{
  "schemaVersion": "1.0",
  "item": {
    "slug": "afrexai-startup-metrics-engine",
    "name": "Startup Metrics Command Center",
    "source": "tencent",
    "type": "skill",
    "category": "数据分析",
    "sourceUrl": "https://clawhub.ai/1kalin/afrexai-startup-metrics-engine",
    "canonicalUrl": "https://clawhub.ai/1kalin/afrexai-startup-metrics-engine",
    "targetPlatform": "OpenClaw"
  },
  "install": {
    "downloadMode": "redirect",
    "downloadUrl": "/downloads/afrexai-startup-metrics-engine",
    "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=afrexai-startup-metrics-engine",
    "sourcePlatform": "tencent",
    "targetPlatform": "OpenClaw",
    "installMethod": "Manual import",
    "extraction": "Extract archive",
    "prerequisites": [
      "OpenClaw"
    ],
    "packageFormat": "ZIP package",
    "includedAssets": [
      "README.md",
      "SKILL.md"
    ],
    "primaryDoc": "SKILL.md",
    "quickSetup": [
      "Download the package from Yavira.",
      "Extract the archive and review SKILL.md first.",
      "Import or place the package into your OpenClaw setup."
    ],
    "agentAssist": {
      "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.",
      "steps": [
        "Download the package from Yavira.",
        "Extract it into a folder your agent can access.",
        "Paste one of the prompts below and point your agent at the extracted folder."
      ],
      "prompts": [
        {
          "label": "New install",
          "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete."
        },
        {
          "label": "Upgrade existing",
          "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run."
        }
      ]
    },
    "sourceHealth": {
      "source": "tencent",
      "status": "healthy",
      "reason": "direct_download_ok",
      "recommendedAction": "download",
      "checkedAt": "2026-04-23T16:43:11.935Z",
      "expiresAt": "2026-04-30T16:43:11.935Z",
      "httpStatus": 200,
      "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard",
      "contentType": "application/zip",
      "probeMethod": "head",
      "details": {
        "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard",
        "contentDisposition": "attachment; filename=\"4claw-imageboard-1.0.1.zip\"",
        "redirectLocation": null,
        "bodySnippet": null
      },
      "scope": "source",
      "summary": "Source download looks usable.",
      "detail": "Yavira can redirect you to the upstream package for this source.",
      "primaryActionLabel": "Download for OpenClaw",
      "primaryActionHref": "/downloads/afrexai-startup-metrics-engine"
    },
    "validation": {
      "installChecklist": [
        "Use the Yavira download entry.",
        "Review SKILL.md after the package is downloaded.",
        "Confirm the extracted package contains the expected setup assets."
      ],
      "postInstallChecks": [
        "Confirm the extracted package includes the expected docs or setup files.",
        "Validate the skill or prompts are available in your target agent workspace.",
        "Capture any manual follow-up steps the agent could not complete."
      ]
    },
    "downloadPageUrl": "https://openagent3.xyz/downloads/afrexai-startup-metrics-engine",
    "agentPageUrl": "https://openagent3.xyz/skills/afrexai-startup-metrics-engine/agent",
    "manifestUrl": "https://openagent3.xyz/skills/afrexai-startup-metrics-engine/agent.json",
    "briefUrl": "https://openagent3.xyz/skills/afrexai-startup-metrics-engine/agent.md"
  },
  "agentAssist": {
    "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.",
    "steps": [
      "Download the package from Yavira.",
      "Extract it into a folder your agent can access.",
      "Paste one of the prompts below and point your agent at the extracted folder."
    ],
    "prompts": [
      {
        "label": "New install",
        "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete."
      },
      {
        "label": "Upgrade existing",
        "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run."
      }
    ]
  },
  "documentation": {
    "source": "clawhub",
    "primaryDoc": "SKILL.md",
    "sections": [
      {
        "title": "Startup Metrics Command Center",
        "body": "Your complete system for tracking, diagnosing, and communicating startup health — not just formulas, but the thinking behind what to measure, when, and what to do when numbers go wrong."
      },
      {
        "title": "Step 1 — Identify Your Model & Stage",
        "body": "Before tracking anything, classify yourself:\n\nBusiness Model:\n\nmodel_type:\n  saas:\n    sub_type: # self-serve | sales-led | PLG | hybrid\n    pricing: # per-seat | usage-based | flat | tiered\n    contract: # monthly | annual | multi-year\n  marketplace:\n    type: # managed | unmanaged | SaaS-enabled\n    unit: # GMV | take-rate | transaction\n  consumer:\n    type: # subscription | ad-supported | freemium | transactional\n    engagement_model: # DAU/MAU | session-based | content\n  hardware_plus_software:\n    type: # device + subscription | IoT | embedded\n\nStage (determines what matters):\n\nStageARR RangeNorth Star FocusBoard Cares AboutPre-seed$0-$50KEngagement + retention signalProblem-solution fit evidenceSeed$50K-$500KCohort retention + early revenueProduct-market fit signalsSeries A$500K-$3MGrowth efficiency + unit economicsLTV:CAC, NDR, growth rateSeries B$3M-$15MScalability + operating leverageRule of 40, magic number, burn multipleGrowth$15M+Capital efficiency + market shareNet margins, NRR, competitive moat"
      },
      {
        "title": "Step 2 — Build Your Metric Stack",
        "body": "Layer 1: Health Vitals (track daily)\n\n- Revenue: MRR, ARR, net new MRR\n- Growth: MoM growth rate, WoW for early stage\n- Retention: Logo churn rate, revenue churn rate\n- Cash: Monthly burn, runway in months\n\nLayer 2: Efficiency (track weekly)\n\n- Unit economics: CAC, LTV, LTV:CAC ratio, payback months\n- Sales: Pipeline coverage, win rate, sales cycle length\n- Product: Activation rate, feature adoption, NPS/CSAT\n- Team: Revenue per employee, quota attainment\n\nLayer 3: Strategic (track monthly)\n\n- NDR (Net Dollar Retention)\n- Burn multiple\n- Rule of 40 score\n- Magic number\n- Cohort analysis curves"
      },
      {
        "title": "Revenue Metrics",
        "body": "MRR = Σ(active_subscriptions × monthly_price)\nARR = MRR × 12\n\nNet New MRR = New MRR + Expansion MRR - Churned MRR - Contraction MRR\n\nMRR Components:\n  new_mrr:         First-time customer revenue this month\n  expansion_mrr:   Upsell + cross-sell from existing customers\n  churned_mrr:     Revenue lost from customers who left\n  contraction_mrr: Revenue lost from downgrades (customer stayed)\n  reactivation_mrr: Revenue from returning churned customers\n\nMoM Growth = (MRR_current - MRR_previous) / MRR_previous\nCMGR (Compound Monthly Growth Rate) = (MRR_end / MRR_start)^(1/months) - 1\n\nWhy CMGR > MoM: Monthly growth is noisy. CMGR smooths 6-12 month periods for real trend."
