creating-financial-models

anthropics/claude-cookbooks · Agent Skill

建立高階財務模型，包括貼現現金流分析、敏感性測試和蒙特卡洛模擬，以做出明智的投資決策。

前往安裝 ↓

總覽

發布者anthropics組織
儲存庫anthropics/claude-cookbooks ↗
技能資料夾creating-financial-models
星52,543
附帶檔案1
授權MIT
最近更新2025年11月27日
連結技能原始碼 ↗ · 儲存庫 ↗

Mozgi 檢查

我們在技能檔案中發現的內容。詳細資訊需訂閱。

安全性
低風險 · 未發現明顯風險 — 完整報告會顯示細節
相容性
僅含說明 — 適用於多數代理
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安裝

技能就是一個含 SKILL.md 的資料夾。選擇你的代理，在專案根目錄的終端機（macOS、Linux 或 Windows 的 Git Bash）執行指令。

把提示貼到 Claude、Codex 或其他助理：它會自行檢查並安裝技能。

Install the “creating-financial-models” skill from mozgi.io (https://mozgi.io/skills/creating-financial-models/) for me.

1. Download the archive: https://mozgi.io/skills/creating-financial-models/download
   If it fails, take the skill folder from the source: https://github.com/anthropics/claude-cookbooks/tree/main/skills/custom_skills/creating-financial-models
2. Read SKILL.md and the other files. Check the skill is safe: no hidden commands, no sending data elsewhere, no scripts doing something other than described. If anything looks suspicious, stop and tell me.
3. Put the “creating-financial-models” folder into your agent's skills folder: .claude/skills/ for Claude Code, .agents/skills/ for Codex, .cursor/skills/ for Cursor, .gemini/skills/ for Gemini CLI. Inside the project if we are in one, otherwise in the home folder (~/.claude/skills/ etc.).
4. Make sure <skills folder>/creating-financial-models/SKILL.md exists.
5. Briefly tell me what this skill does and offer to try it on my task.

If you cannot do a step yourself, tell me what to click or type.

技能檔案

1 個檔案 下載 ZIP

安裝前請先檢視 SKILL.md 與相關檔案。點擊檔案即可預覽。

SKILL.md 內容

作者原文 · 唯讀

Financial Modeling Suite

A comprehensive financial modeling toolkit for investment analysis, valuation, and risk assessment using industry-standard methodologies.

Core Capabilities

1. Discounted Cash Flow (DCF) Analysis

  • Build complete DCF models with multiple growth scenarios
  • Calculate terminal values using perpetuity growth and exit multiple methods
  • Determine weighted average cost of capital (WACC)
  • Generate enterprise and equity valuations

2. Sensitivity Analysis

  • Test key assumptions impact on valuation
  • Create data tables for multiple variables
  • Generate tornado charts for sensitivity ranking
  • Identify critical value drivers

3. Monte Carlo Simulation

  • Run thousands of scenarios with probability distributions
  • Model uncertainty in key inputs
  • Generate confidence intervals for valuations
  • Calculate probability of achieving targets

4. Scenario Planning

  • Build best/base/worst case scenarios
  • Model different economic environments
  • Test strategic alternatives
  • Compare outcome probabilities

Input Requirements

For DCF Analysis

  • Historical financial statements (3-5 years)
  • Revenue growth assumptions
  • Operating margin projections
  • Capital expenditure forecasts
  • Working capital requirements
  • Terminal growth rate or exit multiple
  • Discount rate components (risk-free rate, beta, market premium)

For Sensitivity Analysis

  • Base case model
  • Variable ranges to test
  • Key metrics to track

For Monte Carlo Simulation

  • Probability distributions for uncertain variables
  • Correlation assumptions between variables
  • Number of iterations (typically 1,000-10,000)

For Scenario Planning

  • Scenario definitions and assumptions
  • Probability weights for scenarios
  • Key performance indicators to track

Output Formats

DCF Model Output

  • Complete financial projections
  • Free cash flow calculations
  • Terminal value computation
  • Enterprise and equity value summary
  • Valuation multiples implied
  • Excel workbook with full model

