DCF Equity Valuation Modeler
Builds auditable DCF equity valuations with WACC, cash-flow forecasts, scenarios, and sensitivity analysis.
The skill requires staged user confirmation, formula-driven calculations, source comments, and terminal growth below WACC, showing some data-flow transparency and human review. Points are deducted because least privilege, sensitive financial-data handling, authorization for external MCP/web access, rollback, and delivery consent are not clearly specified; source URLs are optional.
The documentation provides a detailed DCF workflow, formula rules, layout planning, troubleshooting guidance, and a validation script, making the happy path plausible. Points are deducted because the supplied files do not prove execution of key paths; the referenced recalc.py is not included, dependency setup and recovery are incomplete, and the scenario-selection guidance shifts from IF statements to INDEX formulas.
The target audience, DCF valuation use case, input sources, Excel output, and sensitivity-analysis scope are reasonably clear, with some special cases documented. Points are deducted because exclusion criteria, industry boundaries, abnormal-input behavior, and Chinese-language support are not explicit; core sourcing may depend on overseas MCP/web services requiring subscriptions or API keys, with no mainland-China reachability guidance.
The skill uses layered overview, constraints, workflow, correct-pattern, mistake, and troubleshooting sections; the repository also supplies an Apache-2.0 license, CI validation, and official-organization provenance. Points are deducted because the skill lacks its own version, changelog, named maintenance responsibility, dependency installation notes, and stable update path, while referenced dependencies such as recalc.py are not confirmed in the supplied material.
The documentation covers historical analysis, projections, FCF, WACC, terminal value, the equity bridge, and three sensitivity tables, and specifies professional Excel and CSV-style outputs. Static calibration limits this to 7; points are deducted because no actual model, representative output, or execution evidence is provided, so correctness and marginal benefit over manual work cannot be verified.
The source includes detailed formulas, a validation script, error checks, and repository-level plugin-validation and secret-scan workflows, providing some auditability. Points are deducted because there are no executed results, committed tests covering the DCF key paths, or independent third-party corroboration, so stronger verification scores are unsupported.
- Before delivery, confirm external-data permissions, API access, sensitive-financial-data boundaries, and traceability of every dated input.
- Do not assume that recalc.py or an openpyxl environment exists; verify dependencies, recalculation capability, and formula-error cache behavior first.
- For mainland-China use, verify network reachability and compliance of required MCP/web data services because core data retrieval may otherwise be unavailable.
What it does & when to use it
This skill builds institutional-style discounted cash flow models for equity valuation. It can use user-provided information, MCP servers, SEC filings, analyst reports, and web data to assemble historical inputs, forecasts, WACC, terminal value, and an equity-value bridge. Its intended deliverable is a formula-driven Excel model with Bear, Base, and Bull scenarios and sensitivity tables. It is designed for analyst work product that requires professional review, not for investment advice or autonomous trading decisions.
Reads user-provided data and available MCP, SEC, analyst-report, and web sources; validates net debt or net cash, diluted shares, historical margins, and tax assumptions; builds Bear, Base, and Bull projections for revenue, operating expenses, EBIT, NOPAT, D&A, CapEx, working capital, and unlevered free cash flow; calculates WACC using CAPM, debt costs, and capital-structure weights; discounts forecast cash flows using a mid-year convention; calculates terminal value using a preferred perpetuity-growth method or an exit-multiple alternative; bridges enterprise value to equity value and implied share price; produces an Excel model with three sensitivity tables and runs recalc.py to check formula errors.
- An investment-banking analyst needs a five-year DCF with terminal value, WACC, and implied share price for a public company.
- An equity researcher wants to refresh historical inputs and compare Bear, Base, and Bull growth and margin scenarios.
- A private-equity or corporate-finance team needs an enterprise-to-equity bridge using net debt, diluted shares, and CAPM-based WACC.
- A financial modeler needs an Excel workbook containing full-formula sensitivity grids for WACC, terminal growth, revenue growth, EBIT margin, beta, and the risk-free rate.
Pros & cons
- Covers the full DCF workflow from data validation through implied per-share value.
- Requires projections, discounting, terminal value, and sensitivity cells to remain live Excel formulas.
- Includes Bear, Base, and Bull scenarios, CAPM-based WACC, and three sensitivity analyses.
- Requires source comments for hardcoded inputs and formula-error checks before delivery.
- It depends on available financial data sources, network access, or MCP connections; some providers may require subscriptions or API keys.
- Workbook creation differs by environment: Python/openpyxl for standalone files and Office JS inside Excel.
- The supplied source does not include a test suite, specific provider configuration, or the full implementation of recalc.py.
- The workflow requires staged user confirmation, so it is not designed as an entirely unattended end-to-end build.
How to install
In Claude Code, add and install the financial-analysis plugin: claude plugin marketplace add anthropics/financial-services, then run claude plugin install financial-analysis@claude-for-financial-services. In Cowork, use Settings → Plugins → Add plugin and paste https://github.com/anthropics/financial-services, then select the financial-analysis vertical plugin. The skill source is plugins/vertical-plugins/financial-analysis/skills/dcf-model/SKILL.md.
How to use
After installation, trigger the workflow with /dcf, or ask for a request such as: “Build a five-year DCF model for ABC using Bear, Base, and Bull scenarios, with WACC, terminal value, and sensitivity analysis in Excel.” Provide a company name or ticker, historical financials, and growth assumptions; projection period, terminal growth, and WACC inputs are optional. The workflow calls for confirmation after data retrieval, revenue projections, FCF construction, WACC, terminal value, and the valuation bridge.
Compared to similar skills
Methodologically, the skill prefers the perpetuity-growth terminal-value method and also supports an exit-multiple method. The exit-multiple alternative requires industry comparable-company or precedent-transaction multiples.