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Agent Skill · 研究检索

financial-unit-economics(金融单位经济学)

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。

financial-unit-economics 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证为 MIT,第三方扫描已通过,按安装量在研究检索类的 73,674 个 Skill 里排第 33,257。

总安装 2,376周安装 98GitHub Stars 85下载 776核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT

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

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

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。

来源仓库内路径:skills/financial-unit-economics

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/lyndonkl/claude --skill financial-unit-economi…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

financial-unit-economics 怎么安装?

在终端执行 npx skills add https://github.com/lyndonkl/claude --skill financial-unit-economics。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

financial-unit-economics 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

financial-unit-economics 需要什么权限?

权限级别是需要联网。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。

financial-unit-economics 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

financial-unit-economics 用的人多吗?

公开数据是累计安装 2,376 次,近一周安装 98 次,下载 776 次,GitHub 85 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

financial-unit-economics 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
financial-unit-economics

金融单位经济学 · 本页

2,37685需要联网MIT通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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

平台分布

Claude Code

32.21%
按下载量换算250

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24.25%
按下载量换算188

Antigravity

16.74%
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windsurf

13.85%
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7.75%
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Cursor

3.72%
按下载量换算29

安全审计

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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。

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