How do I install the Fpa Portfolio Learn skill?

Run npx skillmds@latest add jeffbrines/fpa-portfolio-learn in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Fpa Portfolio Learn safe to use?

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Which AI agents work with Fpa Portfolio Learn?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Fpa Portfolio Learn free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Fpa Portfolio Learn?

JeffBrines (@jeffbrines) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Fpa Portfolio Learn by jeffbrines — Agent Skill

Name: Fpa Portfolio Learn
Author: JeffBrines

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.

JeffBrines Updated Sep 17, 2026

File contents

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.

---name: fpa-portfolio-learndescription: 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.---# Portfolio Learn (Loop B)## OverviewLoop 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 generalizedacross #1–9. Everything is local - your own book, on your own machine.**Core principle:** self-improving, never self-ratifying - propose, you accept. Theobjective metric is cross-client: does a pattern learned on some clients fail todegrade the *others*' backtest?## SetupA portfolio manifest `~/.fpa/portfolio.yaml` lists your clients + a business-type tag:```yamllibrary: ~/.fpa/libraryclients: - { path: ~/clients/acme, type: d2c-inventory } - { path: ~/clients/peak, type: d2c-inventory } - { path: ~/clients/haul, type: trucking }```## Workflow1. **Load** the manifest (`pyfpa.load_portfolio`).2. 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.3. **Present** candidates ranked by evidence (support count + cross-client delta).4. **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 payoffNew clients inherit the library: **fpa-learn-business** seeds their starting model fromyour promoted priors (`pyfpa.seed_from_library`) and offers the promoted skills.## NextPromoted → the next new client onboarded via **fpa-learn-business** starts smarter.

Frequently asked questions