skills/horizon-scenarios/SKILL.md

---
name: horizon-scenarios
description: Model future industry size, adoption, and growth with explicit scenarios, event probabilities, economic ratings, and sensitivity checks. Use to quantify a defined field or compare evidence-backed candidates.
---

# Scenario and rating analyst

Read [scoring](../horizon-forecast/references/scoring.md), [scenario method](../horizon-forecast/references/scenario-method.md), and [calculation interface](../horizon-forecast/references/calculation-interface.md). Use the established scope when delegated; if standalone, state a plan, metric, geography, and endpoints before modeling.

1. Establish compatible baseline definitions and buyer/capacity constraints. An unknown baseline remains unknown.
2. Elicit independent initial estimates where actual delegation is available and useful; this skill requests a bounded independent forecasting pass for material comparisons. Supply neutral evidence and event definitions before sharing a preferred number. Follow [orchestration](../horizon-forecast/references/agent-orchestration.md).
3. Construct coherent downside, central, and upside paths. Use justified subjective probabilities only for an exhaustive, non-overlapping partition of outcomes; otherwise show unweighted exploratory scenarios.
4. Calculate growth, added annual size, and probability-weighted outcomes with the bundled calculator when inputs qualify. Use the declared economic basis for every candidate.
5. Apply anchored growth and overlooked-opportunity ratings. Keep evidence confidence and event probability separate. Missing inputs create bounds and provisional status, not neutral imputation.
6. Test rating-weight sensitivity and the economic assumptions that can reverse the result. Preserve unresolved differences; do not treat majority agreement as accuracy.
7. Return input assumptions, calculations, scenario table, scorecards with evidence, threshold definitions, reversal conditions, and specific 1–3-year signposts.

Do not imply the 0–100 ratings are calibrated probabilities, expected investment returns, or objectively validated weights. If a quantitative estimate is unsupported, deliver a qualitative scenario and specify the missing measurement.