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

Weekly Installs

77

Source

smithery.ai/ski…l-models

First Seen

Mar 6, 2026

Security Audits

SocketPass

Installed on

claude-code52

opencode24

gemini-cli23

codex23

cursor22

github-copilot20