      },
      {
        "title": "Unit Economics",
        "body": "CAC = Total_Sales_Marketing_Spend / New_Customers_Acquired\n  - Include: salaries, commissions, tools, ads, events, content costs\n  - Exclude: product/engineering, CS (post-sale)\n  - Time-lag adjustment: match spend to cohort it generated (typically 1-3 month lag)\n\nBlended CAC vs Channel CAC:\n  blended_cac = total_spend / total_new_customers\n  channel_cac = channel_spend / channel_new_customers\n  # Always track both — blended hides channel problems\n\nLTV = ARPU × Gross_Margin% × Average_Customer_Lifetime\n  # Or: LTV = ARPU × Gross_Margin% × (1 / Monthly_Churn_Rate)\n  # Cap at 5 years for conservative estimates\n\nLTV:CAC Ratio — THE ratio:\n  > 5.0  → Under-investing in growth (spend more!)\n  3.0-5.0 → Excellent efficiency\n  1.5-3.0 → Healthy but watch payback period\n  1.0-1.5 → Marginal — fix churn or reduce CAC\n  < 1.0  → Burning cash per customer — STOP and fix\n\nCAC Payback = CAC / (Monthly_ARPU × Gross_Margin%)\n  < 6 months  → Elite (PLG companies)\n  6-12 months → Great\n  12-18 months → Acceptable for enterprise\n  > 18 months → Danger zone (unless >130% NDR)"
      },
      {
        "title": "Retention & Churn",
        "body": "Logo Churn Rate = Customers_Lost / Customers_Start_of_Period\nRevenue Churn Rate = MRR_Lost / MRR_Start_of_Period\n  # Revenue churn > logo churn = losing big customers (very bad)\n  # Revenue churn < logo churn = losing small customers (less bad)\n\nNet Dollar Retention (NDR) = (Starting_MRR + Expansion - Contraction - Churn) / Starting_MRR\n  > 130% → World-class (Snowflake, Twilio territory)\n  110-130% → Excellent\n  100-110% → Good\n  90-100% → Acceptable but concerning\n  < 90% → Leaky bucket — growth can't outrun churn\n\nGross Dollar Retention (GDR) = (Starting_MRR - Contraction - Churn) / Starting_MRR\n  # NDR without expansion — shows your floor\n  > 90% → Sticky product\n  80-90% → Normal for SMB\n  < 80% → Product or market problem"
      },
      {
        "title": "Growth Efficiency",
        "body": "Burn Multiple = Net_Burn / Net_New_ARR\n  < 1.0 → Amazing (rare at early stage)\n  1.0-1.5 → Great\n  1.5-2.0 → Good\n  2.0-3.0 → Mediocre\n  > 3.0 → Bad — inefficient growth\n\nRule of 40 = Revenue_Growth_Rate% + Profit_Margin%\n  > 40 → Healthy SaaS (IPO-ready)\n  # Example: 60% growth + -20% margin = 40 ✓\n  # Example: 20% growth + 20% margin = 40 ✓\n\nMagic Number = Net_New_ARR_This_Quarter / Sales_Marketing_Spend_Last_Quarter\n  > 1.0 → Efficient, invest more in S&M\n  0.5-1.0 → OK, optimize before scaling\n  < 0.5 → Inefficient — fix before spending more\n\nHype Ratio = Valuation / ARR\n  # Reality check on fundraising expectations\n  # Median SaaS multiples: 6-12x ARR (varies by growth + retention)"
      },
      {
        "title": "Cash & Runway",
        "body": "Monthly Burn = Total_Monthly_Expenses - Total_Monthly_Revenue\nGross Burn = Total_Monthly_Expenses (ignoring revenue)\nNet Burn = Gross_Burn - Revenue\n\nRunway = Cash_Balance / Monthly_Net_Burn\n  > 18 months → Comfortable\n  12-18 months → Start planning next raise\n  6-12 months → Urgently fundraising\n  < 6 months → Default alive or dead calculation needed\n\nDefault Alive? = Can_Current_Growth_Rate_Make_Revenue > Expenses_Before_Cash_Runs_Out\n  # Paul Graham's test — if growing, project the intersection"
      },
      {
        "title": "Sales Efficiency",
        "body": "Sales Cycle Length = Avg_Days(First_Touch → Closed_Won)\nPipeline Coverage = Total_Pipeline_Value / Revenue_Target\n  # Need 3-4x for predictable revenue\n  \nWin Rate = Deals_Won / Total_Deals_in_Stage\n  By stage: SQL→Opp (30-40%), Opp→Proposal (50-60%), Proposal→Close (60-70%)\n\nACV (Annual Contract Value) = Total_Contract_Value / Contract_Years\nASP (Average Selling Price) = Total_Revenue / Deals_Closed\n\nQuota Attainment = Actual_Bookings / Quota_Target\n  # Healthy org: 60-70% of reps hitting quota\n\nSales Efficiency = Net_New_ARR / Fully_Loaded_Sales_Cost\n  > 1.0 → Scalable"
      },
      {
        "title": "Phase 3: Diagnostic Framework — PULSE Method",
        "body": "When a metric is off, don't just report it — diagnose it."
