/**
 * Darwin — Pattern Detector
 *
 * Analyzes experiment history to detect performance patterns:
 * strengths, weaknesses, trends, and anomalies.
 *
 * These patterns drive prompt optimization decisions.
 */
import type { DarwinPattern, MemoryProvider } from '../types.js';
export declare class PatternDetector {
    private memory;
    constructor(memory: MemoryProvider);
    /**
     * Detect patterns across all experiments for a given agent.
     *
     * Groups experiments by taskType, then looks for:
     *   - Strengths: categories where the agent consistently scores high
     *   - Weaknesses: categories where the agent consistently scores low
     *   - Trends: improving or declining performance over time
     *   - Anomalies: individual experiments far from the mean
     */
    detectPatterns(agentName: string): Promise<DarwinPattern[]>;
    private detectStrengths;
    private detectWeaknesses;
    /**
     * Detect categories that underperform relative to the agent's overall average.
     * This catches the "mediocre zone" (4.0-7.5) that absolute thresholds miss.
     *
     * Example: Writer has market=6.3, tech=7.1, webdesign=7.1 → overall ~6.8
     * Market is 0.5 below average. With RELATIVE_WEAKNESS_GAP=1.0, it wouldn't trigger.
     * But if we also check against the BEST category, market is 0.8 below tech/webdesign.
     *
     * Strategy: flag if category is >RELATIVE_WEAKNESS_GAP below the best category
     * OR if category is below 7.0 with significant data (improvement opportunity).
     */
    private detectRelativeWeaknesses;
    /**
     * Detect improving or declining quality trends across all experiments
     * (sorted chronologically).
     */
    private detectTrends;
    /**
     * Detect outlier experiments within a task category using
     * standard deviation from the mean quality score.
     */
    private detectAnomalies;
    private groupByTaskType;
    private avgQuality;
    private successRate;
    /**
     * Calculate confidence from evidence count.
     * More data points = higher confidence, capping at 1.0.
     * Formula: min(count / 10, 1.0) — so 10+ experiments = full confidence.
     */
    private confidenceFromCount;
    /**
     * Sample standard deviation (Bessel's correction, n-1).
     * With small category sizes (MIN_CATEGORY_SIZE=2), population std
     * would underestimate variance and over-report anomalies.
     */
    private standardDeviation;
}
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