fpa-capture-correctionlisted

# 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}`. 3. **Write** the correction with `pyfpa.save_correction`. Set `slug` to a