/**
 * Skill Registry — in-memory store and search for skills
 *
 * Mirrors ToolRegistry but for skills. Provides discovery, search, and filtering.
 * Supports both substring matching (default) and semantic search via pluggable embeddings.
 */
import { SkillDefinition, SkillSummary, SearchSkillsOptions, EmbeddingProvider, SkillContentOptions } from './types.js';
/**
 * Semantic search result with relevance score.
 */
export interface SemanticSearchResult {
    skill: SkillSummary;
    /** Cosine similarity score (0–1, higher = more relevant) */
    score: number;
}
export declare class SkillRegistry {
    private logger;
    private skills;
    /** Pluggable embedding provider for semantic search */
    private embeddingProvider;
    /** Cached embeddings: skill name → vector */
    private embeddings;
    /** Whether embeddings need rebuilding */
    private embeddingsDirty;
    /** Cached parsed content for selective loading */
    private parsedContent;
    /**
     * Set a custom embedding provider (OpenAI, Cohere, etc.).
     * If not set, a built-in TF-IDF provider is used as fallback.
     */
    setEmbeddingProvider(provider: EmbeddingProvider): void;
    /**
     * Register a single skill
     */
    register(skill: SkillDefinition): void;
    /**
     * Register multiple skills
     */
    registerAll(skills: SkillDefinition[]): void;
    /**
     * Get a single skill by name
     */
    get(name: string): SkillDefinition | undefined;
    /**
     * Get a single skill by name (throws if not found)
     */
    getRequired(name: string): SkillDefinition;
    /**
     * Get selective skill content — only the sections an agent needs.
     * This prevents dumping entire SKILL.md files into the LLM context.
     *
     * @example
     * // Get only the error handling section, max 500 tokens
     * registry.getSkillContent('postgres-query-operations', {
     *   sections: ['Error Handling'],
     *   maxTokens: 500,
     * })
     */
    getSkillContent(name: string, options?: SkillContentOptions): string | null;
    /**
     * List all sections of a skill with their token costs.
     * Agents use this to decide which sections to load.
     */
    getSkillSections(name: string): Array<{
        path: string;
        level: number;
        tokenEstimate: number;
    }> | null;
    /**
     * List all skills (Level 1 discovery — minimal context)
     */
    list(): SkillSummary[];
    /**
     * Search skills by keyword, category, difficulty, etc.
     *
     * When `options.semantic` is true, uses embedding-based similarity ranking
     * instead of substring matching. Falls back to built-in TF-IDF if no
     * external embedding provider is configured.
     */
    search(options?: SearchSkillsOptions): SkillSummary[];
    /**
     * Semantic search with relevance scores.
     * Returns ranked results with cosine similarity scores.
     *
     * @example
     * const results = await registry.semanticSearch('How do I handle Postgres connection pooling?');
     * // → [{ skill: { name: 'postgres-query-operations', ... }, score: 0.82 }]
     */
    semanticSearch(query: string, options?: {
        limit?: number;
        minScore?: number;
    }): Promise<SemanticSearchResult[]>;
    /**
     * Get all skills (full definitions)
     */
    getAll(): SkillDefinition[];
    /**
     * Check if a skill exists
     */
    has(name: string): boolean;
    /**
     * Get the count of registered skills
     */
    count(): number;
    /**
     * Clear all skills
     */
    clear(): void;
    /**
     * Remove a skill
     */
    remove(name: string): boolean;
    /**
     * Ensure embeddings are computed and up-to-date.
     */
    private ensureEmbeddings;
    /**
     * Synchronous ranking using TF-IDF (for the non-async search() method).
     */
    private rankBySimilarity;
    /**
     * Get or create the default TF-IDF provider.
     */
    private defaultProvider;
    private getDefaultProvider;
    /**
     * Convert a skill into a searchable text string for embedding.
     */
    private skillToText;
}
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