DCF Model Builder

Builds an institutional-quality DCF as a live Excel workbook: four sheets, three scenarios, three sensitivity grids, and a valuation a portfolio manager can argue with. A model that computes correctly and says nothing about the stock is half the job, so the construction rules and the judgement rules below are one document.

Evidence labels, source tiers, staleness, the readiness posture and the intake limits: .agents/skills/research-conventions/SKILL.md, read before the first figure enters the workbook.

• recalc.py reports errors, the case selector does not move the model, or the valuation looks wrong: read .agents/skills/dcf-model/TROUBLESHOOTING.md.
• The revenue build needs the drivers of a particular sector, or the company is a bank, an insurer, a miner or a REIT: read .agents/skills/dcf-model/references/sector-drivers.md.

Tools

• fundamentals MCP: get_financial_statements, get_financial_ratios, get_growth_metrics, get_historical_valuation; diluted shares come from here
• macro MCP: get_treasury_rates, get_market_risk_premium
• get_company_overview: share price, analyst consensus, earnings surprises, revenue segmentation
• yf_fundamentals MCP: compare_valuations, whose per-symbol record carries the beta field
• yf_analysis MCP: get_growth_estimates for consensus growth, one record per period (0q, +1q, 0y, +1y, +5y, -5y)
• Web search and fetch, and user-provided data, as supplements

Step 0: Before you build