      },
      {
        "title": "P — Pattern Recognition",
        "body": "Questions:\n- Is this a trend (3+ months) or a blip (1 month)?\n- Is it seasonal or structural?\n- Did it change gradually or suddenly?\n- Which cohorts/segments are affected?"
      },
      {
        "title": "U — Upstream Tracing",
        "body": "Every metric has upstream drivers. Trace back:\n\nRevenue declining? →\n  ├── New MRR down? → Lead volume? → Conversion rate? → Channel performance?\n  ├── Expansion down? → Upsell attempts? → Product adoption? → CSM activity?\n  └── Churn up? → Which segment? → Voluntary vs involuntary? → Reasons?\n\nCAC increasing? →\n  ├── Spend up? → Which channels? → CPM/CPC changes?\n  ├── Volume same but cost up? → Market saturation? → Competition?\n  └── Conversion down? → Funnel stage? → Lead quality? → Sales process?"
      },
      {
        "title": "L — Leverage Point",
        "body": "Find the highest-impact intervention:\n- Which single metric, if improved 10%, would cascade the most?\n- What's the cheapest/fastest fix vs highest-impact fix?\n- Score: Impact (1-5) × Feasibility (1-5) × Speed (1-5)"
      },
      {
        "title": "S — So-What Translation",
        "body": "Convert metric into business language:\n- \"Churn increased 2%\" → \"We'll lose $X00K ARR this year at this rate\"\n- \"CAC payback is 18 months\" → \"Each new customer is cash-negative for 1.5 years\"\n- \"NDR is 95%\" → \"Even with zero new sales, we shrink 5% annually\""
      },
      {
        "title": "E — Experiment Design",
        "body": "diagnostic_experiment:\n  hypothesis: \"[Metric] is declining because [upstream cause]\"\n  test: \"[Specific action] for [time period]\"\n  success_metric: \"[Metric] improves by [X%] within [timeframe]\"\n  sample: \"[Segment/cohort to test on]\"\n  kill_criteria: \"Stop if [negative signal] within [days]\""
      },
      {
        "title": "Phase 4: Cohort Analysis — The Truth Machine",
        "body": "Aggregate metrics lie. Cohorts tell the truth."
      },
      {
        "title": "Revenue Cohort Table",
        "body": "Track each monthly cohort's MRR over time:\n\n         Month 0   Month 1   Month 3   Month 6   Month 12\nJan '25  $50K      $48K      $45K      $42K      $38K\nFeb '25  $55K      $53K      $50K      $48K      —\nMar '25  $60K      $58K      $57K      $56K      —\nApr '25  $45K      $44K      $43K      —         —\n\nReading this:\n- Jan cohort retained 76% at month 12 → mediocre\n- Mar cohort retained 93% at month 3 → improving! What changed?\n- Apr cohort started smaller but retention looks good"
      },
      {
        "title": "Engagement Cohort (Non-Revenue Signal)",
        "body": "cohort_engagement:\n  week_1_activation: # % completing key action within 7 days\n  week_4_habit: # % using product 3+ days in week 4\n  month_3_retention: # % still active at 90 days\n  \n  # Leading indicators of revenue retention\n  # If engagement drops, revenue follows 1-3 months later"
      },
      {
        "title": "Cohort Red Flags",
        "body": "🚩 Each new cohort retains worse → product-market fit eroding\n🚩 Large cohorts churn more → scaling quality issues\n🚩 Specific channel cohorts churn fast → bad-fit leads\n🚩 Expansion only in old cohorts → pricing/packaging problem"
      },
      {
        "title": "Monthly Investor Update Template",
        "body": "investor_update:\n  subject: \"[Company] — [Month] Update: [One-line headline]\"\n  \n  # 1. TL;DR (3 bullets max)\n  highlights:\n    - \"ARR: $X (+Y% MoM) — [context]\"\n    - \"Key win: [biggest achievement]\"\n    - \"Challenge: [biggest problem + what you're doing]\"\n  \n  # 2. Key Metrics Table\n  metrics:\n    arr: {current: \"\", prior_month: \"\", delta: \"\"}\n    mrr: {current: \"\", growth_mom: \"\"}\n    customers: {total: \"\", new: \"\", churned: \"\"}\n    ndr: \"\"\n    burn_rate: \"\"\n    runway_months: \"\"\n    cash_balance: \"\"\n    \n  # 3. What Happened (5-7 bullets)\n  wins: []\n  challenges: []\n  \n  # 4. What's Next (3-5 bullets)\n  next_month_priorities: []\n  \n  # 5. Asks (be specific!)\n  asks:\n    - intro: \"Looking for intro to [person/company] for [reason]\"\n    - advice: \"Would love 15 min on [specific topic]\"\n    - hiring: \"Seeking [role] — know anyone?\""
      },
      {
        "title": "Board Deck Metric Slides",
        "body": "Slide 1: Business Health Dashboard\n\nARR: $___     MoM: ___%     NDR: ___%\nCustomers: ___  New: ___    Churned: ___\nRunway: ___ months          Burn Multiple: ___\n\nTraffic light: 🟢 On track | 🟡 Watch | 🔴 Action needed\n\nSlide 2: Revenue Waterfall\n\nStarting MRR:     $___\n+ New:            $___\n+ Expansion:      $___\n- Contraction:    $___\n- Churn:          $___\n= Ending MRR:     $___\n\nSlide 3: Unit Economics\n\nCAC: $___  →  LTV: $___  →  LTV:CAC: ___x\nPayback: ___ months\nBlended vs top channel efficiency"
      },
      {
        "title": "SaaS Additions",
