dcf-modellisted

## Environment This skill assumes **headless openpyxl** — you are producing an .xlsx file on disk. Follow the `excel-author` skill's conventions for cell coloring, formulas, named ranges, and sensitivity tables. Recalculate before delivery: `python /path/to/excel-author/scripts/recalc.py ./out/model.xlsx`. # DCF Model Builder ## Overview This skill creates institutional-quality DCF models for equity valuation following investment banking standards. Each analysis produces a detailed Excel model (with sensitivity analysis included at the bottom of the DCF sheet). ## Tools - Default to using all of the information provided by the user and MCP servers available for data sourcing. ## Critical Constraints - Read These First These constraints apply throughout all DCF model building. Review before starting: **Formulas Over Hardcodes (NON-NEGOTIABLE):** - Every projection, margin, discount factor, PV, and sensitivity cell MUST be a live Excel formula — never a value computed in Python and written as a number - When using openpyxl: `ws["D20"] = "=D19*(1+$B$8)"` is correct; `ws["D20"] = calculated_revenue` is WRONG - The only hardcoded numbers permitted are: (1) raw historical inputs, (2) assumption drivers (growth rates, WACC inputs, terminal g), (3) current market data (share price, debt balance) - If you catch yourself computing something in Python and writing the result — STOP. The model must flex when the user changes an assumption. **Verify Step-by-Step With the User (DO