/**
 * Pacing & Structure Analyzer (GAP-11 / SPEC-08)
 *
 * Queries the MCP SQLite database to compute tension-arc, POV balance,
 * chapter-length distribution, and derived pacing flags.
 */
import type { MCPClient } from '../core/database.js';
import type { ChapterTensionData, POVBalance, ChapterLengthData, PacingReport } from '../types/novel.js';
export declare class PacingAnalyzer {
    private readonly client;
    private readonly projectId;
    constructor(client: MCPClient, projectId: number);
    /**
     * Compute average tension per chapter using scenes.tension_level.
     *
     * SQL shape: { chapter_number, title, avg_tension, scene_count }
     */
    analyzeTensionArc(): Promise<ChapterTensionData[]>;
    /**
     * Compute POV character distribution across all scenes.
     *
     * SQL shape: { character_name, scene_count }
     */
    analyzePOVBalance(): Promise<POVBalance[]>;
    /**
     * Compute word-count distribution per chapter and flag outliers.
     *
     * SQL shape: { chapter_number, title, word_count }
     * Flags: word_count > 2 * avg → 'long', < 0.5 * avg → 'short'
     */
    analyzeChapterLengths(): Promise<ChapterLengthData[]>;
    /**
     * Generate a full pacing report combining all three analyses and derived flags.
     */
    generateReport(): Promise<PacingReport>;
    /**
     * Derive PacingFlag[] from the three data arrays.
     *
     * Rules:
     *  - tension_dip   : 3+ consecutive chapters with avg_tension < 4,
     *                    appearing after a chapter with avg_tension > 5
     *  - conflict_gap  : 3+ consecutive chapters with avg_tension <= 3
     *  - pov_imbalance : any single character holds > 70% of scenes
     *  - length_outlier: any chapter flagged 'long' or 'short'
     */
    private deriveFlags;
    /**
     * Build an ASCII tension chart.
     *
     * Format per line:  "Ch<N> <filled>█<empty>░ <value>"
     * Scale: 10 blocks total; each █ = 1 tension point (clamped to [0, 10]).
     */
    private buildAsciiChart;
}
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