gtm-metrics
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,
npx skills add tech-leads-club/agent-skills --skill gtm-metrics --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Defines GTM metrics, dashboards, and measurement for AI-native products, guiding metric selection, dashboard architecture, attribution, AI cost metrics, and weekly review cadences. It emphasizes metrics for AI product performance, usage-based considerations, and weekly governance, from metric selection through dashboard design. It explicitly states not to use the skill for technical implementation, code review, or software architecture.
How it works
The skill presents a structured framework: gathering context before building metrics (sales motion, pricing, ARR/MRR, tools, team size, buyer journey, weekly cadence); a Core GTM Metrics Dashboard with sections for Revenue, Efficiency, Pipeline, and Retention metrics including definitions and targets; Funnel Metrics by GTM motion (PLG, Sales-Led, Agent-Led); AI Product-Specific Metrics including AI cost metrics and usage-based pricing metrics; Data Health Scoring with a formula and targets; Attribution Models with model options and lookback windows; Dashboard Architecture with a three-tier hierarchy (Board, Executive, Operator) and tool recommendations; Leading vs. Lagging indicators; Weekly GTM Review Cadence; and PQL Scoring. The instructions are to describe these elements and their concrete components as stated in the material.
When to use it
Use when the user seeks to define GTM metrics, build a metrics dashboard, measure pipeline efficiency, or track AI product performance, and when they mention GTM metrics, revenue latency, pipeline metrics, TTFV, CAC, LTV, NRR, GTM dashboard, magic number, pipeline velocity, funnel metrics, etc. It is not to be used for technical implementation or code review.
What it can touch
The skill lists declared tools: claude-code, copilot, cursor. It specifies dashboards and metrics touchpoints but does not authorize executing data pulls or changes; the content implies design and governance touchpoints rather than direct data operations. No scripts or commands are provided to run.
Caveats
It states not to use for technical implementation, code review, or software architecture. It frames AI-specific cost metrics and attribution considerations, but does not guarantee outcomes or implementation details beyond the descriptive framework. Licensing is NOASSERTION in the metadata; explicit reuse rights are not stated here.
# GTM Metrics, Dashboards & Measurement for AI Products 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. ## Before Starting Gather this context before building any metrics framework, dashboard, or measurement plan: - What is the current sales motion? PLG, sales-led, agent-led, or hybrid. - What is the pricing model? Per-seat, usage-based, outcome-based, or hybrid. - What is the current ARR or MRR? Stage determines which benchmarks apply. - What CRM and data tools are in use? HubSpot, Salesforce, Attio, or spreadsheets. - What analyti
- Before Starting
- 1. Core GTM Metrics Dashboard
- Revenue Metrics
- Efficiency Metrics
- Pipeline Metrics
- Retention Metrics
- NRR Benchmarks by Stage
- Growth Rate Benchmarks
- 2. Funnel Metrics by GTM Motion
- PLG Funnel
- Sales-Led Funnel
- Agent-Led Funnel (AI SDR)
- 3. AI Product-Specific Metrics
- AI Cost Metrics
What does the gtm-metrics skill do?
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,
How do I install it?
Run `npx skills add tech-leads-club/agent-skills --skill gtm-metrics --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From tech-leads-club/agent-skills, a repository with 4,983 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.