fpa-scaffold-modellisted

# Scaffold a Model (Phase 1) ## Overview Turn a company's financials into a runnable `pyfpa` config. Read the business profile first (see **fpa-learn-business**), infer the chart-of-accounts → model-line mapping, and write a validated `EntityConfig` YAML following openfpa conventions. Output a runnable skeleton plus an explicit list of assumptions to confirm. **Core principle:** Convention over invention. Map the real numbers onto the existing engine shape; don't design a new one. ## When to use - A trial balance / P&L (CSV, XLSX, or pasted) needs to become a forecast model - Onboarding follow-on after `.fpa/business-profile.md` exists ## Workflow 1. **Ingest** the financials: `pyfpa.read_pl_csv(path)` (or a `pyfpa.io.adapters` source) → `{account: amount}`. 2. **Map accounts to model lines** of the `EntityConfig` schema: - revenue accounts → `channels[]` (one `Channel` per channel/segment, with `annual_revenue`, a 12-month `seasonality` weight list, `growth_rate`, `cogs_pct`) - cost accounts → `opex[]` as `OpexLine(kind="fixed", monthly_amount=…)` or `kind="variable", pct_of_revenue=…` - debt → `debt[]` (`term_loan` with `monthly_principal`, or interest-only `loc`) - balance-sheet rhythm → `working_capital(dso_days, dpo_days, dio_days)` and `opening_balances` 3. **Write** the company model and config under `models/generated/`. Validate config with `pyfpa.load_config(path)`, which raises on any bad field. 4. **Create a runnable command** such as `pyth