/**
 * Skill search — fuzzy matching with edit-distance and prefix scoring
 */
import { type SkillMeta } from "./registry.js";
/**
 * Compute Levenshtein edit distance between two strings.
 * Uses a 2-row DP approach — no external deps.
 */
export declare function editDistance(a: string, b: string): number;
/**
 * Search skills by name, description, and tags using fuzzy matching.
 */
export declare function searchSkills(query: string, registry?: SkillMeta[]): SkillMeta[];
/**
 * Bounded close canonical names for a refused name, strongest evidence first:
 *
 *  0. the exact name;
 *  1. a hyphen-delimited containment — the candidate owns the query as a whole
 *     leading or trailing segment (`fleet-refresh` → `instructions-fleet-refresh`);
 *  2. a multi-word token subset of the candidate;
 *  3. the edit-distance rule that was the only signal before.
 *
 * Containment is what edit distance cannot see, and it is the case that cost
 * real time: the canonical name that owns a shorter name as its trailing
 * segment is 13 edits from the short name, so a caller naming the procedure by
 * its last words was told the procedure did not exist (Agents Audit A27). The
 * distance pass still runs and fills any slots containment leaves, so a
 * distance-only miss — including every query under three characters — keeps the
 * answer it has always had.
 */
export declare function findCloseSkillNames(query: string, names: readonly string[], maxResults?: number): string[];
/**
 * Find skills with names similar to the given query (for "did you mean?" suggestions).
 */
export declare function findSimilarSkills(query: string, maxResults?: number, registry?: SkillMeta[]): string[];