The tag × metric crosstab summed cumulative metrics (steps, active
energy, sleep duration) per day across every source with no canonical
pick, so once two sources reported the same day — Fitbit + Apple steps,
Fitbit + WHOOP sleep — the per-day total double-counted and inflated the
with/without averages and the Welch delta the surface ranks on. Run the
metric rows through the same per-day source-priority + device-type picker
the analytics steps/sleep path uses before bucketing, keyed by the
metric's priority ladder and the user's Berlin calendar day. Same-source
per-stage rows still sum into the night total; only cross-source twins
collapse. Thread the user's source-priority blob through the read so the
pick honours the configured ladder.
The cross-metric read was a flat `take: 5000` over the 365-day window
ordered `measuredAt desc`, unioned across per-stage sleep + per-sample
pulse/HRV; a multi-source, high-frequency user blew past it and silently
dropped the oldest months, biasing the crosstab and the long-window
correlations toward recent data. Lift the cap to cover the realistic
worst case for the window and annotate a wide event when the cap is hit
so a truncated read is observable rather than silently wrong. The read
stays raw rather than moving to the DAY rollup tier because that tier
buckets on UTC midnight while these aggregates key on the user's Berlin
day, which would skew the day pairing at the boundary.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
(cherry picked from commit aa72ffba6e3bcac9f97be4adc26d689d4f352e98)
Migration 0121 replaces the MedicationIntakeEvent unique constraint with a
partial unique index `WHERE deleted_at IS NULL`. A slot the user previously
deleted (tombstoned) no longer occupies the unique slot, so a re-take
re-creates the row cleanly instead of P2002-ing against the tombstone and
500-ing. The migration dedups any pre-existing duplicate live rows first
(keep newest, tombstone the rest) so the unique build can't fail on real
data.
Measurement and Workout deliberately stay full uniques: Measurement's
compound-key writes use prisma.upsert, which Prisma 7 compiles to native
INSERT ... ON CONFLICT and Postgres cannot arbiter against a partial unique
(and consolidate-daily-mean intentionally resurrects a tombstoned canonical
row, which needs the full unique); Workout has no deleted_at column. The
rationale is documented in the migration header and the schema comments.
Migration 0122 adds MoodEntry.externalId plus a NULL-distinct
`@@unique([userId, source, externalId])`. The moodLog webhook, the pull sync,
and the JSON import now carry the source's stable id into externalId and
upsert on the new key when present, so a re-import is idempotent even when
moodLoggedAt re-rounds or re-zones. Native/MANUAL entries keep their NULL
externalId and stay on the legacy (userId, date, moodLoggedAt) path.
Migration 0123 adds AuditLog(userId, action, createdAt desc) and
MoodEntry(userId, moodLoggedAt) composite indexes for the status-cache and
mood-insights read paths.
Also corrects the stale "v1.6.0 drops this column" note on
MedicationSchedule.daysOfWeek (the column is still dual-written and read by
live paths, so it is retained) and documents the UTC-only timezone storage
invariant in a schema header comment.
Integration tests cover the intake re-take after delete (no resurrection /
no 500) and mood re-import idempotency (with and without an external id) on
real Postgres.
(cherry picked from commit 9a6723f469bdec063a99cd08df132e07ec3c95a5)
Close the launch-blocking gap where the ConsentReceipt infrastructure was
built but never enforced as a precondition: a direct API caller with a valid
token could forward a health snapshot to a server-managed external LLM with no
receipt on file. Consent was enforced client-side only.
Add a consent guard (src/lib/ai/consent-guard.ts) and call it before the first
external-provider call on the server-managed path. The gate fires only when the
resolved chain could egress via the operator's global key (admin-openai); a
user's own BYOK key (openai/anthropic), their own ChatGPT OAuth account, and
the self-hosted local provider stay ungated — that egress is the user's own act
and the settings flow is its consent. The check fails closed: a BYOK-primary
chain with an admin-openai fallback still requires a receipt, since the runner
may cascade to the operator key. The surface maps to the consent kind (coach ->
ai_coach, insights -> ai_insights_only; master ai_full satisfies either).
