/**
 * Trace Config Generator (Spark Plan 07)
 *
 * Given an analysis `intent` (scrolling / startup / anr / memory / generic)
 * the generator returns a `TraceConfigGeneratorContract` listing the
 * Perfetto data sources that should be enabled, plus the canonical custom
 * slice schema and a self-description block that should be embedded inside
 * the captured trace metadata.
 *
 * Spark coverage:
 *  - #197 — AI generated trace config (intent → fragments)
 *  - #53  — custom slice / business instrumentation schema
 *  - #201 — trace self-description metadata
 */
import { type CustomSliceSpec, type TraceConfigGeneratorContract } from '../types/sparkContracts';
export type TraceIntent = 'scrolling' | 'startup' | 'anr' | 'memory' | 'gpu' | 'power' | 'network' | 'generic';
export interface GenerateTraceConfigOptions {
    intent: TraceIntent;
    /** Optional package the capture is targeted at. */
    packageName?: string;
    /** CUJ name (`cold_start`, `scroll_feed`, `tap_interaction`, …). */
    cuj?: string;
    /** Device fingerprint to embed in self-description. */
    device?: string;
    /** Build id / git sha to embed in self-description. */
    buildId?: string;
    /** Extra custom slice declarations that should accompany the trace. */
    customSlices?: CustomSliceSpec[];
}
/**
 * Build a deterministic trace config for the given intent. AI-driven
 * customization can layer on top of this baseline, but the function alone
 * is enough to produce a working trace config without an LLM call.
 */
export declare function generateTraceConfig(opts: GenerateTraceConfigOptions): TraceConfigGeneratorContract;
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