/**
 * log10x_metric_overlay — deterministic primitive: return two timeseries
 * pre-aligned to the same timestamp grid, plus a small set of deterministic
 * facts the agent can read directly (peak_at, peak_value, peak_offset).
 *
 * Design rationale (from the design-dilemma consult + the chaos retest):
 * the agent's hardest call in cross-pillar correlation is "does this
 * candidate's curve actually lead/lag/co-move with the anchor's curve."
 * Pearson collapses that into one number; correlation tier compresses
 * further. The agent then has to trust the tool's verdict OR ask for
 * the raw curves anyway. This primitive gives the curves directly with
 * NO interpretation layer — no Pearson, no tier, no causal claim. The
 * agent eyeballs lead/lag the same way an SRE opens two Grafana panels
 * side by side.
 *
 * Inputs: anchor expression (a Log10x pattern OR a customer PromQL),
 * candidate expression (a customer PromQL), window + step.
 *
 * Output: aligned arrays of `(ts, anchor_value, candidate_value)` tuples
 * plus deterministic facts:
 *   - peak_anchor_at, peak_anchor_value
 *   - peak_candidate_at, peak_candidate_value
 *   - peak_offset_seconds (candidate_peak_ts - anchor_peak_ts; negative
 *     = candidate leads anchor)
 *   - n_buckets_aligned (how many buckets where both series have data)
 *
 * No Pearson. No tier. No verdict. Composes with rank_by_shape_similarity
 * (when the agent has many candidates) and metrics_that_moved (when the
 * agent is narrowing the candidate pool first).
 */
import { z } from 'zod';
import type { EnvConfig } from '../lib/environments.js';
import { type StructuredOutput } from '../lib/output-types.js';
export declare const metricOverlaySchema: {
    anchor_type: z.ZodEnum<["log10x_pattern", "customer_metric"]>;
    anchor: z.ZodString;
    candidate: z.ZodOptional<z.ZodString>;
    candidates: z.ZodOptional<z.ZodArray<z.ZodString, "many">>;
    window: z.ZodDefault<z.ZodString>;
    timeRange: z.ZodOptional<z.ZodString>;
    step: z.ZodDefault<z.ZodString>;
    max_buckets: z.ZodDefault<z.ZodNumber>;
    environment: z.ZodOptional<z.ZodString>;
    customer_metrics_url: z.ZodOptional<z.ZodString>;
    customer_metrics_type: z.ZodOptional<z.ZodEnum<["grafana_cloud", "amp", "datadog_prom", "generic_prom", "log10x"]>>;
    customer_metrics_auth: z.ZodOptional<z.ZodString>;
};
/**
 * Top-level call status. Agent branches on this before reading anything else.
 *   - `success`: math ran cleanly; read `series` and `facts`.
 *   - `anchor_no_phase_separation`: anchor MAD/median < 0.15. Refused.
 *   - `no_signal`: anchor or candidate returned no overlapping data.
 *   - `error`: structural failure; read `data.error`.
 */
export type MetricOverlayStatus = 'success' | 'anchor_no_phase_separation' | 'no_signal' | 'error';
export declare function executeMetricOverlay(argsInput: {
    anchor_type: 'log10x_pattern' | 'customer_metric';
    anchor: string;
    candidate?: string;
    candidates?: string[];
    window?: string;
    timeRange?: string;
    step?: string;
    max_buckets?: number;
    environment?: string;
    customer_metrics_url?: string;
    customer_metrics_type?: string;
    customer_metrics_auth?: string;
    /** Ignored. Retained for backward-compat with in-process callers. */
    view?: 'summary' | 'markdown';
}, env: EnvConfig): Promise<StructuredOutput>;
export declare function peakOf(points: Array<{
    ts: number;
    v: number | null;
}>): {
    ts: number;
    v: number;
} | null;