Audit cleanup completion plan, all tiers shipped: Tier 1 (security + data integrity) - A.7 RTBF true wipe: redact email_messages body/subject/addresses for threads owned by deleted client; redact document_sends.recipient_email; collect file storage keys + delete blobs post-commit. - A.8 user_permission_overrides FK: documented inline why cascade is correct (not set-null as audit suggested) — overrides have no value without their user. - W2.14 PII redaction: camelCase normalization in audit.ts + error-events.service.ts isSensitiveKey; added city/postal/country/ birth fragments. firstName/lastName/dateOfBirth/postalCode etc. now caught in BOTH masker paths. 12 new test cases lock the coverage. Tier 2 (Documenso completion + refactor) - C.2: documentEvents.recipient_email column + partial unique index for per-recipient webhook dedup (migration 0075). handleDocumentSigned now sets recipient_email on insert. - Phase 2: completion_cc_emails distribution. handleDocumentCompleted reads documents.completionCcEmails, filters out signer-duplicates case-insensitively, fans signed PDF out to non-signer recipients. - C.4: extracted createPublicInterest() service from the 346-line api/public/interests route. Route becomes a thin shell (rate-limit, port resolution, audit log, email fan-out). The trio creation logic is now unit-testable without an HTTP fixture. - Phase 4: POST /api/v1/document-templates/[id]/detect-fields wired to document-field-detector.detectFields(). Sparkles "Auto-detect" button added to template-editor.tsx — maps DetectedField → marker with best-guess merge token (DATE / NAME / EMAIL); user retags. Tier 3 (reporting + recommender snapshot lockfiles) - W7.reports: extracted rollupStageRevenue / rollupStageCounts / computeTotalForecast / computeOccupancyRate / rollupBerthStatusCounts into src/lib/services/report-math.ts (pure functions). 16 new tests including an inline-snapshot lockfile on a representative 7-stage forecast. report-generators.ts now delegates. - W7.recommender: 18 new toMatchSnapshot tripwires on classifyTier boundaries + computeHeat at canonical input points. Tier 4 (rolling) - W6.attach: fixed outdated CLAUDE.md claim — threshold banner is informational and never depended on IMAP; bounce monitoring (the IMAP poller) is separate. - D.1 + D.2: documented deferral inline with full why-not-build-it reasoning so a future engineer sees the rationale. - G.1: representative formatDate sweep (audit-log-list, user-list, document-templates merge tokens, document-signing email). Rest of the ~100 sites stay rolling. Quality gates: 1420/1420 vitest (46 new tests above baseline of 1374), tsc clean, 0 lint errors. Plan: docs/superpowers/plans/2026-05-18-audit-cleanup-completion.md Migration: 0075_c2_document_events_recipient_email.sql (applied to dev DB). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
252 lines
7.4 KiB
TypeScript
252 lines
7.4 KiB
TypeScript
import { describe, it, expect } from 'vitest';
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import {
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classifyTier,
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computeHeat,
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DEFAULT_RECOMMENDER_SETTINGS,
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} from '@/lib/services/berth-recommender.service';
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describe('classifyTier', () => {
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it('"A" when there is no interest history at all', () => {
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expect(classifyTier({ activeInterestCount: 0, lostCount: 0, maxActiveStage: 0 })).toBe('A');
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});
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it('"B" when only lost interests exist (no active)', () => {
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expect(classifyTier({ activeInterestCount: 0, lostCount: 2, maxActiveStage: 0 })).toBe('B');
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});
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// L-001 renumber: 7-stage ranks are 1=enquiry, 2=qualified/nurturing,
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// 3=eoi, 4=reservation, 5=deposit_paid, 6=contract. Tier D fires at
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// deposit_paid (5) or later.