        "body": "Quick Ratio = (New MRR + Expansion MRR) / (Churned MRR + Contraction MRR)\n  > 4.0 → Very healthy growth\n  2.0-4.0 → Good\n  1.0-2.0 → Sustainable but slow\n  < 1.0 → Shrinking\n\nLogo-to-Revenue Retention Gap:\n  If logo retention 85% but revenue retention 95% → upsell compensates\n  If logo retention 85% and revenue retention 85% → no expansion = problem\n\nExpansion Revenue % = Expansion MRR / Total New MRR\n  > 30% → Healthy at scale\n  # Best SaaS: expansion > new revenue (Twilio was 170% NDR)"
      },
      {
        "title": "Marketplace Additions",
        "body": "GMV (Gross Merchandise Value) = Total value of transactions on platform\nTake Rate = Platform Revenue / GMV\n  5-15% → Typical for most marketplaces\n  15-30% → Managed/full-service marketplaces\n  \nSupply-side metrics:\n  supply_liquidity = listings_with_transaction / total_listings\n  time_to_first_match = avg_days_from_listing_to_sale\n  \nDemand-side metrics:\n  search_to_fill = completed_transactions / searches\n  repeat_purchase_rate = returning_buyers / total_buyers"
      },
      {
        "title": "Consumer/PLG Additions",
        "body": "DAU/MAU Ratio:\n  > 50% → Exceptional (messaging apps)\n  25-50% → Strong habit (social, productivity)\n  10-25% → Good (media, entertainment)\n  < 10% → Weak engagement\n\nViral Coefficient (K-factor) = Invites_per_User × Conversion_Rate\n  > 1.0 → Viral growth (each user brings >1 new user)\n  0.5-1.0 → Amplified growth\n  < 0.5 → Not viral — need paid acquisition\n\nFree-to-Paid Conversion:\n  PLG benchmark: 2-5% of free users convert\n  Freemium benchmark: 1-3%\n  Enterprise self-serve: 5-15%\n\nTime to Value = Time from signup to \"aha moment\"\n  # Reduce this aggressively — strongest lever for activation"
      },
      {
        "title": "Vanity vs Real Metrics",
        "body": "Vanity (Avoid)Real (Track)Total signupsActivated users (completed key action)Page viewsEngaged sessions (>2 min or action taken)\"Pipeline\"Qualified pipeline (met ICP criteria)Gross revenueNet revenue (after refunds + credits)Total customersActive customers (logged in last 30d)DownloadsWAU/MAU\"Partnerships\"Revenue from partnerships"
      },
      {
        "title": "Common Manipulation Tactics to Watch",
        "body": "🚩 Counting annual contracts as MRR at signing (vs. monthly recognition)\n🚩 Excluding \"one-time\" churns from churn rate\n🚩 Using gross revenue instead of net\n🚩 Measuring CAC without fully-loaded costs\n🚩 Cherry-picking best cohort as \"representative\"\n🚩 Counting reactivations as new customers\n🚩 Using \"committed ARR\" (signed but not live)\n🚩 Trailing-12-month NDR when recent cohorts are worse"
      },
      {
        "title": "When CAC Is Too High",
        "body": "1. Audit channel efficiency — kill bottom 20% channels\n2. Improve activation rate (reduces wasted spend)\n3. Increase conversion at each funnel stage (+10% each = compound effect)\n4. Shift mix: more organic/PLG, less paid\n5. Reduce sales cycle length (lower cost per deal)\n6. Tighten ICP — stop selling to bad-fit customers"
      },
      {
        "title": "When Churn Is Too High",
        "body": "1. Segment: which customers churn? (Size, channel, use case)\n2. Time: when do they churn? (Month 1-3 = onboarding, 6-12 = value, 12+ = competition)\n3. Reason: exit survey + CS interviews (top 3 reasons)\n4. Fix activation if month 1-3 churn\n5. Fix value delivery if month 6-12 churn\n6. Fix switching cost / competitive moat if 12+ churn"
      },
      {
        "title": "When Growth Stalls",
        "body": "1. Check: is TAM exhausted in current segment? → Expand to adjacent\n2. Check: conversion rates declining? → Product or message fatigue\n3. Check: CAC rising with flat volume? → Channel saturation\n4. Check: expansion revenue flat? → Packaging/pricing problem\n5. Check: sales cycle lengthening? → Market conditions or competition"
      },
      {
        "title": "When Raising Capital",
        "body": "Metrics investors care about BY STAGE:\n\nPre-seed: Engagement, retention curves, market size\nSeed: MoM growth (15%+), retention cohorts, early unit economics\nSeries A: $1M+ ARR, 3x+ YoY growth, LTV:CAC > 3, NDR > 100%\nSeries B: $5M+ ARR, path to Rule of 40, burn multiple < 2, sales efficiency"
      },
      {
        "title": "Quick Commands",
        "body": "\"Set up metrics for [stage] [model] startup\" → Full metric stack recommendation\n\"Diagnose [metric]\" → PULSE diagnostic framework\n\"Build investor update for [month]\" → Template with guidance\n\"Cohort analysis on [data]\" → Retention curve analysis\n\"Compare us to benchmarks\" → Gap analysis vs stage-appropriate benchmarks\n\"What metrics for Series [A/B] raise?\" → Investor-ready checklist\n\"Calculate unit economics from [data]\" → Full LTV, CAC, payback analysis\n\"Red flag check\" → Scan metrics for warning signs\n\"Board deck metrics\" → Generate slide-ready metric views"
      },
      {
        "title": "Multi-Product Companies",
        "body": "Track metrics per product line AND blended. Watch for cross-subsidization where one product's margins mask another's losses."
