/**
 * router-intents.ts — LLM-based intent classification for the router.
 *
 * Flow (zero added latency):
 *   1. User sends prompt → router picks model with heuristics → turn starts IMMEDIATELY
 *   2. In background: haiku classifies the prompt intent ("debugging", "architecture"…)
 *   3. If user overrode the router, record: intent X → user prefers model Y
 *   4. After 3 consistent observations → intent X gets a "learned preference"
 *   5. Next time: classifyPromptForRouter checks learned preferences BEFORE heuristics
 *
 * Only runs when config.router.learn = true AND anthropic is authenticated.
 * Storage: ~/.squeezr-code/router-intents.json
 */
import type { AuthManager } from '../auth/manager.js';
export declare const INTENT_CATEGORIES: readonly ["debugging", "code-review", "architecture", "refactoring", "explanation", "translation", "creative", "factual", "testing", "documentation", "other"];
export type IntentCategory = typeof INTENT_CATEGORIES[number];
/**
 * Classifies the intent of a prompt using haiku.
 * Runs in background — does NOT block the main turn.
 */
export declare function classifyPromptIntentAsync(prompt: string, auth: AuthManager): Promise<IntentCategory>;
/**
 * Record an observation after a turn completes (background, fire-and-forget).
 * Then re-compute preferences from all observations.
 */
export declare function recordIntentObservation(intent: IntentCategory, modelUsed: string, wasOverride: boolean): void;
/**
 * Returns the learned model preference for the given prompt, or null.
 * Reads the intent store synchronously — called from classifyPromptForRouter.
 */
export declare function getLearnedIntentPreference(prompt: string): string | null;
/** Human-readable summary of learned intent preferences. */
export declare function learnedIntentsSummary(): string;
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