sku-profitabilitylisted

# SKU profitability > **Generated skill (example).** This is the kind of bespoke skill `fpa-learn-business` > proposes when the business profile says *'product company with a discrete SKU set.'* > It lives in `skills/generated/` - in a real engagement it would be written into the > client's repo after human approval, citing the profile facts that justify it (here: > a limited-SKU D2C brand where per-product economics drive the mix decision). ## Overview The channel-level forecast tells you the business is healthy; it doesn't tell you *which products* carry it. This skill computes per-SKU economics and the Pareto curve so you can see the 80/20, find margin-dilutive SKUs, and make cut/reprice/push calls. **Core principle:** Revenue flatters; margin and contribution decide. Rank by gross profit, not by sales. ## When to use - 'Which products actually make money?' / 'what should we cut?' - Product-mix, pricing, or assortment-rationalization decisions - Any product business with a discrete SKU set (especially limited-SKU brands) ## Workflow 1. **Load the SKUs** (annual units, price, unit cost): ```python import pyfpa skus = pyfpa.load_skus("examples/ridgeline/skus.yaml") # or build [Sku(...)] inline df = pyfpa.sku_profitability(skus) ``` `df` is sorted by revenue (desc) so the Pareto columns read top-down, indexed by SKU, with columns: `units, revenue, cogs, gross_profit, gross_margin, revenue_share, cumulative_revenue_pct`. For a profit ranking,