Sensitivity Analysis Output

  • Sensitivity tables showing value ranges
  • Tornado chart of key drivers
  • Break-even analysis
  • Charts showing relationships

Monte Carlo Output

  • Probability distribution of valuations
  • Confidence intervals (e.g., 90%, 95%)
  • Statistical summary (mean, median, std dev)
  • Risk metrics (VaR, probability of loss)

Scenario Planning Output

  • Scenario comparison table
  • Probability-weighted expected values
  • Decision tree visualization
  • Risk-return profiles

Model Types Supported

1. Corporate Valuation

  • Mature companies with stable cash flows
  • Growth companies with J-curve projections
  • Turnaround situations

2. Project Finance

  • Infrastructure projects
  • Real estate developments
  • Energy projects

3. M&A Analysis

  • Acquisition valuations
  • Synergy modeling
  • Accretion/dilution analysis

4. LBO Models

  • Leveraged buyout analysis
  • Returns analysis (IRR, MOIC)
  • Debt capacity assessment

Best Practices Applied

Modeling Standards

  • Consistent formatting and structure
  • Clear assumption documentation
  • Separation of inputs, calculations, outputs
  • Error checking and validation
  • Version control and change tracking

Valuation Principles

  • Use multiple valuation methods for triangulation
  • Apply appropriate risk adjustments
  • Consider market comparables
  • Validate against trading multiples
  • Document key assumptions clearly

Risk Management

  • Identify and quantify key risks
  • Use probability-weighted scenarios
  • Stress test extreme cases
  • Consider correlation effects
  • Provide confidence intervals

Example Usage

"Build a DCF model for this technology company using the attached financials"

"Run a Monte Carlo simulation on this acquisition model with 5,000 iterations"

"Create sensitivity analysis showing impact of growth rate and WACC on valuation"

"Develop three scenarios for this expansion project with probability weights"

Scripts Included

  • dcf_model.py: Complete DCF valuation engine
  • sensitivity_analysis.py: Sensitivity testing framework

Limitations and Disclaimers

  • Models are only as good as their assumptions
  • Past performance doesn't guarantee future results
  • Market conditions can change rapidly
  • Regulatory and tax changes may impact results
  • Professional judgment required for interpretation
  • Not a substitute for professional financial advice

Quality Checks

The model automatically performs:

1. Balance sheet balancing checks

2. Cash flow reconciliation

3. Circular reference resolution

4. Sensitivity bound checking

5. Statistical validation of Monte Carlo results

Updates and Maintenance

  • Models use latest financial theory and practices
  • Regular updates for market parameter defaults
  • Incorporation of regulatory changes
  • Continuous improvement based on usage patterns

常見問題

creating-financial-models 技能有什麼用途？

建立高階財務模型，包括貼現現金流分析、敏感性測試和蒙特卡洛模擬，以做出明智的投資決策。

如何安裝 creating-financial-models？

打開本頁的「安裝」區塊並選擇你的代理 — Claude Code、Codex、Cursor 或 Gemini CLI：那裡有把技能資料夾複製到正確位置的現成指令。也可以複製「透過助理」分頁中的提示，讓助理自行檢查並安裝技能。

creating-financial-models 適用哪些 AI 模型？

技能是純 Markdown 指示（Agent Skills 格式），因此 creating-financial-models 適用任何讀取 SKILL.md 的代理：Claude Code、Codex、Cursor、Gemini CLI、GitHub Copilot、OpenCode 等。只有在任務符合技能描述時，模型才會載入指示。

creating-financial-models 是免費的嗎？

此技能由 anthropics 公開發布，採用 MIT 授權。技能本身免費 — 你只需向 AI 供應商支付實際使用的 token 費用。

安裝技能安全嗎？

技能是代理會遵循的指示，安裝前請閱讀 SKILL.md 及相關檔案。技能資料夾中的腳本只有在代理執行時才會運行 — 留意它的動作，不要授予多餘權限。

技能和 MCP 伺服器或外掛有何不同？

MCP 伺服器或外掛為模型提供新工具 — 會執行並回傳結果的程式碼。技能提供的是知識與流程：如何處理任務、要遵循哪些步驟、好的成果長什麼樣子。它不需要伺服器或 API 金鑰。

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