      },
      {
        "title": "Usage-Based Pricing",
        "body": "MRR is estimated, not contracted. Track committed vs consumed. Expansion is automatic (usage growth), so NDR is naturally higher — compare to usage-based peers, not seat-based."
      },
      {
        "title": "Negative Churn via Price Increases",
        "body": "If NDR > 100% only because of price increases (not organic expansion), this is fragile. Separate price-driven vs usage-driven expansion."
      },
      {
        "title": "Very Early Stage (Pre-Revenue)",
        "body": "Track leading indicators: activation rate, engagement frequency, NPS, waitlist growth, organic traffic, time-to-value. Revenue metrics come later — don't force them."
      },
      {
        "title": "Seasonal Businesses",
        "body": "Use YoY comparisons, not MoM. Adjust cohort analysis for seasonal patterns. Build seasonal forecast models.\n\nBuilt by AfrexAI — turning data into revenue."
      }
    ],
    "body": "Startup Metrics Command Center\n\nYour complete system for tracking, diagnosing, and communicating startup health — not just formulas, but the thinking behind what to measure, when, and what to do when numbers go wrong.\n\nPhase 1: Metrics Architecture\nStep 1 — Identify Your Model & Stage\n\nBefore tracking anything, classify yourself:\n\nBusiness Model:\n\nmodel_type:\n  saas:\n    sub_type: # self-serve | sales-led | PLG | hybrid\n    pricing: # per-seat | usage-based | flat | tiered\n    contract: # monthly | annual | multi-year\n  marketplace:\n    type: # managed | unmanaged | SaaS-enabled\n    unit: # GMV | take-rate | transaction\n  consumer:\n    type: # subscription | ad-supported | freemium | transactional\n    engagement_model: # DAU/MAU | session-based | content\n  hardware_plus_software:\n    type: # device + subscription | IoT | embedded\n\n\nStage (determines what matters):\n\nStage\tARR Range\tNorth Star Focus\tBoard Cares About\nPre-seed\t$0-$50K\tEngagement + retention signal\tProblem-solution fit evidence\nSeed\t$50K-$500K\tCohort retention + early revenue\tProduct-market fit signals\nSeries A\t$500K-$3M\tGrowth efficiency + unit economics\tLTV:CAC, NDR, growth rate\nSeries B\t$3M-$15M\tScalability + operating leverage\tRule of 40, magic number, burn multiple\nGrowth\t$15M+\tCapital efficiency + market share\tNet margins, NRR, competitive moat\nStep 2 — Build Your Metric Stack\n\nLayer 1: Health Vitals (track daily)\n\n- Revenue: MRR, ARR, net new MRR\n- Growth: MoM growth rate, WoW for early stage\n- Retention: Logo churn rate, revenue churn rate\n- Cash: Monthly burn, runway in months\n\n\nLayer 2: Efficiency (track weekly)\n\n- Unit economics: CAC, LTV, LTV:CAC ratio, payback months\n- Sales: Pipeline coverage, win rate, sales cycle length\n- Product: Activation rate, feature adoption, NPS/CSAT\n- Team: Revenue per employee, quota attainment\n\n\nLayer 3: Strategic (track monthly)\n\n- NDR (Net Dollar Retention)\n- Burn multiple\n- Rule of 40 score\n- Magic number\n- Cohort analysis curves\n\nPhase 2: The Complete Formula Reference\nRevenue Metrics\nMRR = Σ(active_subscriptions × monthly_price)\nARR = MRR × 12\n\nNet New MRR = New MRR + Expansion MRR - Churned MRR - Contraction MRR\n\nMRR Components:\n  new_mrr:         First-time customer revenue this month\n  expansion_mrr:   Upsell + cross-sell from existing customers\n  churned_mrr:     Revenue lost from customers who left\n  contraction_mrr: Revenue lost from downgrades (customer stayed)\n  reactivation_mrr: Revenue from returning churned customers\n\nMoM Growth = (MRR_current - MRR_previous) / MRR_previous\nCMGR (Compound Monthly Growth Rate) = (MRR_end / MRR_start)^(1/months) - 1\n\n\nWhy CMGR > MoM: Monthly growth is noisy. CMGR smooths 6-12 month periods for real trend.\n\nUnit Economics\nCAC = Total_Sales_Marketing_Spend / New_Customers_Acquired\n  - Include: salaries, commissions, tools, ads, events, content costs\n  - Exclude: product/engineering, CS (post-sale)\n  - Time-lag adjustment: match spend to cohort it generated (typically 1-3 month lag)\n\nBlended CAC vs Channel CAC:\n  blended_cac = total_spend / total_new_customers\n  channel_cac = channel_spend / channel_new_customers\n  # Always track both — blended hides channel problems\n\nLTV = ARPU × Gross_Margin% × Average_Customer_Lifetime\n  # Or: LTV = ARPU × Gross_Margin% × (1 / Monthly_Churn_Rate)\n  # Cap at 5 years for conservative estimates\n\nLTV:CAC Ratio — THE ratio:\n  > 5.0  → Under-investing in growth (spend more!)