菜单
Use when a human reviewing a forecast catches something off ("December always spikes", "you're double-counting deferred revenue", "that Q3 number was a one-time contract") - captures it as a durable, typed correction in the company's memory so future forecasts are grounded by it.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
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.
Use when producing a board deck, investor update, or CFO briefing from an openfpa forecast - turning model output into a board-ready narrative and exportable artifacts.
Use when building a new openfpa forecast model from a company's financials - a trial balance, a P&L export, or a pasted income statement - and you need a runnable config to exist before any forecasting or analysis.
Use when modeling a multi-segment company that discloses segment net sales and segment Adjusted EBITDA (ASU 2023-07) but not segment COGS or operating income - rolls segment P&Ls into a consolidated forecast and reconciles total segment Adjusted EBITDA to the disclosed total. Generated for Fox Factory (PVG/AAG/SSG).
Use when you want the model to learn from how its past forecasts actually turned out - scoring forecasts against the company's real actuals, backtesting assumptions on history, and proposing ratified improvements. Runs at/after monthly close.
Use when answering "when do we run out of cash", building a 13-week cash forecast, sizing a credit line, or analyzing near-term liquidity and payment timing in openfpa.
| name | fpa-capture-correction |
| description | Use when a human reviewing a forecast catches something off ("December always spikes", "you're double-counting deferred revenue", "that Q3 number was a one-time contract") - captures it as a durable, typed correction in the company's memory so future forecasts are grounded by it. |
A human reviewing a forecast is the highest-signal feedback there is - they catch
structural errors and domain knowledge the backtest can't see, and catch them now.
This skill turns that into durable memory: a typed correction in .fpa/corrections/
that grounds every future forecast.
Core principle: the human is the authority; capture, confirm interpretation once, then it persists. Everything is plain markdown the user owns.
override (a config path + value) applied to every future forecast via
pyfpa.apply_corrections.channels[*].seasonality[11],
working_capital.dio_days), line, or profile area. For parametric, draft the concrete
override: {path, value}.pyfpa.save_correction. Set slug to a
<date>-<short-name> string (e.g. 2026-06-08-december-seasonality) - save_correction
uses the whole slug as the filename (.fpa/corrections/<slug>.md), so keep the date in
it. Include frontmatter (type, target, status, date, override) and a markdown
body (**Was off:** … **Correction:** … **Why:** [[…]]), linking to the assumption/profile
it corrects with [[wikilinks]].status: applied..fpa/MEMORY.md current - the vault index (see below).When building any forecast, pyfpa.apply_corrections(cfg, load_corrections(".fpa/corrections"))
folds the applied parametric corrections into the config. The per-client loop refines from
there - corrections are seeds, not mandates.
.fpa/ vault (MEMORY.md index)Keep a .fpa/MEMORY.md that orients a human, Obsidian, or Claude:
business-profile.md - what we know about the business.corrections/ - human corrections (this skill).forecasts/*.snapshot.yaml, scorecard.md - forecast snapshots + backtest track record.learnings.md - accepted model changes.
All plain markdown - open it in Obsidian if you like, but never required.applied. Reversible via status (open/applied/superseded).Correction captured → fpa-monthly-close / fpa-board-briefing (re-run grounded by it).