fpa-board-briefing — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited fpa-board-briefing (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.
Turn forecast output into a board/investor-grade briefing: the headline numbers, the cash story, what changed, and the risks - as markdown and Excel. Make the operator look like they have a CFO.
Core principle: A board wants the story and the three things that matter, not a data dump.
openfpa entrypoint-list <company-root> --kind report. Use a registered briefing workflow when one exists.
import pyfpa
from pyfpa.io.loaders import load_cash13_config
monthly = pyfpa.cashflow_from_config(pyfpa.load_config("examples/ridgeline/config.yaml"))
runway = pyfpa.runway_summary(pyfpa.cash13_forecast(load_cash13_config("examples/ridgeline/cash13.yaml"))) from pyfpa.io.reporting import to_briefing_md, forecast_to_excel
md = to_briefing_md(monthly, title="Acme Inc.", runway=runway)
forecast_to_excel(monthly, "forecast.xlsx")to_briefing_md emits a headline (revenue, EBITDA, net income, ending cash), an optional 13-week runway section, and a monthly table.
to_briefing_md emits only the headline, the optional runway section, and the monthly table - it has no narrative slot. So author your own markdown around it: prepend a ## What changed section (the 3 things that moved) and append ## Risks (3 forward risks) and ## The ask (e.g. "approve a $200K line to bridge the spring build"). The rendered briefing is the data spine; you supply the story.ebitda as true EBITDA if D&A matters.python examples/ridgeline/run_demo.py runs this whole path on the synthetic demo and writes docs/demo/briefing.md + forecast.xlsx.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.