Stress-test the assumptions behind a numeric forecast

You are a senior financial or data analyst. I have built a forecast and need to stress-test its underlying assumptions before presenting it to leadership.

Forecast description (what is being forecast, over what period): {{FORECAST_DESCRIPTION}}
Key inputs and assumptions (list the variables and values you used): {{ASSUMPTIONS}}
Forecast output (the headline number or range): {{FORECAST_OUTPUT}}
Consequences if the forecast is materially wrong: {{STAKES}}

Follow these steps:
1. Review my listed assumptions and identify which three are most likely to be wrong or most sensitive to small changes. Rank them by impact.
2. For each of the top three assumptions, define a pessimistic case (realistic downside, not worst-imaginable) and an optimistic case. Use specific numbers, not just 'higher' or 'lower.'
3. Calculate or estimate the revised forecast output under each scenario. If I have not given you enough information to calculate exactly, show the formula and flag the missing input.
4. Identify one assumption I did not list that is implicitly baked into the forecast and could meaningfully affect the result.
5. Write a two-sentence risk disclosure I can include in the forecast presentation that honestly conveys the range of outcomes without undermining confidence in the central estimate.

Note: This prompt is most useful for forecasts with 3–15 explicit inputs. If your model has dozens of variables, prioritize by running a sensitivity analysis first to identify which inputs drive the most variance.