\n  3.0-5.0 → Excellent efficiency\n  1.5-3.0 → Healthy but watch payback period\n  1.0-1.5 → Marginal — fix churn or reduce CAC\n  < 1.0  → Burning cash per customer — STOP and fix\n\nCAC Payback = CAC / (Monthly_ARPU × Gross_Margin%)\n  < 6 months  → Elite (PLG companies)\n  6-12 months → Great\n  12-18 months → Acceptable for enterprise\n  > 18 months → Danger zone (unless >130% NDR)\n\nRetention & Churn\nLogo Churn Rate = Customers_Lost / Customers_Start_of_Period\nRevenue Churn Rate = MRR_Lost / MRR_Start_of_Period\n  # Revenue churn > logo churn = losing big customers (very bad)\n  # Revenue churn < logo churn = losing small customers (less bad)\n\nNet Dollar Retention (NDR) = (Starting_MRR + Expansion - Contraction - Churn) / Starting_MRR\n  > 130% → World-class (Snowflake, Twilio territory)\n  110-130% → Excellent\n  100-110% → Good\n  90-100% → Acceptable but concerning\n  < 90% → Leaky bucket — growth can't outrun churn\n\nGross Dollar Retention (GDR) = (Starting_MRR - Contraction - Churn) / Starting_MRR\n  # NDR without expansion — shows your floor\n  > 90% → Sticky product\n  80-90% → Normal for SMB\n  < 80% → Product or market problem\n\nGrowth Efficiency\nBurn Multiple = Net_Burn / Net_New_ARR\n  < 1.0 → Amazing (rare at early stage)\n  1.0-1.5 → Great\n  1.5-2.0 → Good\n  2.0-3.0 → Mediocre\n  > 3.0 → Bad — inefficient growth\n\nRule of 40 = Revenue_Growth_Rate% + Profit_Margin%\n  > 40 → Healthy SaaS (IPO-ready)\n  # Example: 60% growth + -20% margin = 40 ✓\n  # Example: 20% growth + 20% margin = 40 ✓\n\nMagic Number = Net_New_ARR_This_Quarter / Sales_Marketing_Spend_Last_Quarter\n  > 1.0 → Efficient, invest more in S&M\n  0.5-1.0 → OK, optimize before scaling\n  < 0.5 → Inefficient — fix before spending more\n\nHype Ratio = Valuation / ARR\n  # Reality check on fundraising expectations\n  # Median SaaS multiples: 6-12x ARR (varies by growth + retention)\n\nCash & Runway\nMonthly Burn = Total_Monthly_Expenses - Total_Monthly_Revenue\nGross Burn = Total_Monthly_Expenses (ignoring revenue)\nNet Burn = Gross_Burn - Revenue\n\nRunway = Cash_Balance / Monthly_Net_Burn\n  > 18 months → Comfortable\n  12-18 months → Start planning next raise\n  6-12 months → Urgently fundraising\n  < 6 months → Default alive or dead calculation needed\n\nDefault Alive? = Can_Current_Growth_Rate_Make_Revenue > Expenses_Before_Cash_Runs_Out\n  # Paul Graham's test — if growing, project the intersection\n\nSales Efficiency\nSales Cycle Length = Avg_Days(First_Touch → Closed_Won)\nPipeline Coverage = Total_Pipeline_Value / Revenue_Target\n  # Need 3-4x for predictable revenue\n  \nWin Rate = Deals_Won / Total_Deals_in_Stage\n  By stage: SQL→Opp (30-40%), Opp→Proposal (50-60%), Proposal→Close (60-70%)\n\nACV (Annual Contract Value) = Total_Contract_Value / Contract_Years\nASP (Average Selling Price) = Total_Revenue / Deals_Closed\n\nQuota Attainment = Actual_Bookings / Quota_Target\n  # Healthy org: 60-70% of reps hitting quota\n\nSales Efficiency = Net_New_ARR / Fully_Loaded_Sales_Cost\n  > 1.0 → Scalable\n\nPhase 3: Diagnostic Framework — PULSE Method\n\nWhen a metric is off, don't just report it — diagnose it.\n\nP — Pattern Recognition\nQuestions:\n- Is this a trend (3+ months) or a blip (1 month)?\n- Is it seasonal or structural?\n- Did it change gradually or suddenly?\n- Which cohorts/segments are affected?\n\nU — Upstream Tracing\nEvery metric has upstream drivers. Trace back:\n\nRevenue declining? →\n  ├── New MRR down? → Lead volume? → Conversion rate? → Channel performance?\n  ├── Expansion down? → Upsell attempts? → Product adoption? → CSM activity?\n  └── Churn up? → Which segment? → Voluntary vs involuntary? → Reasons?\n\nCAC increasing? →\n  ├── Spend up? → Which channels? → CPM/CPC changes?\n  ├── Volume same but cost up? → Market saturation? → Competition?\n  └── Conversion down? → Funnel stage? → Lead quality? → Sales process?\n\nL — Leverage Point\nFind the highest-impact intervention:\n- Which single metric, if improved 10%, would cascade the most?\n- What's the cheapest/fastest fix vs highest-impact fix?\n- Score: Impact (1-5) × Feasibility (1-5) × Speed (1-5)\n\nS — So-What Translation\nConvert metric into business language:\n- \"Churn increased 2%\" → \"We'll lose $X00K ARR this year at this rate\"\n- \"CAC payback is 18 months\" → \"Each new customer is cash-negative for 1.5 years\"\n- \"NDR is 95%\" → \"Even with zero new sales, we shrink 5% annually\"\n\nE — Experiment Design\ndiagnostic_experiment:\n  hypothesis: \"[Metric] is declining because [upstream cause]\"\n  test: \"[Specific action] for [time period]\"\n  success_metric: \"[Metric] improves by [X%] within [timeframe]\"\n  sample: \"[Segment/cohort to test on]\"\n  kill_criteria: \"Stop if [negative signal] within [days]\"\n\nPhase 4: Cohort Analysis — The Truth Machine\n\nAggregate metrics lie. Cohorts tell the truth.