Capture a Correction (Operate)

Overview

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.

The three correction types

• parametric - a concrete driver fix ("December runs ~2× a normal month"). Becomes

an override (a config path + value) applied to every future forecast via pyfpa.apply_corrections.

• structural - a methodology fix ("you're double-counting deferred revenue"). A

pre-ratified structural proposal (the human authored it) - route it to fpa-learn-business to generate the skill/model change; do NOT wait for backtest misses.

• context - a one-time-item note ("that Q3 spike was a one-off contract"). Annotates so

fpa-cfo-judgment's one-time screen keeps the backtest from "learning" a one-off.

Workflow

1. Classify the correction (parametric / structural / context).
2. Identify the target - the driver path (e.g. channels[*].seasonality[11],

working_capital.dio_days), line, or profile area. For parametric, draft the concrete override: {path, value}.

1. Write the correction with 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]].

1. Confirm interpretation. Echo back the concrete change ("I'll set December

seasonality to 2.0 on all channels - right?"). Only on confirmation set status: applied.

1. Keep `.fpa/MEMORY.md` current - the vault index (see below).

Applying corrections

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.

The .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.

Guardrails

• Confirm interpretation before applied. Reversible via status (open/applied/superseded).
• The backtest monitors applied corrections and may flag a stale one - it never reverts;

the human decides.

Next

Correction captured → fpa-monthly-close / fpa-board-briefing (re-run grounded by it).