- Interactive routes (insights/chat, insights/generate POST) throw
ConsentRequiredError; api-handler renders a 403 with
meta.errorCode = "consent.ai.required", mirroring assistant.disabled.* so
the client can render an inline grant-consent notice.
- Off-request pipelines fail closed quietly: comprehensive generation returns a
typed skipped:no-consent outcome, and the per-metric status family
(runStatusCompletion, gating every status card + the period-narrative warm +
the off-budget Coach memory workers) surfaces the no-key fallback instead of
egressing.
Also rate-limit POST /api/fitbit/sync (the one Fitbit route without a limiter):
a 5/60s baseline plus a tighter 1/hour bucket on the expensive fullSync walk,
keyed by user id, matching the sibling Fitbit routes.
Harden the Fitbit OAuth redirect_uri: assert it is an absolute https origin (or
http on localhost), lands on /api/fitbit/callback, and stays same-origin with
NEXT_PUBLIC_APP_URL — defence-in-depth behind Google's registered-URI check so
a Host-coerced or misconfigured app URL cannot redirect the authorization code
off-origin.
(cherry picked from commit 46b5fc2cea1b6e80bee0445bfdacf5790442c67d)
Three hardening + performance fixes to the Fitbit/Google Health sync.
Stop soft-delete resurrection. The measurement write probed the existing
row by the `(userId, type, source, externalId)` unique key without excluding
soft-deleted rows, so a reading the user deleted came back on the next hourly
sync (the upsert matched the tombstone and took the update branch — silently
undoing the delete). The write now probes only live rows (`deletedAt: null`)
and treats a tombstone as absent, minting a fresh insert instead. The fresh
insert relies on the partial unique index over `deleted_at IS NULL` to keep
live-row uniqueness; the tombstone sits outside it, and `skipDuplicates` guards
a live-row race.
Batch the backfill writes + collapse the rollup recompute. The write loop
was one upsert per reading (N+1) followed by a per-(type,day) DAY-rollup
recompute, so a multi-year backfill paid thousands of serial round-trips on a
concurrency-1 worker. The existence probe is now a single `findMany` over the
batch; fresh rows go through chunked `createMany`; only already-live rows take a
per-row update for their differing values. On a `fullSync` the per-write rollup
hook is deferred and the touched type-days collapse into one
`recomputeUserRollups(from, to)` pass at the end of the cycle. The incremental
path keeps the inline per-day hook. The `stats:` overwrite contract, the
single-watermark `markSynced`, and the all-403 soft-skip guard are unchanged.
Bound the hourly poll concurrency. The poll synced every connection in one
serial loop, so a single slow Google response stalled the whole cohort. The
cohort now fans out through a bounded `p-limit` pool with per-user error
isolation, extracted into `runFitbitPollCohort` so the contract is unit testable
without exporting worker internals. The accumulators are read after the await to
avoid a compound-assignment lost-update across overlapping pool tasks.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
(cherry picked from commit 8369af2471fb8da04a938b5a784433318d81a248)
Harden the native auth-token surface against four confirmed findings.
- /api/auth/logout was a no-op for a bearer credential: destroySession only
clears the cookie, leaving an Authorization: Bearer hlk_ token live until
expiry. When the request carries a bearer, also flip the matching
ApiToken.revoked and revoke its paired refresh-token sibling. Cookie path
unchanged.
- A browser spoofing X-Client-Type: native was handed a 60-day refresh token
in the JSON body. Gate refresh-token issuance on a new
isCookielessNativeCaller check (no Mozilla UA, no inbound session cookie),
so the secret reaches genuine cookie-less native callers only and never
alongside a browser session. The iOS native path is unaffected; a spoofing
browser falls back to the short-lived access token with no refresh.
- Refresh-reuse revoke trusted the stored row's deviceId, so a stolen token
replayed under a fabricated X-Device-Id confined the revoke to the
attacker's own id and left the victim's family live. On a present-but-
mismatched deviceId, escalate to the user-wide family revoke (a real device
never changes its id mid-family). The matching-id and null-id cases keep
their existing scope.
