import { type EngineAnswer, type MentionResult, type ProductEntity } from '../types.js';
/**
 * The ordered list of products an answer recommends, first-mentioned first.
 *
 * Engines format recommendations in several ways and all of them are real
 * (lesson #4): numbered lists, bullets, markdown headings, and bold lead-in
 * paragraphs. Anything with no such structure yields an empty shelf, which is a
 * signal in itself - `analyzeProductAnswer` sends those to the judge rather
 * than reading them as "recommends nothing".
 *
 * What a candidate must survive to reach the shelf is `isPlausibleName`: real
 * answers bullet a product's FEATURES underneath its name, and those bullets
 * are structurally identical to a list of products. Live runs on 2026-07-23 put
 * "Automatic milk steaming with adjustable temp and texture", "Compact" and
 * "Height" on merchants' shelves as if they were rival products, which is what
 * those filters exist to stop.
 */
export declare function extractRecommendations(text: string): string[];
/**
 * One product's analysis of one engine answer.
 *
 * Structurally a {@link MentionResult}, so M7's scoring aggregation
 * (`scoreEngine`, `compositeScore`) applies unchanged - one formula, one
 * `SCORING_VERSION`, no forked maths (rule #6).
 */
export interface ProductMention extends MentionResult {
    /** The MERCHANT's name for this product, never an engine-echoed variant. */
    product: string;
    /** The answer's recommendation list, in the order it presented them. */
    shelf: string[];
}
/** Every name this product answers to. */
export declare function productTerms(product: ProductEntity): string[];
/** Whether a shelf entry refers to this product (boundary-aware, folded). */
export declare function shelfEntryIsProduct(entry: string, terms: string[]): boolean;
/** Analyze one engine answer for one product (deterministic pass 1). */
export declare function analyzeProductAnswer(answer: EngineAnswer, product: ProductEntity): ProductMention;