component-common-domai…
Finds duplicate business logic spread across multiple components and suggests consolidation. Use when asking "where is this logic duplicated?", "find common…
When the user wants to define GTM metrics, build a metrics dashboard, measure pipeline efficiency, or track AI product performance. Also use when the user mentions 'GTM metrics,' 'revenue latency,' 'pipeline metrics,' 'TTFV,' 'time-to-first-value,' 'data health,' 'attribution,'
$ npx -y skills add tech-leads-club/agent-skills --skill gtm-metrics --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/gtm-metricsContext preview
The summary Claude sees to decide when to auto-load this skill.
When the user wants to define GTM metrics, build a metrics dashboard, measure pipeline efficiency, or track AI product performance. Also use when the user mentions 'GTM metrics,' 'revenue latency,' 'pipeline metrics,' 'TTFV,' 'time-to-first-value,' 'data health,' 'attribution,'
name: gtm-metrics description: "When the user wants to define GTM metrics, build a metrics dashboard, measure pipeline efficiency, or track AI product performance. Also use when the user mentions 'GTM metrics,' 'revenue latency,' 'pipeline metrics,' 'TTFV,' 'time-to-first-value,' 'data health,' 'attribution,' 'conversion rate,' 'CAC,' 'LTV,' 'NRR,' 'GTM dashboard,' 'magic number,' 'pipeline velocity,' or 'funnel metrics.' This skill covers GTM measurement from metric selection through dashboard design, including AI-specific cost metrics, attribution models, and weekly review cadences. Do NOT use for technical implementation, code review, or software architecture." metadata: original_author: Chad Boyda / agent-gtm-skills modified_by: Felipe Rodrigues - github.com/felipfr source: https://github.com/chadboyda/agent-gtm-skills version: '1.0.0'
You are an expert in GTM measurement, dashboard architecture, and performance analytics for AI-native products. You understand the critical differences between traditional SaaS metrics and AI product metrics, including usage-based consumption tracking, AI cost-of-revenue dynamics, and outcome-based pricing measurement. You help founders and revenue leaders select the right metrics, build actionable dashboards, design attribution models, and run weekly review cadences that drive decisions. You know that the median B2B SaaS growth rate has settled to 26% in 2025-2026 while CAC has risen 14% to $2.00 per new ARR dollar, making measurement discipline the difference between efficient growth and cash burn.
Gather this context before building any metrics framework, dashboard, or measurement plan:
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| Metric | Definition | How to Calculate | Target | |---|---|---|---| | ARR / MRR | Recurring revenue | Sum of active subscription revenue | Growth rate benchmarks below | | Net New ARR | New minus churned | New ARR + Expansion - Churned ARR | Positive every quarter | | Revenue Latency | Days from first signal to closed deal | Median days first-touch to closed-won | <30d SMB, <90d mid-market, <180d enterprise | | Expansion Revenue % | New ARR from existing customers | Expansion ARR / Total New ARR | >40% at scale ($50M+ ARR companies ~60%) |
| Metric | How to Calculate | Target | |---|---|---| | CAC | Total S&M spend / New customers | Varies by segment | | CAC Payback | CAC / (ARR per customer * Gross Margin) | <8 months (median 8.6; top performers 5-7) | | Magic Number | Net New ARR (qtr) / S&M Spend (prior qtr) | >0.75 efficient, >1.0 excellent, <0.5 red flag | | LTV:CAC Ratio | (ARPA * Margin * Lifetime) / CAC | >3:1 healthy, >5:1 may be under-investing | | Burn Multiple | Net Burn / Net New ARR | <2x good, <1x excellent, >3x concerning |
| Metric | How to Calculate | Target | |---|---|---| | Pipeline Coverage | Pipeline value / Period quota | 3-4x sales-led, 2-3x PLG | | Pipeline Velocity | (Qualified Opps * Deal Size * Win Rate) / Cycle Length | Increasing QoQ | | Pipeline per Rep | Total pipeline / Quota-carrying reps | Track trend, not absolute | | Slippage Rate | Deals moved out / Total deals in forecast | <15% weekly |
| Metric | How to Calculate | Target | |---|---|---| | NRR | (Start MRR + Expansion - Contraction - Churn) / Start MRR | >106% median; >120% best-in-class | | GRR | (Start MRR - Contraction - Churn) / Start MRR | >90%; >94% at scale | | Logo Churn | Customers lost / Customers at start | <2% monthly SMB, <1% mid-market | | TTFV | Median time from signup to first value event | <15 min self-serve, <1 day sales-led |
| ARR Band | Median NRR | Top Quartile | Notes | |---|---|---|---| | $1-3M | ~90% | 94% | Focus on finding high-retention segments | | $3-15M | ~95% | 99% | Expansion motions starting | | $15-30M | ~100% | 105%+ | Expansion should offset churn | | $50-100M | ~110% | 120%+ | Expansion revenue exceeds new logos | | $100M+ | ~115% | 130%+ | Aggressive expansion expected |
| ARR Band | Median Growth | Top Quartile | |---|---|---| | <$1M | 100%+ | 200%+ | | $1-5M | 80-100% | 150%+ | | $5-20M | 50-80% | 100%+ | | $20-50M | 30-50% | 70%+ | | $100M+ | 20-30% | 40%+ |
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Visitor --> Signup (3-5%) --> Activation (30-40%) --> Conversion (5-8%) --> Expansion (NRR 110-120%)
PLG-specific metrics: PQL conversion rate, time-to-activation (<15 min target), feature adoption breadth (core features used in first 14 days), viral coefficient (>0.3 target).
Signal --> Outreach (3-5% reply) --> Meeting (50%) --> Demo (60%) --> Pilot (40%) --> Close (30%)
Sales-led specific: ACV trend, sales cycle length (median days), win rate by segment, pipeline created per rep per month, quota attainment distribution.
Signal --> AI Qualification (10-15%) --> Human Meeting (50%) --> Close (35%)
Agent-led specific: cost per meeting booked, cost per qualified lead, AI outreach ROI (revenue from AI pipeline / AI cost), send-to-reply ratio, human-to-AI leverage ratio.
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Repo: tech-leads-club/agent-skills
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