fpa-portfolio-learn — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited fpa-portfolio-learn (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
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?
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 }pyfpa.load_portfolio).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.
pyfpa.find_recurring_skills(portfolio, type) for recurring generatedskills. Also weigh recurring structural corrections across clients (read each .fpa/corrections/ for type: structural) - a human-authored pattern that repeats is strong signal.
pyfpa.promote_prior / pyfpa.promote_skill writes the~/.fpa/library/ and library-log.md. Reversible.
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.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.