import type { SkillCandidateEvidence } from './skill-candidate-store.js';
export interface SkillScoreResult {
    confidence: number;
    signals: {
        toolCallCount: number;
        distinctTools: number;
        errorRecovered: boolean;
        patternOccurrences: number;
        hasVerification: boolean;
        allSucceeded: boolean;
        failedCount: number;
        unrecoveredFailure: boolean;
        sameToolNoRetry?: boolean;
        differentToolRecovery?: boolean;
    };
    errorRecoveryPatterns: string[];
    preconditions: string[];
}
export declare function isHighConfidence(score: SkillScoreResult): boolean;
export declare function isMediumConfidence(score: SkillScoreResult): boolean;
/**
 * Scores a skill candidate based on evidence quality, error recovery, and verification.
 *
 * Confidence scoring layers (0-1 scale):
 * - Base: 0.3 (any complete skill attempt)
 * - Process: +0.15 (4+ tools) or +0.1 (3+ tools), +0.12-0.2 (error recovery)
 * - Reliability: +0.3 (3+ occurrences) or +0.15 (2+ occurrences)
 * - Quality: +0.1 (diverse tools), +0.05-0.1 (verification), +0.1-0.15 (teaching notes)
 * - Penalty: ×0.8 (ends with failure), ×0.6 (>50% failures), ×0.7 (unrecovered same-tool failure)
 *
 * Design rationale:
 * - Same-tool retry (+0.2) is stronger signal than different-tool recovery (+0.12)
 * - Pattern recurrence drives confidence more than single-run perfection
 * - Verification bonus is quality-dependent: strong(exec) > medium(read) > weak(search)
 * - Penalties are multiplicative, not hard caps, allowing partial credit for recovery attempts
 */
export declare function scoreSkillCandidate(evidence: SkillCandidateEvidence, patternOccurrences?: number): SkillScoreResult;
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