/**
 * LangChain Integration for Matimo
 *
 * Converts Matimo tools to LangChain-compatible format.
 * Simple, lightweight, scales to 2000+ tools.
 *
 * NOTE: Requires @langchain/core as peer dependency.
 * Install with: npm install @langchain/core langchain
 *
 * Usage:
 *   const matimo = await MatimoInstance.init('./tools');
 *   const langchainTools = await convertToolsToLangChain(
 *     matimo.listTools(),
 *     matimo,
 *     { SLACK_BOT_TOKEN: process.env.SLACK_BOT_TOKEN }
 *   );
 */
import { z } from 'zod';
import type { ToolDefinition } from '../core/types.js';
import type { MatimoInstance } from '../matimo-instance.js';
export interface LangChainTool {
    name: string;
    description: string;
    schema: z.ZodSchema;
    invoke: (input: Record<string, unknown>) => Promise<unknown>;
}
/**
 * Convert Matimo tools to LangChain format
 *
 * @param tools - Matimo tools
 * @param matimo - MatimoInstance
 * @param secrets - Map of parameter names to secret values
 * @param secretParamNames - Explicitly declared secret parameters (optional)
 * @returns LangChain tools
 *
 * @example
 * ```ts
 * const tools = await convertToolsToLangChain(
 *   matimo.listTools().filter(t => t.name.startsWith('slack')),
 *   matimo,
 *   { SLACK_BOT_TOKEN: process.env.SLACK_BOT_TOKEN }
 * );
 * ```
 */
export declare function convertToolsToLangChain(tools: ToolDefinition[], matimo: MatimoInstance, secrets?: Record<string, string>, secretParamNames?: Set<string>): Promise<LangChainTool[]>;
export interface SkillContext {
    name: string;
    description: string;
    content: string;
}
/**
 * Return Level 1 metadata (name + description) for all available skills.
 *
 * Token-safe — only a few lines per skill. Include this in the system prompt
 * so the agent knows what skills exist and can request them by name, mirroring
 * what `matimo_list_skills` does in the tool-based flow.
 *
 * @example
 * ```ts
 * const meta = getSkillsMetadata(matimo);
 * // → [{ name: 'code-review', description: 'Code review checklist' }, ...]
 * ```
 */
export declare function getSkillsMetadata(matimo: MatimoInstance): Array<{
    name: string;
    description: string;
}>;
/**
 * Build a per-request system prompt snippet from semantically relevant skills.
 *
 * Uses TF-IDF semantic search (built-in, zero dependencies) to rank all skills
 * against the user's query and loads full content only for the top matches.
 * This preserves the progressive disclosure model without MCP:
 *
 *   Level 1 at startup → Level 2 per-request (only relevant skills)
 *
 * @param matimo     - Initialised MatimoInstance
 * @param query      - The user's current message/query; drives semantic ranking
 * @param options.topK      - Max skills to load (default 3); keeps token cost bounded
 * @param options.minScore  - Minimum cosine similarity to include (default 0.3)
 * @param options.header    - Custom header text (optional)
 * @returns Formatted string ready to inject as a context block, or empty string
 *          when no skills score above `minScore`.
 *
 * @example
 * ```ts
 * // In your ReAct loop, per message:
 * const skillContext = await buildRelevantSkillPrompt(matimo, userMessage, { topK: 2 });
 * const messages = [
 *   new SystemMessage(baseSystemPrompt),
 *   ...(skillContext ? [new SystemMessage(skillContext)] : []),
 *   new HumanMessage(userMessage),
 * ];
 * ```
 */
export declare function buildRelevantSkillPrompt(matimo: MatimoInstance, query: string, options?: {
    topK?: number;
    minScore?: number;
    header?: string;
}): Promise<string>;
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