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it('"C" when an active interest is in an early stage (eoi)', () => {
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expect(classifyTier({ activeInterestCount: 1, lostCount: 0, maxActiveStage: 3 })).toBe('C');
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});
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it('"C" even when a prior interest was lost, if there is an active one', () => {
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expect(classifyTier({ activeInterestCount: 1, lostCount: 5, maxActiveStage: 2 })).toBe('C');
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});
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it('"D" when an active interest is at deposit or beyond', () => {
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expect(classifyTier({ activeInterestCount: 1, lostCount: 0, maxActiveStage: 5 })).toBe('D');
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expect(classifyTier({ activeInterestCount: 1, lostCount: 0, maxActiveStage: 6 })).toBe('D');
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});
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it('still "C" at reservation (stage 4) - tier D only kicks in at deposit', () => {
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expect(classifyTier({ activeInterestCount: 1, lostCount: 0, maxActiveStage: 4 })).toBe('C');
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});
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});
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describe('computeHeat', () => {
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const w = DEFAULT_RECOMMENDER_SETTINGS;
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const NOW = new Date('2026-05-05T00:00:00Z');
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it('zero heat when nothing in history', () => {
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const h = computeHeat(
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{
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latestFallthroughAt: null,
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totalInterestCount: 0,
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eoiSignedCount: 0,
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fallthroughMaxStage: 0,
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},
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w,
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NOW,
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);
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expect(h.total).toBe(0);
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});
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it('full recency for a fall-through within the last 30 days', () => {
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const h = computeHeat(
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{
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latestFallthroughAt: new Date('2026-04-25T00:00:00Z'),
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totalInterestCount: 0,
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eoiSignedCount: 0,
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fallthroughMaxStage: 1,
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},
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w,
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NOW,
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);
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// recency component should be the full heat_weight_recency (30)
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expect(h.recency).toBeCloseTo(30, 1);
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});
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it('zero recency for an ancient fall-through (>1 year)', () => {
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const h = computeHeat(
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{
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latestFallthroughAt: new Date('2024-01-01T00:00:00Z'),
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totalInterestCount: 0,
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eoiSignedCount: 0,
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fallthroughMaxStage: 1,
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},
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w,
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NOW,
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);
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expect(h.recency).toBe(0);
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});
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it('full furthest-stage when the fall-through reached deposit', () => {
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const h = computeHeat(
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{
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latestFallthroughAt: null,
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totalInterestCount: 0,
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eoiSignedCount: 0,
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fallthroughMaxStage: 5, // deposit_paid (was deposit_10pct=6 pre-refactor)
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},
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w,
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NOW,
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);
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expect(h.furthestStage).toBeCloseTo(40, 1);
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});
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it('saturates interest-count at 5+', () => {
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const h = computeHeat(
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{
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latestFallthroughAt: null,
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totalInterestCount: 10,
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eoiSignedCount: 0,
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fallthroughMaxStage: 0,
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},
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w,
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NOW,
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);
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expect(h.interestCount).toBeCloseTo(15, 1); // full weight
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});
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it('saturates EOI-count at 3+', () => {
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const h = computeHeat(
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{
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latestFallthroughAt: null,
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totalInterestCount: 0,
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eoiSignedCount: 5,
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fallthroughMaxStage: 0,
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},
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w,
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NOW,
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);
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expect(h.eoiCount).toBeCloseTo(15, 1);
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});
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it('total ≈ 100 when everything is maxed', () => {
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const h = computeHeat(
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{
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latestFallthroughAt: new Date('2026-04-25T00:00:00Z'),
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totalInterestCount: 5,
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eoiSignedCount: 3,
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fallthroughMaxStage: 6,
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},
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w,
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NOW,
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);
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expect(h.total).toBeGreaterThanOrEqual(99);
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expect(h.total).toBeLessThanOrEqual(100);
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});
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it('respects tunable per-port weights (skewed toward recency)', () => {
