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creating-financial-models — Claude Code skill: DCF, scenarios, risk

Skill for building DCF models, sensitivity analysis, Monte Carlo simulations, and scenario planning for investment decisions. Use it for M&A, project finance, and LBO valuation.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add anthropics/claude-cookbooks --skill creating-financial-models -a claude-code
Open on GitHub

What creating-financial-models does

The skill provides a complete financial modeling toolkit: DCF analysis with WACC and terminal value calculation, sensitivity testing through data tables and tornado charts, probability distributions in Monte Carlo (1,000–10,000 iterations), and best/base/worst scenario comparison. It handles corporate valuation, project finance, M&A, LBO, and supports both mature stable-cash-flow companies and growth companies with J-curve projections.

Includes two Python scripts: dcf_model.py for DCF engine and sensitivity_analysis.py for sensitivity testing. Requires historical financial statements (3–5 years), revenue and margin projections, capital expenditure forecasts, and discount rate components (risk-free rate, beta, market premium). For Monte Carlo, you need probability distributions for uncertain variables and iteration count (typically 1,000–10,000).

Models auto-validate balance sheet balancing, reconcile cash flows, resolve circular references, and check statistical bounds. Outputs include complete financial projections, sensitivity tables, probability distributions with confidence intervals, and scenario comparison. Methodology reflects current financial theory and industry best practices.

What it helps with

  • build a DCF model with enterprise and equity value calculation
  • test impact of WACC and growth rate changes on valuation
  • model uncertainty in key assumptions using 5,000 Monte Carlo scenarios
  • compare probability of success across acquisition or LBO structures
  • identify critical value drivers with tornado chart ranking

Example prompts for the agent

  • Build a DCF model for this tech company using the attached financial statements
  • Run a Monte Carlo simulation on this acquisition model with 5,000 iterations and calculate the 95% confidence interval
  • Create sensitivity analysis showing how growth rate and WACC changes affect enterprise value

How to install creating-financial-models

1Skills CLI (Vercel)

npx skills add anthropics/claude-cookbooks --skill creating-financial-models -a claude-code

2Manually: put the files in place

~/.claude/skills/creating-financial-models/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/creating-financial-models/

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Questions about creating-financial-models

How do I install creating-financial-models in Claude Code?

Run two commands: npx skills add anthropics/claude-cookbooks --skill creating-financial-models -a claude-code, then refer to ~/.claude/skills/creating-financial-models/SKILL.md to initialize.

What does creating-financial-models do?

It provides financial modeling tools: DCF analysis with WACC and terminal value, sensitivity analysis to rank value drivers, Monte Carlo simulations for risk assessment, and scenario planning to compare best/base/worst outcomes.

What inputs does DCF analysis require?

You need historical financial statements (3–5 years), revenue growth and margin projections, capital expenditure and working capital forecasts, terminal growth rate or exit multiple, and discount rate components (risk-free rate, beta, market premium).

Source text

SKILL.md · MIT
---
name: creating-financial-models
description: This skill provides an advanced financial modeling suite with DCF analysis, sensitivity testing, Monte Carlo simulations, and scenario planning for investment decisions
---

# 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

© anthropics · MIT · anthropics/claude-cookbooks

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