Can this skill generate actual Excel files?

Yes, the skill is designed to produce structured financial projections that can be exported into Excel workbooks with full modeling logic.

Does the skill handle financial validation?

Yes, it automatically performs quality checks including balance sheet balancing, cash flow reconciliation, and circular reference resolution.

What kind of risk analysis does the suite perform?

It performs both Sensitivity Analysis (testing variable impact) and Monte Carlo Simulations (running thousands of iterations to determine probability distributions).

Is it suitable for M&A and LBO modeling?

Absolutely. It includes specific support for M&A synergy modeling and Leveraged Buyout (LBO) returns analysis, including IRR and MOIC calculations.

Financial Modeling Suite

Name: Financial Modeling Suite
Author: Microck

byMicrock

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Data Science & ML

Generates professional financial models using DCF analysis, Monte Carlo simulations, and scenario planning for investment decision-making.

About

The Financial Modeling Suite is a comprehensive toolkit designed to streamline complex valuation and risk assessment tasks within Claude. It provides industry-standard methodologies for Discounted Cash Flow (DCF) analysis, including WACC and terminal value calculations, alongside advanced statistical tools like Monte Carlo simulations for quantifying uncertainty. This skill is ideal for analysts and developers needing to build robust corporate valuations, LBO models, or project finance assessments while maintaining high standards for error checking, documentation, and sensitivity testing.

Key Features

Automated DCF analysis with multiple growth and exit multiple scenarios
Monte Carlo simulations for probabilistic risk and confidence interval assessment
Integrated Python scripts for programmatic valuation and balance sheet reconciliation
81 GitHub stars
Multi-case scenario planning (Best/Base/Worst) with probability weighting
Dynamic sensitivity testing using data tables and tornado charts

Use Cases

Valuing mature or growth-stage companies using historical financial statements and projections
Evaluating project finance feasibility for infrastructure or real estate developments
Conducting M&A analysis including synergy modeling and accretion/dilution checks

About

Key Features

Automated DCF analysis with multiple growth and exit multiple scenarios
Monte Carlo simulations for probabilistic risk and confidence interval assessment
Integrated Python scripts for programmatic valuation and balance sheet reconciliation
81 GitHub stars
Multi-case scenario planning (Best/Base/Worst) with probability weighting
Dynamic sensitivity testing using data tables and tornado charts

Use Cases

Valuing mature or growth-stage companies using historical financial statements and projections
Evaluating project finance feasibility for infrastructure or real estate developments
Conducting M&A analysis including synergy modeling and accretion/dilution checks