\n\nRevenue Cohort Table\nTrack each monthly cohort's MRR over time:\n\n         Month 0   Month 1   Month 3   Month 6   Month 12\nJan '25  $50K      $48K      $45K      $42K      $38K\nFeb '25  $55K      $53K      $50K      $48K      —\nMar '25  $60K      $58K      $57K      $56K      —\nApr '25  $45K      $44K      $43K      —         —\n\nReading this:\n- Jan cohort retained 76% at month 12 → mediocre\n- Mar cohort retained 93% at month 3 → improving! What changed?\n- Apr cohort started smaller but retention looks good\n\nEngagement Cohort (Non-Revenue Signal)\ncohort_engagement:\n  week_1_activation: # % completing key action within 7 days\n  week_4_habit: # % using product 3+ days in week 4\n  month_3_retention: # % still active at 90 days\n  \n  # Leading indicators of revenue retention\n  # If engagement drops, revenue follows 1-3 months later\n\nCohort Red Flags\n🚩 Each new cohort retains worse → product-market fit eroding\n🚩 Large cohorts churn more → scaling quality issues\n🚩 Specific channel cohorts churn fast → bad-fit leads\n🚩 Expansion only in old cohorts → pricing/packaging problem\n\nPhase 5: Board & Investor Reporting\nMonthly Investor Update Template\ninvestor_update:\n  subject: \"[Company] — [Month] Update: [One-line headline]\"\n  \n  # 1. TL;DR (3 bullets max)\n  highlights:\n    - \"ARR: $X (+Y% MoM) — [context]\"\n    - \"Key win: [biggest achievement]\"\n    - \"Challenge: [biggest problem + what you're doing]\"\n  \n  # 2. Key Metrics Table\n  metrics:\n    arr: {current: \"\", prior_month: \"\", delta: \"\"}\n    mrr: {current: \"\", growth_mom: \"\"}\n    customers: {total: \"\", new: \"\", churned: \"\"}\n    ndr: \"\"\n    burn_rate: \"\"\n    runway_months: \"\"\n    cash_balance: \"\"\n    \n  # 3. What Happened (5-7 bullets)\n  wins: []\n  challenges: []\n  \n  # 4. What's Next (3-5 bullets)\n  next_month_priorities: []\n  \n  # 5. Asks (be specific!)\n  asks:\n    - intro: \"Looking for intro to [person/company] for [reason]\"\n    - advice: \"Would love 15 min on [specific topic]\"\n    - hiring: \"Seeking [role] — know anyone?\"\n\nBoard Deck Metric Slides\n\nSlide 1: Business Health Dashboard\n\nARR: $___     MoM: ___%     NDR: ___%\nCustomers: ___  New: ___    Churned: ___\nRunway: ___ months          Burn Multiple: ___\n\nTraffic light: 🟢 On track | 🟡 Watch | 🔴 Action needed\n\n\nSlide 2: Revenue Waterfall\n\nStarting MRR:     $___\n+ New:            $___\n+ Expansion:      $___\n- Contraction:    $___\n- Churn:          $___\n= Ending MRR:     $___\n\n\nSlide 3: Unit Economics\n\nCAC: $___  →  LTV: $___  →  LTV:CAC: ___x\nPayback: ___ months\nBlended vs top channel efficiency\n\nPhase 6: Model-Specific Metrics\nSaaS Additions\nQuick Ratio = (New MRR + Expansion MRR) / (Churned MRR + Contraction MRR)\n  > 4.0 → Very healthy growth\n  2.0-4.0 → Good\n  1.0-2.0 → Sustainable but slow\n  < 1.0 → Shrinking\n\nLogo-to-Revenue Retention Gap:\n  If logo retention 85% but revenue retention 95% → upsell compensates\n  If logo retention 85% and revenue retention 85% → no expansion = problem\n\nExpansion Revenue % = Expansion MRR / Total New MRR\n  > 30% → Healthy at scale\n  # Best SaaS: expansion > new revenue (Twilio was 170% NDR)\n\nMarketplace Additions\nGMV (Gross Merchandise Value) = Total value of transactions on platform\nTake Rate = Platform Revenue / GMV\n  5-15% → Typical for most marketplaces\n  15-30% → Managed/full-service marketplaces\n  \nSupply-side metrics:\n  supply_liquidity = listings_with_transaction / total_listings\n  time_to_first_match = avg_days_from_listing_to_sale\n  \nDemand-side metrics:\n  search_to_fill = completed_transactions / searches\n  repeat_purchase_rate = returning_buyers / total_buyers\n\nConsumer/PLG Additions\nDAU/MAU Ratio:\n  > 50% → Exceptional (messaging apps)\n  25-50% → Strong habit (social, productivity)\n  10-25% → Good (media, entertainment)\n  < 10% → Weak engagement\n\nViral Coefficient (K-factor) = Invites_per_User × Conversion_Rate\n  > 1.0 → Viral growth (each user brings >1 new user)\n  0.5-1.0 → Amplified growth\n  < 0.5 → Not viral — need paid acquisition\n\nFree-to-Paid Conversion:\n  PLG benchmark: 2-5% of free users convert\n  Freemium benchmark: 1-3%\n  Enterprise self-serve: 5-15%\n\nTime to Value = Time from signup to \"aha moment\"\n  # Reduce this aggressively — strongest lever for activation\n\nPhase 7: Metric Manipulation