Token导航 LogoToken导航TokenDH.com
研究检索需要联网github未标认证来源可访问clear审计通过

financial-unit-economics金融单位经济学

Agent Skill

financial-unit-economics 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

2,376

周安装

98

GitHub Stars

85

下载量

776
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:financial-unit-economics(金融单位经济学)
来源仓库:https://github.com/lyndonkl/claude
仓库路径:skills/financial-unit-economics
安装命令:
npx skills add https://github.com/lyndonkl/claude --skill financial-unit-economics
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/lyndonkl/claude --skill financial-unit-economics

简介

financial-unit-economics 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。

  • 可结合来源仓库、安装命令和原始 README 继续核验具体用法。
  • 安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。
  • 当前顶部介绍为空,需参考原始 SKILL.md 获取详细功能说明。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

Financial Unit Economics

Table of Contents

Example

Scenario: SaaS startup, $100/month subscription

  • CAC: $20k spend / 100 customers = $200
  • Gross margin: ($100 - $20 variable) / $100 = 80%
  • Monthly churn: 5% -> Average lifetime = 20 months
  • LTV: $100 x 20 months x 80% = $1,600
  • LTV/CAC: 8:1 (healthy, >3:1), Payback: 2.5 months (good, <12 months)
  • Interpretation: Strong unit economics. Can profitably scale marketing spend.

Workflow

Copy this checklist and track your progress:

Unit Economics Analysis Progress:
- [ ] Step 1: Define the unit
- [ ] Step 2: Calculate CAC
- [ ] Step 3: Calculate LTV
- [ ] Step 4: Assess contribution margin
- [ ] Step 5: Analyze cohorts
- [ ] Step 6: Interpret and recommend

Step 1: Define the unit

What is your unit of analysis? (Customer, product SKU, transaction, subscription). See resources/template.md.

Step 2: Calculate CAC

Total acquisition costs (sales + marketing) ÷ new units acquired. Break down by channel if applicable. See resources/template.md and resources/methodology.md.

Step 3: Calculate LTV

Revenue over unit lifetime minus variable costs. Use cohort data for retention/churn. See resources/template.md and resources/methodology.md.

Step 4: Assess contribution margin

(Revenue - Variable Costs) ÷ Revenue. Identify levers to improve margin. See resources/template.md and resources/methodology.md.

Step 5: Analyze cohorts

Track retention, LTV, payback by customer cohort (acquisition month/channel/segment). See resources/template.md and resources/methodology.md.

Step 6: Interpret and recommend

Assess LTV/CAC ratio, payback period, cash efficiency. Make recommendations (pricing, channels, growth). See resources/template.md and resources/methodology.md.

Validate using resources/evaluators/rubric_financial_unit_economics.json. Minimum standard: Average score ≥ 3.5.

Common Patterns

Pattern 1: SaaS Subscription Model

  • Key metrics: MRR, ARR, churn rate, LTV/CAC, payback period, CAC payback
  • Calculation: LTV = ARPU × Gross Margin % ÷ Churn Rate
  • Benchmarks: LTV/CAC ≥3:1, Payback <12 months, Churn <5% monthly (B2C) or <2% (B2B)
  • Levers: Reduce churn (increase LTV), upsell/cross-sell (increase ARPU), optimize channels (reduce CAC)
  • When: Subscription business, recurring revenue, retention critical

Pattern 2: E-commerce / Transactional

  • Key metrics: AOV (Average Order Value), repeat purchase rate, contribution margin per order, CAC
  • Calculation: LTV = AOV × Purchase Frequency × Gross Margin % × Customer Lifetime (years)
  • Benchmarks: Contribution margin ≥40%, Repeat purchase rate ≥25%, LTV/CAC ≥2:1
  • Levers: Increase AOV (bundling, upsells), drive repeat purchases (loyalty programs), reduce variable costs
  • When: Transactional business, e-commerce, retail

Pattern 3: Marketplace / Platform

  • Key metrics: Take rate, GMV (Gross Merchandise Value), supply/demand CAC, liquidity
  • Calculation: LTV = GMV per user × Take Rate × Gross Margin % ÷ Churn Rate
  • Benchmarks: Take rate 10-30%, LTV/CAC ≥3:1 for both sides, network effects kicking in
  • Levers: Increase take rate (value-added services), improve matching (increase GMV), balance supply/demand
  • When: Two-sided marketplace, platform business

Pattern 4: Freemium / PLG (Product-Led Growth)

  • Key metrics: Free-to-paid conversion rate, time to convert, paid user LTV, blended CAC
  • Calculation: Blended LTV = (Free users × Conversion % × Paid LTV) - (Free user costs)
  • Benchmarks: Conversion ≥2%, Time to convert <90 days, Paid LTV/CAC ≥4:1
  • Levers: Increase conversion rate (improve product, optimize paywall), reduce time to value, lower CAC via virality
  • When: Product-led growth, freemium model, viral product

Pattern 5: Enterprise / High-Touch Sales

  • Key metrics: CAC (including sales team costs), sales cycle length, NRR (Net Revenue Retention), LTV
  • Calculation: LTV = ACV (Annual Contract Value) × Gross Margin % × Average Customer Lifetime (years)
  • Benchmarks: LTV/CAC ≥3:1, Sales efficiency (ARR added ÷ S&M spend) ≥1.0, NRR ≥110%
  • Levers: Shorten sales cycle, increase ACV (upsell, premium tiers), improve retention (NRR)
  • When: Enterprise sales, high ACV, long sales cycles

