| 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. |
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
- Classify the correction (parametric / structural / context).
- 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}.
- 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]].
- 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.
- 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).