- Tighten the auth:refresh rate limit from 60/15min to 10/15min, aligning the
high-value rotation endpoint with the passkey-verify tier.
Add coverage for each: logout revokes bearer + sibling, a cookie/Mozilla
caller never receives a refresh token, a device-mismatch replay triggers the
wide revoke, and the cookie-less native path still gets its refresh token.
(cherry picked from commit 14dcdefc5f40855305d8aaf573d073faebc61476)
Use FITBIT_ACTIVITY_PAGE_SIZE for the sleep and exercise reads instead of a
magic 25, remove the unused FitbitDataTypeKey type, and declare the crosstab
day floors independently so they aren't a duplicate re-export of the influence
floors.
Snapshot the incremental watermark once per sync cycle and thread the same
lower bound to every resource (metrics, activity, sleep, workout), stamping
lastSyncedAt once at the end. Previously each resource read lastSyncedAt then
stamped now() independently, so the first resource advanced the watermark and
later resources only fetched the last overlap window — silently dropping the
gap after an outage longer than the overlap. The full/backfill path is
unaffected (it passes no lower bound).
Reject an OAuth callback whose token response carries no refresh_token instead
of persisting an empty string. An empty refresh token bricks every future
token refresh; the callback now errors out cleanly so the user re-consents
rather than landing a dead connection.
Model the INTERVAL data types (steps/distance/calories/floors daily totals,
sleep and exercise sessions) on Google Health's interval.start_time /
interval.civil_start_time anchor instead of a sample_time / bare date. The
incremental filter for sleep/exercise previously targeted
sample_time.physical_time, which 400s/empties for interval types and stalls
incremental sync. Daily-summary metrics and spot readings keep their existing
date / sample anchors. Shape mismatches still fail safe (return []/null).
Mark the Google Health (Fitbit & Pixel) settings card as experimental with a
badge and a short note that data coverage is still being verified, in all six
locales.
(cherry picked from commit 3f116802149eebe9144729e2779f7f4405f248f2)
Thread the acting user id through createTagLinks, replaceTagLinks, and
replaceRatedFactorLinks, and assert the target MoodEntry belongs to that
user before writing any link. The check runs against the same client (tx
or singleton) as the write, so it shares one snapshot. Behaviour is
unchanged for the existing POST and bulk paths, where the entry is freshly
created and owned; the guard closes a latent cross-user link hazard for
the planned PATCH mood-edit route. A mismatched or missing entry throws
MoodEntryOwnershipError before any catalog or join touch.
Add IF NOT EXISTS to every ADD COLUMN in migration 0119 so a clean replay
is idempotent, matching the 0117 convention.
(cherry picked from commit 1bf983f6c2c5e44796c99983d57c1b1d90cd7099)
Extend the mood-relations surface from mood to a health metric. For each
structured mood tag present on enough days, compare a metric's value on
tag-present vs tag-absent days: active energy on workout days (same day),
sleep length on nights tagged sleep (same day), and next-day recovery
after a tag like alcohol or food (D to D+1 lag). The comparison reuses the
existing statistics engine wholesale — the Welch two-sample t-test, the
per-group day floors, the confidence band, and the Benjamini-Hochberg FDR
control already used by the influence and discovery boards — so no new
statistics code lands. Only pairs that clear p < 0.05 and the FDR control
surface, ranked by adjusted q then absolute delta and capped at eight rows.
The result rides on GET /api/mood/insights as a new additive
`tagMetricCrosstab` field and renders as a confidence-gated, observational
card on the mood insights page, framed as an association in your own data
rather than a cause. The endpoint is server-rendered and not in the OpenAPI
contract, so no contract change is required.
Deprecate the standalone moodLog integration. Mood is now tracked fully
inside HealthLog through entries, structured tags, and rated factors, so
the external webhook and reverse-sync bridge no longer add anything. The
webhook route, the push and pull helpers, and the validation schemas carry
an @deprecated note pointing at the native path; the surface stays
functional this release and is slated for removal in a future major. The
settings card shows a deprecation hint, localised across all six locales.
