/**
 * Vision shot scoring via OpenAI's chat completions API.
 *
 * Self-contained on purpose — gg-ai is for streaming agent loops; this is a
 * one-shot batch call. Keeps the dependency surface small and lets us tune
 * the structured-output prompt independently.
 *
 * Why OpenAI: gpt-4o-mini is cost-effective (~$0.15/1M tokens) and follows
 * strict JSON output reliably. Anthropic vision is comparable and could be
 * added as an alternate backend later.
 */
export interface FrameToScore {
    /** Path to a JPEG/PNG file on disk. */
    path: string;
    /** Time in seconds where this frame was sampled. */
    atSec: number;
}
export interface ShotScore {
    atSec: number;
    /** 0-10. Higher = better composition / focus / subject clarity. */
    score: number;
    /** One-line reason. ≤ 80 chars. */
    why: string;
}
export interface ScoreOptions {
    apiKey?: string;
    model?: string;
    /** "low" = ~85 tokens/image (cheap), "high" = ~700 tokens/image (more accurate). */
    detail?: "low" | "high";
    /** Override the system prompt used to instruct the scorer. */
    systemPrompt?: string;
    signal?: AbortSignal;
}
/**
 * Score a batch of frames. Splits into requests of ≤20 frames each (OpenAI
 * vision practical limit). Returns ShotScore[] in the same order as input.
 */
export declare function scoreFrames(frames: FrameToScore[], opts?: ScoreOptions): Promise<ShotScore[]>;
/**
 * Parse the model's JSON response. Robust to:
 *   - Array-of-objects: `[{score, why}, ...]`
 *   - Object-wrapped:   `{"shots": [...]}` or `{"results": [...]}`
 *   - Stray prose around the JSON (extracts first array)
 */
export declare function parseScoreResponse(content: string, expected: number): Array<{
    score?: unknown;
    why?: unknown;
}>;
/** Clamp a score value to the 0-10 grading scale. Exposed for testing. */
export declare function clampScore(v: unknown): number;
/** Truncate the rationale to 80 chars; non-strings collapse to "". Exposed for testing. */
export declare function clampWhy(v: unknown): string;
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