fix(cycle): scope symptothermal to the current cycle, unify luteal clamp, widen fertile + crosstab coverage

The symptothermal layer ran against the full day-log history: detectTempShift
returned the earliest 3-over-6 rise and detectMucusPeak the last egg-white day
across all cycles, so a multi-cycle user with historical BBT confirmed a
months-old ovulation and the predicted next-period start landed in the past.
Scope the temperature/mucus detectors to the current cycle window
[lastConfirmedStart − BBT_WINDOW, today], take the latest qualifying temp shift,
and reject any confirmation whose next-start is already in the past.

Resolve luteal length through a single clamp. The predictor clamped to [10,16]
while the calendar adapter and phase mapper used the raw stored value, so the
ovulation dot and the OVULATORY band could land on different days for an
out-of-clamp profile. Export resolveLuteal/clampLuteal, route buildCalendar,
buildPhaseCycles and phaseForDay through it, and tighten the profile-prefs Zod
bound to the same [10,16] range.

Show the fertile window for AVOID_PREGNANCY as well as TRYING_TO_CONCEIVE per the
algorithm spec; it stays hidden for GENERAL_HEALTH/PERIMENOPAUSE.

Bound the day-log reads on the calendar and insights routes to the rendered span
plus the symptothermal lookback instead of the full history; cycle-length stats
already run off the MenstrualCycle rows.

Add mood and blood glucose as phase-crosstab outcome metrics under the same FDR
and per-group day floors. Mood is read from MoodEntry and injected as a synthetic
channel since it is not a measurement row.

Add multi-cycle, luteal-clamp and fertile-window fixtures.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

src/app/api/cycle/calendar/route.ts

@@ -28,7 +28,8 @@ import {
   goalAllowsFertileWindow,
 } from "@/lib/cycle/dto";
 import { persistPredictionCache } from "@/lib/cycle/prediction-cache";
-import { addDays } from "@/lib/cycle/day-math";
+import { addDays, dayDiff } from "@/lib/cycle/day-math";
+import { BBT_WINDOW } from "@/lib/cycle/types";
 import { DEFAULT_TIMEZONE, moodDateKey } from "@/lib/mood/date-key";
 import { getServerTranslator } from "@/lib/i18n/server-translator";
 import { resolveServerLocale } from "@/lib/i18n/server-locale";
@@ -75,8 +76,21 @@ export const GET = apiHandler(async (request: NextRequest) => {
       where: { userId: user.id, deletedAt: null },
       orderBy: { startDate: "asc" },
     }),
+    // Bound the day-log read to the rendered span plus the symptothermal
+    // lookback — the earlier of `from` and (today − BBT_WINDOW). Cycle-length
+    // stats run off MenstrualCycle rows, so day-logs can be windowed (QA: perf
+    // — unbounded full-history read on a hot, per-navigation route).
     prisma.cycleDayLog.findMany({
-      where: { userId: user.id, deletedAt: null },
+      where: {
+        userId: user.id,
+        deletedAt: null,
+        date: {
+          gte:
+            dayDiff(from, addDays(today, -BBT_WINDOW)) <= 0
+              ? from
+              : addDays(today, -BBT_WINDOW),
+        },
+      },
       orderBy: { date: "asc" },
       select: {
         date: true,

src/app/api/cycle/insights/route.ts

@@ -35,6 +35,7 @@ import {
   discoverPhaseCorrelations,
   selectHeadlinePhaseRow,
   PHASE_CROSSTAB_METRIC_TYPES,
+  MOOD_CHANNEL_KEY,
 } from "@/lib/cycle/phase-crosstab";
 import type { CrossMetricMeasurement } from "@/lib/insights/mood-aggregates";
 import { addDays } from "@/lib/cycle/day-math";
@@ -56,14 +57,24 @@ export const GET = apiHandler(async () => {
   const today = moodDateKey(new Date(), tz);
   const from = addDays(today, -WINDOW_DAYS);
 
-  const [cycles, dayLogRows, nightlyTempRows, measurementRows, prefsRow] =
-    await Promise.all([
+  const [
+    cycles,
+    dayLogRows,
+    nightlyTempRows,
+    measurementRows,
+    prefsRow,
+    moodRows,
+  ] = await Promise.all([
       prisma.menstrualCycle.findMany({
         where: { userId: user.id, deletedAt: null },
         orderBy: { startDate: "asc" },
       }),
+      // Bound the day-log read to the rendered window (`from` = today − 365d),
+      // which is already earlier than the symptothermal lookback. Cycle-length
+      // stats run off MenstrualCycle rows so the day-logs can be windowed (QA:
+      // perf — unbounded full-history read).
       prisma.cycleDayLog.findMany({
-        where: { userId: user.id, deletedAt: null },
+        where: { userId: user.id, deletedAt: null, date: { gte: from } },
         orderBy: { date: "asc" },
         select: {
           date: true,
@@ -112,6 +123,18 @@ export const GET = apiHandler(async () => {
         where: { id: user.id },
         select: { sourcePriorityJson: true },
       }),
+      // MOOD outcome (QA HIGH): mood lives in MoodEntry (1–5 score), not a
+      // Measurement row, so read it here and inject it into the crosstab as a
+      // synthetic MOOD_CHANNEL_KEY measurement (same FDR / day-floor guards).
+      prisma.moodEntry.findMany({
+        where: {
+          userId: user.id,
+          deletedAt: null,
+          moodLoggedAt: { gte: new Date(Date.parse(`${from}T00:00:00Z`)) },
+        },
+        orderBy: { moodLoggedAt: "asc" },
+        select: { score: true, moodLoggedAt: true },
+      }),
     ]);
 
   // The latest open cycle runs to the predicted next-period start so the
@@ -146,6 +169,18 @@ export const GET = apiHandler(async () => {
     source: m.source,
     deviceType: m.deviceType,
   }));
+  // Inject mood as a synthetic MOOD_CHANNEL_KEY series (no source ladder — the
+  // metricDayMap pass-through keeps every row, averaging multiple same-day
+  // entries like every other channel).
+  for (const m of moodRows) {
+    measurements.push({
+      type: MOOD_CHANNEL_KEY,
+      value: m.score,
+      measuredAt: m.moodLoggedAt,
+      source: null,
+      deviceType: null,
+    });
+  }
 
   const userPriorityJson = prefsRow?.sourcePriorityJson ?? null;