Fpa excel model

Skill JeffBrines/openfpa/skills/fpa-excel-model

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-excel-model

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

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What its author says it does

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Use when the user wants their model in Excel with real working formulas - "export this to Excel", "a workbook I can hand my board", "something that recalculates when I change an assumption". Produces a live-formula workbook generated from the model structure and verified against the engine before delivery.

SKILL.md

3.0 KB, as published. Nobody here has run it

Live-Formula Excel Model (Operate)

Overview

Finance professionals ultimately want a workbook where changing an assumption recalculates the model. This skill produces one from the model structure, with named assumption cells and real formulas, then proves it reproduces the engine before it ships. Static value dumps stay with forecast_to_excel; this skill is for living models.

Core principle: no workbook ships unverified. The workbook is generated from the model, never hand-edited values.

The standard model

For the kernel's monthly model, one call:

import pyfpa
cfg = pyfpa.load_config("config.yaml")
pyfpa.model_to_excel(cfg, "model.xlsx")

Or via CLI: python3 -m pyfpa.cli model-export <company-root> --config config.yaml --out model.xlsx.

The workbook has an Assumptions sheet (every driver a named, editable cell: growth, COGS pct, seasonality, DSO/DIO/DPO, debt, tax, D&A, capex) and a Model sheet where every line of the monthly P&L and cash flow is a formula referencing those names.

Any other cadence or layout

Quarterly, weekly, a tab per segment, a lender view: these are YOUR work, not kernel variants. Compose pyfpa.excel.toolkit (add_named_cell, add_named_row, fill_formula_row, formats) in a company-specific exporter under the generated namespace (models/generated/<name>/), then register it:

python3 -m pyfpa.cli entrypoint-register <root> --name excel-quarterly --kind report \
  --description "Quarterly live-formula workbook" \
  --command-json '["python3", "models/generated/<name>/excel_quarterly.py"]'

Keep formulas within the supported vocabulary (arithmetic, ^, SUM, MIN, MAX, IF) so verification works.

Verify before delivering (always)

pip install formulas (verification-only dependency, not shipped at runtime).

report = pyfpa.verify_workbook("model.xlsx", pyfpa.cashflow_from_config(cfg))
assert report.passed, report.failures

For a bespoke exporter, verify against the frame your exporter is meant to reproduce (the engine frame, or its quarterly/weekly aggregation).
A failing report means the exporter is wrong. Fix the formulas, never the tolerance, and never deliver an unverified workbook.

Guardrails

No workbook ships unverified.
Generated from model structure; never paste computed values into formula cells.
Supported formula vocabulary only; if you need a function outside it, restructure the formula (helper rows are fine) rather than expanding the vocabulary.
Company-specific exporters live in generated namespaces and get tests like any other generated code.

Workbook delivered, then fpa-board-briefing (the narrative that goes with it).