/**
 * LearningVelocityScorer — EXO 3.0's KPI inversion (Salim Ismail, "Your KPI
 * System Is Training You to Miss the Future").
 *
 * Backward-looking operational KPIs (throughput, utilization, efficiency) reward
 * the existing model and suppress the weak signals where the future shows up.
 * EXO 3.0 says measure **learning velocity** instead — adaptability,
 * experimentation, capability creation. Instar already emits the raw learning
 * events (registered learnings, Playbook items added, corrections captured,
 * evolution actions); this scorer turns them into a velocity + trend + diversity
 * signal so an org can watch how fast it's *learning*, not just how much it's
 * *producing*.
 *
 * Pure + deterministic so it's testable and reproducible. The route gathers the
 * real events; this computes the metric.
 */
export interface LearningEvent {
    /** ISO timestamp of the learning event. */
    timestamp: string;
    /** Category: 'learning' | 'playbook' | 'correction' | 'evolution' | 'memory' | string. */
    type: string;
}
export type VelocityTrend = 'accelerating' | 'steady' | 'declining' | 'insufficient-data';
export interface LearningVelocityResult {
    windowDays: number;
    totalEvents: number;
    /** Events per day across the window. */
    eventsPerDay: number;
    /** Count by event type. */
    byType: Record<string, number>;
    /** Distinct learning categories seen (a diversity proxy). */
    typeDiversity: number;
    /** First-half vs second-half comparison of the window. */
    trend: VelocityTrend;
    /** 0–100, blends velocity (capped) and category diversity. */
    adaptabilityScore: number;
    reason: string;
}
export declare function computeLearningVelocity(events: LearningEvent[], nowIso: string, windowDays?: number): LearningVelocityResult;
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