/**
 * Prose compression by deterministic importance scoring.
 *
 * NO MODEL, ON PURPOSE. Their equivalent engine, Kompress-base, is a
 * ModernBERT classifier -- and it is simultaneously their weakest engine by
 * their own figures (30-50%, against 70-92% for the structural ones) and the
 * source of a stated limitation: "Kompress-base adds RAM overhead on
 * memory-constrained machines". Their whole product also "requires local Python
 * runtime -- incompatible with restricted sandboxes".
 *
 * Buying their least effective engine at the price of their worst deployment
 * constraint is a bad trade. The one structural advantage we hold is that we
 * are Node, already present wherever all sixteen supported clients run, and a
 * transformer dependency would spend exactly that.
 *
 * So: score sentences on features that are cheap and legible. A deterministic
 * scorer is also TESTABLE in a way a learned one is not -- every decision here
 * can be pinned by a fixture, which matters for a component whose failure mode
 * is quietly discarding the sentence that mattered.
 *
 * IF IT UNDERPERFORMS THEIR BAND, IT REPORTS THAT. The benchmark prints the
 * real number for this engine; it does not get to claim theirs.
 */
import type { CompressionResult, EngineContext } from './types.js';
/**
 * Importance of one sentence, higher is more worth keeping.
 *
 * Every term is a claim about what a coding agent needs, and every one is
 * checkable against a fixture.
 */
export declare function score(sentence: string, index: number, total: number): number;
/** Recognises prose rather than structured content. */
export declare function looksLikeProse(text: string): boolean;
/**
 * Keeps the highest-scoring half, in original order.
 *
 * LOSSY, and it says so. The marker names how many sentences went and, when a
 * spill is available, where the original is. A model reading "12 lower-signal
 * sentences" knows the shape of what it is missing, which is the difference
 * between compression and quiet damage.
 */
export declare function compressProse(text: string, ctx?: EngineContext): CompressionResult;
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