Set up the Creating Financial Models Skill

Overview of the skill

Creating Financial Models Skill is a skill from Anthropic's official claude-cookbooks repository for Claude that provides a comprehensive financial modeling toolkit. According to the skill description, it covers DCF analysis, sensitivity testing, Monte Carlo simulations, and scenario planning for investment decisions. DCF stands for Discounted Cash Flow, a valuation method in which a company's expected future cash flows are discounted back to their present value to derive an enterprise value. The skill thus covers four classic building blocks of financial analysis that are frequently combined in practice.

The four core areas in detail

According to the description, DCF analysis enables building complete models with multiple growth scenarios, calculating terminal values via the perpetuity growth method or an exit multiple method, and determining the weighted average cost of capital, known as WACC. Sensitivity analysis tests how strongly changes in individual assumptions affect the valuation outcome, including via so-called tornado charts that visually rank the most influential value drivers by impact. Monte Carlo simulation runs thousands of random scenarios based on stored probability distributions to model uncertainty in key inputs and to calculate confidence intervals and the probability of reaching specific target values from that. Scenario planning, finally, compares best-case, base-case, and worst-case assumptions and different economic environments, weighting outcomes by their probability of occurring.

Required input data

For a DCF analysis, the skill needs historical financial figures over typically three to five years, revenue growth assumptions, operating margin and capital expenditure projections, working capital assumptions, and either a terminal growth rate or an exit multiple, along with the components of the discount rate such as the risk-free rate, beta, and market risk premium. For sensitivity analysis, an existing base case model serves as the starting point, supplemented with the value ranges to be tested and the metrics to track. Monte Carlo simulation needs probability distributions for uncertain variables, correlation assumptions between those variables, and the number of simulation iterations, which per the description typically ranges from one thousand to ten thousand. Scenario planning needs clearly defined scenarios with assumptions, probability weights, and the key metrics to follow.

Output formats and supported model types

As output, the skill produces complete financial projections, free cash flow calculations, the terminal value derivation, a summary of enterprise and equity value, and implied valuation multiples, packaged as a complete Excel workbook. Sensitivity analysis produces value-range tables, tornado charts, and break-even analyses. Monte Carlo simulation delivers probability distributions of valuation outcomes, confidence intervals, and risk metrics such as value at risk. Per the description, supported model types include corporate valuations for mature and high-growth companies as well as turnaround situations, project finance for infrastructure, real estate, or energy projects, M&A analysis including synergy modeling, and leveraged buyout models with returns analysis.

Best practices and security considerations

According to the description, the skill follows established modeling standards: consistent formatting and structure, clearly documented assumptions, a clean separation between inputs, calculations, and outputs, and built-in error checking. On the valuation side, the recommendation is to use multiple valuation methods to cross-check each other, apply appropriate risk adjustments, incorporate market comparables, and validate results against trading multiples. What matters most in practice is that any financial model is only as good as its input assumptions: a model can be arithmetically correct and still lead to wrong conclusions if the underlying growth or margin assumptions are unrealistic. Anyone using the skill for real investment decisions should therefore always treat the results as decision support rather than a sole basis, supplementing them with independent professional review where needed, especially for regulatory-relevant valuations.

Limits of the tool

The skill automates the computational and structural side of financial modeling but does not replace professional judgment on whether the input assumptions are realistic, which market data is currently relevant, or which regulatory requirements apply in a given context. For binding investment decisions, especially in a regulated financial environment, involving qualified human expertise remains indispensable.