black-swanlisted

# Black Swan ## Overview Taleb (2007): a black swan is (1) outside all prior expectations, (2) extreme impact, (3) obvious in hindsight only. Many domains (markets, careers, tech) are **Extremistan** (power-law / fat-tail), yet most models assume **Mediocristan** (Gaussian / thin-tail) — underestimating tail risk by orders of magnitude. The Turkey Problem: 1000 days of feeding creates confidence; day 1001 is Thanksgiving. Composes with [`antifragile`](../antifragile/SKILL.md), [`probabilistic-thinking`](../probabilistic-thinking/SKILL.md), [`inversion`](../inversion/SKILL.md), [`first-principles`](../first-principles/SKILL.md). ## When to Use Use when: a risk model assumes normality in a fat-tailed domain; "never happened in N years" dismisses tail risk; strategy assumes stable environment; you're constructing a retrospective narrative; stress-testing against extreme scenarios; someone says "fat tails / Taleb / narrative fallacy / turkey problem"; a thesis rests on a one-directional trend like "AI demand can only go up," AI-capex payoff, or concentrated mega-cap / AI-bubble exposure. **Not when:** domain is genuinely Mediocristan; "black swan" is being used to excuse a foreseeable planning failure. ## Coaching Novices (Adaptive Front Door) - **Engine mode:** user has a concrete case → run The Process directly. - **Coach mode:** user is unfamiliar or has no concrete case → guide step by step. In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — outp