JeffBrines
UserThe FP&A toolbelt for AI coding agents. Claude Code or Codex does the thinking; openfpa gives it a tested finance kernel, durable company memory, and a Karpathy inspired research loop that improves forecasts against your actuals. By Guiderail and Jeff Brines.
Categories
Indexed Skills (13)
segment-rollup
Use when modeling a multi-segment company that discloses segment net sales and segment Adjusted EBITDA (ASU 2023-07) but not segment COGS or operating income - rolls segment P&Ls into a consolidated forecast and reconciles total segment Adjusted EBITDA to the disclosed total. Generated for Fox Factory (PVG/AAG/SSG).
fpa-backtest-learn
Use when you want the model to learn from how its past forecasts actually turned out - scoring forecasts against the company's real actuals, backtesting assumptions on history, and proposing ratified improvements. Runs at/after monthly close.
fpa-board-briefing
Use when producing a board deck, investor update, or CFO briefing from an openfpa forecast - turning model output into a board-ready narrative and exportable artifacts.
fpa-capture-correction
Use when a human reviewing a forecast catches something off ("December always spikes", "you're double-counting deferred revenue", "that Q3 number was a one-time contract") - captures it as a durable, typed correction in the company's memory so future forecasts are grounded by it.
fpa-cash-runway
Use when answering "when do we run out of cash", building a 13-week cash forecast, sizing a credit line, or analyzing near-term liquidity and payment timing in openfpa.
fpa-cfo-judgment
Use when interpreting financial actuals, reviewing margins or cash, drawing conclusions from a P&L or balance sheet, or about to tell someone a number means something - the judgment layer that separates "AI that does math" from "AI that thinks like a CFO."
fpa-configure-actuals
Use when wiring a company's real numbers into an openfpa model - from local spreadsheets (P&L, balance sheet, AR/AP aging, inventory), a live system via MCP or API (QuickBooks, NetSuite), public filings (10-K/10-Q), or anything else. Not married to one source - build the ingestion for whatever the company has; produces one normalized account-amount shape the rest of the toolkit reads.
fpa-learn-business
Use when starting FP&A work for a new company, onboarding a business into openfpa, or asked to "understand my business / set up a model for us" before any forecasting - produces a durable business profile and generates business-specific skills.
fpa-monthly-close
Use when running a month-end close, refreshing a forecast with the latest actuals, computing plan-vs-actual variance, or producing a "how did the month go" analysis in openfpa.
fpa-portfolio-learn
Use when you run FP&A for several clients and want your practice to compound - mines patterns that generalize across your same-type clients, validates them by leave-one-out cross-client backtesting, and promotes ratified priors and skills into a local library that seeds every new client. All local; nothing leaves your machine.
fpa-research-loop
Use after forecasts have scored actual outcomes and you want the AI to run bounded autonomous champion/challenger research epochs, discard weak candidates, and propose only evidence-backed model promotions.
fpa-scaffold-model
Use when building a new openfpa forecast model from a company's financials - a trial balance, a P&L export, or a pasted income statement - and you need a runnable config to exist before any forecasting or analysis.
sku-profitability
Use when analyzing which products make or lose money, ranking SKUs by margin or contribution, running a Pareto/80-20 on a product line, or deciding which SKUs to cut, reprice, or push in a product business.
Bio shown is the top-scored skill's repo description as a fallback — real GitHub bios land in a future update.