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const skewed = {
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heatWeightRecency: 100,
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heatWeightFurthestStage: 0,
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heatWeightInterestCount: 0,
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heatWeightEoiCount: 0,
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};
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const recent = computeHeat(
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{
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latestFallthroughAt: new Date('2026-04-25T00:00:00Z'),
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totalInterestCount: 0,
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eoiSignedCount: 0,
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fallthroughMaxStage: 0,
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},
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skewed,
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NOW,
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);
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expect(recent.total).toBeCloseTo(100, 1);
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const old = computeHeat(
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{
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latestFallthroughAt: new Date('2024-01-01T00:00:00Z'),
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totalInterestCount: 5,
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eoiSignedCount: 3,
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fallthroughMaxStage: 6,
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},
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skewed,
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NOW,
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);
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expect(old.total).toBe(0);
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});
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it('zero-weights guard (no division-by-zero blow-up)', () => {
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const zeros = {
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heatWeightRecency: 0,
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heatWeightFurthestStage: 0,
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heatWeightInterestCount: 0,
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heatWeightEoiCount: 0,
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};
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const h = computeHeat(
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{
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latestFallthroughAt: new Date(),
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totalInterestCount: 5,
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eoiSignedCount: 3,
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fallthroughMaxStage: 6,
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},
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zeros,
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NOW,
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);
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expect(h.total).toBe(0);
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});
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});
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// ─── W7 snapshot lockfile — locks current tier-ladder boundaries and heat
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// ordering so weight-tuning changes can't silently shift outputs. The
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// existing toBe / toBeCloseTo tests above cover correctness; these
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// inline snapshots are the regression-catching tripwires.
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describe('W7 snapshots — tier-ladder boundaries', () => {
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it.each([
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[0, 0, 0],
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[0, 1, 0],
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[0, 5, 0],
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[1, 0, 1],
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[1, 0, 3],
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[1, 0, 4],
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[1, 0, 5],
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[1, 0, 6],
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[1, 5, 6],
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[2, 0, 5],
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[3, 2, 4],
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])(
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'tier(active=%i, lost=%i, stage=%i) is stable',
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(activeInterestCount, lostCount, maxActiveStage) => {
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expect({
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in: { activeInterestCount, lostCount, maxActiveStage },
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out: classifyTier({ activeInterestCount, lostCount, maxActiveStage }),
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}).toMatchSnapshot();
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},
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);
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});
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describe('W7 snapshots — heat at canonical inputs', () => {
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const NOW = new Date('2026-05-05T00:00:00Z');
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const w = DEFAULT_RECOMMENDER_SETTINGS;
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it.each([
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// [label, fallthroughDaysAgo|null, totalInterestCount, eoiSignedCount, fallthroughMaxStage]
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['cold (no history)', null, 0, 0, 0],
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['recent fallthrough at enquiry stage', 5, 1, 0, 1],
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['recent fallthrough at eoi stage', 5, 2, 1, 3],
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['recent fallthrough at deposit stage (deepest hurt)', 5, 5, 3, 5],
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['old fallthrough at deposit stage (recency decayed)', 120, 5, 3, 5],
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['no fallthrough but many interests', null, 8, 4, 0],
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['typical mid-funnel hot lead', 14, 3, 2, 4],
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])('heat: %s', (_label, daysAgo, totalInterestCount, eoiSignedCount, fallthroughMaxStage) => {
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const latestFallthroughAt =
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daysAgo === null ? null : new Date(NOW.getTime() - daysAgo * 86400 * 1000);
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const h = computeHeat(
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{ latestFallthroughAt, totalInterestCount, eoiSignedCount, fallthroughMaxStage },
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w,
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NOW,
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);
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// Snapshot the rounded breakdown — exact float math (toBeCloseTo)
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// is covered above; this locks the relative ordering + magnitude.
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expect({
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total: Math.round(h.total * 1000) / 1000,
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recency: Math.round(h.recency * 1000) / 1000,
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furthestStage: Math.round(h.furthestStage * 1000) / 1000,
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interestCount: Math.round(h.interestCount * 1000) / 1000,
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eoiCount: Math.round(h.eoiCount * 1000) / 1000,
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}).toMatchSnapshot();
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});
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});
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