Fpa portfolio learn

Skill JeffBrines/openfpa/skills/fpa-portfolio-learn

The 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.

Install

npx -y skills add JeffBrines/openfpa --skill fpa-portfolio-learn

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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.

SKILL.md

2.6 KB, as published. Nobody here has run it

Portfolio Learn (Loop B)

Overview

Loop A makes the model better at one client. This makes your practice compound: client #10 starts smarter than client #1 because your library carries what generalized across #1–9. Everything is local - your own book, on your own machine.

Core principle: self-improving, never self-ratifying - propose, you accept. The objective metric is cross-client: does a pattern learned on some clients fail to degrade the others' backtest?

Setup

A portfolio manifest ~/.fpa/portfolio.yaml lists your clients + a business-type tag:

library: ~/.fpa/library
clients:
  - { path: ~/clients/acme,  type: d2c-inventory }
  - { path: ~/clients/peak,  type: d2c-inventory }
  - { path: ~/clients/haul,  type: trucking }

Workflow

Load the manifest (pyfpa.load_portfolio).
For each business-type with at least 3 clients:
- Priors: let type_clients = pyfpa.portfolio.clients_of_type(portfolio, type). pyfpa.mine_priors(portfolio, type) finds drivers that cluster tightly; validate each with pyfpa.validate_prior(driver, type_clients) (leave-one-out). Surface validated ones first (by cross-client delta), then unvalidated/judgment.
- Skills: pyfpa.find_recurring_skills(portfolio, type) for recurring generated skills. Also weigh recurring structural corrections across clients (read each .fpa/corrections/ for type: structural) - a human-authored pattern that repeats is strong signal.
Present candidates ranked by evidence (support count + cross-client delta).
Ratify. On your acceptance, pyfpa.promote_prior / pyfpa.promote_skill writes the ~/.fpa/library/ and library-log.md. Reversible.

Guardrails

Local-only; nothing phones home.
At least 3 clients to propose; tight-cluster only; a prior must not degrade held-out clients.
You ratify everything; priors are seeds, not mandates - each client's Loop A refines.

The payoff

New clients inherit the library: fpa-learn-business seeds their starting model from your promoted priors (pyfpa.seed_from_library) and offers the promoted skills.

Promoted → the next new client onboarded via fpa-learn-business starts smarter.