unit-economics-builder

Compute unit economics metrics and flag them as measured or modeled.

Updated Apr 6, 2026
One-click install
npx skills add https://github.com/core-wrk/employee-number-two --skill unit-economics-builder
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: unit-economics-builder
Source: https://github.com/core-wrk/employee-number-two/tree/main/projects/12-financial-modeling/.claude/skills/unit-economics-builder
Command: npx skills add https://github.com/core-wrk/employee-number-two --skill unit-economics-builder

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Early‑stage founders often lack sufficient cohort data to present credible unit economics, leading to inflated metrics that mislead investors. This Skill produces honest, clearly flagged CAC, LTV, LTV/CAC, gross margin, and payback calculations, distinguishing modeled assumptions from measured data.

Core Features & Use Cases

  • Reads revenue‑model, cost‑structure, and hiring‑roadmap outputs to synthesize core financial metrics.
  • Tags every metric as measured or modeled, applies stage‑based framing (pre‑revenue, early‑revenue, emerging, real data).
  • Generates scenario‑based tables (base, bear, bull) and a 20 % sensitivity analysis.
  • Includes an “honesty appendix” that outlines load‑bearing assumptions and data‑collection recommendations.
  • Outputs a ready‑to‑use unit-economics.md file scoped to the financial‑modeling project.

Quick Start

Generate a unit economics report for the current startup using the provided revenue‑model, cost‑structure, and hiring‑roadmap.

Frequently Asked Questions about unit-economics-builder

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I calculate unit economics for a pre-seed or seed startup without cohort data?

Generate startup unit economics by synthesizing revenue, cost, and hiring inputs to compute CAC, LTV, LTV/CAC, gross margin, and payback. The model applies stage-based framing, scenario tables, and sensitivity analysis, outputting a ready-to-use unit-economics.md file for your financial modeling project.

How do I model CAC and LTV sensitivity for an early-stage startup?

Model CAC and LTV sensitivity by generating base, bear, and bull scenario tables alongside a 20% sensitivity analysis. This process flags load-bearing assumptions in an honesty appendix, providing clear payback and LTV/CAC calculations that highlight data collection recommendations for early-stage startups.

What is the best way to present honest startup financial metrics to investors?

Present honest startup financial metrics by tagging every output as either measured or modeled. Including an honesty appendix that outlines load-bearing assumptions and data-collection recommendations prevents inflated metrics, ensuring investors see credible unit economics for pre-revenue or seed companies.

Do I need revenue and cost structure inputs to generate LTV and payback calculations?

Yes, you need revenue-model, cost-structure, and hiring-roadmap inputs to generate LTV and payback calculations. Synthesizing these inputs allows the model to compute gross margin and apply stage-based framing, producing accurate unit economics metrics for early-stage startups.

Can I generate scenario-based financial models for pre-revenue companies?

Yes, you can generate scenario-based financial models for pre-revenue companies by applying stage-based framing. The model computes base, bear, and bull scenarios for CAC and LTV, ensuring pre-revenue startups receive clearly flagged metrics distinguishing modeled assumptions from real data.

Why does my LTV/CAC calculation show inflated metrics for an early-stage startup?

LTV/CAC calculations show inflated metrics when modeled assumptions are not clearly distinguished from measured data. Applying stage-based framing and generating an honesty appendix with sensitivity analysis prevents misleading investors, ensuring credible unit economics for early-stage startups lacking sufficient cohort data.