Guardrails

  1. Fully-loaded CAC: Include all acquisition costs (sales salaries, marketing spend, tools, overhead allocation). Excluding sales team salaries is a common miss that inflates perceived economics.
  2. True variable costs: Only include costs that scale with each unit (COGS, hosting per user, transaction fees). Exclude fixed costs (rent, core engineering). Accurate margins are essential for LTV.
  3. Cohort-based LTV: Early cohorts are not the same as recent cohorts. Track retention curves by cohort. Base LTV on observed retention, not assumptions.
  4. Use conservative time horizons: LTV is a prediction. For new products with limited data, weight recent cohorts more heavily and avoid projecting far beyond observed behavior.
  5. Optimize both payback and LTV/CAC: High LTV/CAC but long payback (>18 months) strains cash. Fast payback (<6 months) allows rapid reinvestment.
  6. Analyze at channel level: Blended metrics hide the truth. CAC and LTV vary by channel (paid search vs. referral vs. content). Break down separately to optimize spend.
  7. Retention drives LTV exponentially: Improving monthly churn from 5% to 4% increases LTV by 25%. Retention improvements typically matter more than acquisition improvements.
  8. Gross margin floor: SaaS needs >=60% gross margin, e-commerce >=40%, to be viable. Low margin means even high LTV/CAC ratios yield poor cash flow.

Common pitfalls:

  • Ignoring churn: Assuming customers stay forever. Reality: churn compounds. Use cohort retention curves.
  • Vanity LTV: Using unrealistic retention (e.g., 5 year LTV with 1 month of data). Stick to observed behavior.
  • Blended CAC: Mixing profitable and unprofitable channels. Break down by channel, segment, cohort.
  • Not updating: Unit economics change as product, market, competition evolve. Re-calculate quarterly.
  • Missing costs: Forgetting support costs, payment processing fees, fraud losses, refunds. Track everything.
  • Premature scaling: Growing before unit economics work (LTV/CAC <2:1). "We'll make it up in volume" rarely works.

Quick Reference

Key formulas:

CAC = (Sales + Marketing Costs) ÷ New Customers Acquired

LTV (subscription) = ARPU × Gross Margin % ÷ Monthly Churn Rate

LTV (transactional) = AOV × Purchase Frequency × Gross Margin % × Lifetime (years)

Contribution Margin % = (Revenue - Variable Costs) ÷ Revenue

LTV/CAC Ratio = Lifetime Value ÷ Customer Acquisition Cost

Payback Period (months) = CAC ÷ (Monthly Revenue × Gross Margin %)

CAC Payback (months) = S&M Spend ÷ (New ARR × Gross Margin %)

Gross Margin % = (Revenue - COGS) ÷ Revenue

Customer Lifetime (months) = 1 ÷ Monthly Churn Rate

MRR (Monthly Recurring Revenue) = Sum of all monthly subscriptions

ARR (Annual Recurring Revenue) = MRR × 12

ARPU (Average Revenue Per User) = Total Revenue ÷ Total Users

NRR (Net Revenue Retention) = (Starting ARR + Expansion - Contraction - Churn) ÷ Starting ARR

Benchmarks (varies by stage and industry):

MetricGoodAcceptablePoor
LTV/CAC Ratio≥5:13:1 - 5:1<3:1
Payback Period<6 months6-12 months>18 months
Gross Margin (SaaS)≥80%60-80%<60%
Gross Margin (E-commerce)≥50%40-50%<40%
Monthly Churn (B2C SaaS)<3%3-7%>7%
Monthly Churn (B2B SaaS)<1%1-3%>3%
CAC Payback (SaaS)<12 months12-18 months>18 months
NRR (SaaS)≥120%100-120%<100%

Decision framework:

LTV/CACPaybackRecommendation
<1:1AnyStop: Losing money on every customer. Fix model or pivot.
1:1 - 2:1>12 monthsCaution: Marginal economics. Don't scale yet. Improve retention or reduce CAC.
2:1 - 3:16-12 monthsOptimize: Unit economics acceptable. Focus on improving before scaling.
3:1 - 5:1<12 monthsScale: Good economics. Can profitably invest in growth.
>5:1<6 monthsAggressive scale: Excellent economics. Raise capital, increase spend rapidly.

Inputs required:

  • Revenue data: Pricing, ARPU, AOV, transaction frequency
  • Cost data: Sales/marketing spend, COGS, variable costs per customer
  • Retention data: Churn rate, cohort retention curves, repeat purchase behavior
  • Channel data: CAC by acquisition channel, LTV by segment
  • Time period: Cohort definition (monthly, quarterly), historical data range

Outputs produced:

  • unit-economics-analysis.md: Full analysis with CAC, LTV, ratios, cohort breakdowns
  • cohort-retention-table.csv: Retention curves by cohort
  • channel-profitability.csv: CAC and LTV by acquisition channel
  • recommendations.md: Pricing, channel, growth recommendations based on metrics

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Claude Code

32.21%
按下载量换算250

Gemini CLI

24.25%
按下载量换算188

Antigravity

16.74%
按下载量换算130

windsurf

13.85%
按下载量换算107

OpenCode

7.75%
按下载量换算60

Cursor

3.72%
按下载量换算29

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。

来源信息

继续浏览同类 Skills