Red Flags\nVanity vs Real Metrics\nVanity (Avoid)\tReal (Track)\nTotal signups\tActivated users (completed key action)\nPage views\tEngaged sessions (>2 min or action taken)\n\"Pipeline\"\tQualified pipeline (met ICP criteria)\nGross revenue\tNet revenue (after refunds + credits)\nTotal customers\tActive customers (logged in last 30d)\nDownloads\tWAU/MAU\n\"Partnerships\"\tRevenue from partnerships\nCommon Manipulation Tactics to Watch\n🚩 Counting annual contracts as MRR at signing (vs. monthly recognition)\n🚩 Excluding \"one-time\" churns from churn rate\n🚩 Using gross revenue instead of net\n🚩 Measuring CAC without fully-loaded costs\n🚩 Cherry-picking best cohort as \"representative\"\n🚩 Counting reactivations as new customers\n🚩 Using \"committed ARR\" (signed but not live)\n🚩 Trailing-12-month NDR when recent cohorts are worse\n\nPhase 8: Action Playbooks\nWhen CAC Is Too High\n1. Audit channel efficiency — kill bottom 20% channels\n2. Improve activation rate (reduces wasted spend)\n3. Increase conversion at each funnel stage (+10% each = compound effect)\n4. Shift mix: more organic/PLG, less paid\n5. Reduce sales cycle length (lower cost per deal)\n6. Tighten ICP — stop selling to bad-fit customers\n\nWhen Churn Is Too High\n1. Segment: which customers churn? (Size, channel, use case)\n2. Time: when do they churn? (Month 1-3 = onboarding, 6-12 = value, 12+ = competition)\n3. Reason: exit survey + CS interviews (top 3 reasons)\n4. Fix activation if month 1-3 churn\n5. Fix value delivery if month 6-12 churn\n6. Fix switching cost / competitive moat if 12+ churn\n\nWhen Growth Stalls\n1. Check: is TAM exhausted in current segment? → Expand to adjacent\n2. Check: conversion rates declining? → Product or message fatigue\n3. Check: CAC rising with flat volume? → Channel saturation\n4. Check: expansion revenue flat? → Packaging/pricing problem\n5. Check: sales cycle lengthening? → Market conditions or competition\n\nWhen Raising Capital\nMetrics investors care about BY STAGE:\n\nPre-seed: Engagement, retention curves, market size\nSeed: MoM growth (15%+), retention cohorts, early unit economics\nSeries A: $1M+ ARR, 3x+ YoY growth, LTV:CAC > 3, NDR > 100%\nSeries B: $5M+ ARR, path to Rule of 40, burn multiple < 2, sales efficiency\n\nQuick Commands\n\"Set up metrics for [stage] [model] startup\" → Full metric stack recommendation\n\"Diagnose [metric]\" → PULSE diagnostic framework\n\"Build investor update for [month]\" → Template with guidance\n\"Cohort analysis on [data]\" → Retention curve analysis\n\"Compare us to benchmarks\" → Gap analysis vs stage-appropriate benchmarks\n\"What metrics for Series [A/B] raise?\" → Investor-ready checklist\n\"Calculate unit economics from [data]\" → Full LTV, CAC, payback analysis\n\"Red flag check\" → Scan metrics for warning signs\n\"Board deck metrics\" → Generate slide-ready metric views\nEdge Cases\nMulti-Product Companies\n\nTrack metrics per product line AND blended. Watch for cross-subsidization where one product's margins mask another's losses.\n\nUsage-Based Pricing\n\nMRR is estimated, not contracted. Track committed vs consumed. Expansion is automatic (usage growth), so NDR is naturally higher — compare to usage-based peers, not seat-based.\n\nNegative Churn via Price Increases\n\nIf NDR > 100% only because of price increases (not organic expansion), this is fragile. Separate price-driven vs usage-driven expansion.\n\nVery Early Stage (Pre-Revenue)\n\nTrack leading indicators: activation rate, engagement frequency, NPS, waitlist growth, organic traffic, time-to-value. Revenue metrics come later — don't force them.\n\nSeasonal Businesses\n\nUse YoY comparisons, not MoM. Adjust cohort analysis for seasonal patterns. Build seasonal forecast models.\n\nBuilt by AfrexAI — turning data into revenue."
  },
  "trust": {
    "sourceLabel": "tencent",
    "provenanceUrl": "https://clawhub.ai/1kalin/afrexai-startup-metrics-engine",
    "publisherUrl": "https://clawhub.ai/1kalin/afrexai-startup-metrics-engine",
    "owner": "1kalin",
    "version": "1.0.0",
    "license": null,
    "verificationStatus": "Indexed source record"
  },
  "links": {
    "detailUrl": "https://openagent3.xyz/skills/afrexai-startup-metrics-engine",
    "downloadUrl": "https://openagent3.xyz/downloads/afrexai-startup-metrics-engine",
    "agentUrl": "https://openagent3.xyz/skills/afrexai-startup-metrics-engine/agent",
    "manifestUrl": "https://openagent3.xyz/skills/afrexai-startup-metrics-engine/agent.json",
    "briefUrl": "https://openagent3.xyz/skills/afrexai-startup-metrics-engine/agent.md"
  }
}