tags":2296,"stars":23,"repoUrl":24,"updatedAt":2306},"architecture","create and evaluate architecture decision records","Create or evaluate an architecture decision record (ADR). Use when choosing between technologies (e.g., Kafka vs SQS), documenting a design decision with trade-offs and consequences, reviewing a system design proposal, or designing a new component from requirements and constraints.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2297,2300,2302,2303],{"name":2298,"slug":2299,"type":16},"ADR","adr",{"name":2301,"slug":2292,"type":16},"Architecture",{"name":2131,"slug":2132,"type":16},{"name":2304,"slug":2305,"type":16},"Engineering","engineering","2026-04-06T17:57:49.26444",{"slug":2308,"name":2308,"fn":2309,"description":2310,"org":2311,"tags":2312,"stars":23,"repoUrl":24,"updatedAt":2325},"audit-support","support SOX 404 control testing","Support SOX 404 compliance with control testing methodology, sample selection, and documentation standards. Use when generating testing workpapers, selecting audit samples, classifying control deficiencies, or preparing for internal or external audits.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2313,2316,2319,2322],{"name":2314,"slug":2315,"type":16},"Audit","audit",{"name":2317,"slug":2318,"type":16},"Finance","finance",{"name":2320,"slug":2321,"type":16},"Regulatory Compliance","regulatory-compliance",{"name":2323,"slug":2324,"type":16},"SOX","sox","2026-04-06T17:57:36.714815",{"slug":2327,"name":2327,"fn":2328,"description":2329,"org":2330,"tags":2331,"stars":23,"repoUrl":24,"updatedAt":2337},"brand-review","review content against brand voice","Review content against your brand voice, style guide, and messaging pillars, flagging deviations by severity with specific before\u002Fafter fixes. Use when checking a draft before it ships, when auditing copy for voice consistency and terminology, or when screening for unsubstantiated claims, missing disclaimers, and other legal flags.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2332,2333,2336],{"name":2086,"slug":2087,"type":16},{"name":2334,"slug":2335,"type":16},"Marketing","marketing",{"name":2189,"slug":2190,"type":16},"2026-04-06T17:58:19.548331",{"slug":2339,"name":2339,"fn":2340,"description":2341,"org":2342,"tags":2343,"stars":23,"repoUrl":24,"updatedAt":2350},"brand-voice-enforcement","enforce brand voice in content","This skill applies brand guidelines to content creation. It should be used when the user asks to \"write an email\", \"draft a proposal\", \"create a pitch deck\", \"write a LinkedIn post\", \"draft a presentation\", \"write a Slack message\", \"draft sales content\", or any content creation request where brand voice should be applied. Also triggers on \"on-brand\", \"brand voice\", \"enforce voice\", \"apply brand guidelines\", \"brand-aligned content\", \"write in our voice\", \"use our brand tone\", \"make this sound like us\", \"rewrite this in our tone\", or \"this doesn't sound on-brand\". Not for generating guidelines from scratch (use guideline-generation) or discovering brand materials (use discover-brand).\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2344,2345,2346,2349],{"name":2086,"slug":2087,"type":16},{"name":2185,"slug":2186,"type":16},{"name":2347,"slug":2348,"type":16},"Content Creation","content-creation",{"name":2189,"slug":2190,"type":16},"2026-04-06T18:00:23.528956",200]