Extract the Coach chat surface (header actions, message thread,
composer, provenance rails, settings, mobile trays) into a shared
<CoachConversation> component so the drawer and a new full-page route
render the exact same implementation — no forked chat logic. The drawer
keeps only its <Sheet> chrome (close button + maximize control); the
page supplies a minimize-back-to-drawer control.
The maximize control in the drawer header navigates to /insights/coach
and closes the drawer (aborting any in-flight stream); the page's
minimize control routes back to /insights and reopens the drawer through
the shared launch context. Both surfaces preserve incremental streaming,
collapsible history + provenance, the single composer disclaimer, the
auto-grow composer, refusal / budget handling, and the aiMode gating —
the full page redirects to /insights when the operator flag is off or
the user has opted out.
Lift useResettableValue / nextResettableValue into their own module so
the shared surface seeds its composer from the same controlled-prefill
contract without importing the drawer shell. Register the new route in
the coach feature-flag and per-user-disable gate-site invariants, and
add the maximize / minimize labels across all six locales.
A weekly injectable (e.g. a GLP-1 dose) with real recorded intakes
reported 0% compliance on the medication card across every window
(7 / 30 / 90 days). The web reads the server-computed rate verbatim,
so the server value itself was wrong.
The intake-to-slot matcher used a fixed +/-12h pairing radius. That is
correct for a daily cadence (24h inter-slot gap) but far too tight for a
once-weekly dose: a real intake is rarely logged within 12h of the
schedule's configured time-of-day, because the user takes the shot on
whichever day and time of the dosing week suits them. Those intakes fell
outside the radius, every weekly slot read "missed", and the rate
collapsed to 0% while the matching intakes were orphaned.
Scale the pairing radius with the cadence gap. `pairDoses` now derives a
per-slot radius from half the distance to its nearer neighbouring slot
(so two adjacent slots never double-claim one intake), floored at the
12h base so daily and multi-dose-daily cadences keep their exact prior
behaviour. `buildCadenceTimeline` additionally probes the schedule's
intrinsic cadence over a padded window and passes half the minimum
inter-slot gap as a radius floor, so a window holding a single expected
slot (a weekly med over a 7-day window) widens correctly too.
A weekly cadence now pairs an intake logged anywhere in the dosing week
to that week's slot, reporting the true rate. Daily-cadence matching is
unchanged.
Adds unit coverage for the widened radius and a real-Postgres integration
test mirroring the weekly-injectable row + off-time intakes.
Extend the structured mood taxonomy so a tag can carry a magnitude. A
rated factor is a MoodTag of kind RATED that the user scores per entry;
the score persists on MoodEntryTagLink.rating. Binary tags keep
kind BINARY and a null rating, so every existing row and read path stays
byte-identical.
Migration 0119 is additive: four marker columns on mood_tags (kind,
scale_min, scale_max, inverse), a nullable rating on mood_entry_tag_links,
and a seeded factors category with five MVP factors (work, social, sleep
quality, stress, conflict). Stress and conflict are inverse-scaled
(higher means worse); conflict is a 1..2 Yes/No factor.
Write contract: POST /api/mood-entries and the bulk route accept an
optional ratedFactors: [{ key, rating }] array parallel to the binary
tagKeys. The resolver builds the join rows field-by-field, drops unknown
and non-RATED keys silently, and rejects a rating outside the factor's
own scaleMin..scaleMax (422 on the single-entry path, per-entry skipped
on bulk). Create and list responses surface the persisted factors split
from binary keys so a client hydrates without a refetch. GET
/api/mood/tags now emits kind, scaleMin, scaleMax, and inverse so a
client can render factors versus tags.
This is the ingestion half; analytics over rated factors follow later.
Factor labels ship in all six locales.
A daily oral with multiple timesOfDay and a wide schedule window had its
dose-slot capture zones overlap, so a second same-day dose could snap onto
an already-actioned slot and be dropped by the no-downgrade guard — the
intake never landed as its own row. A twice-daily 08:00 / 20:00 schedule
with an 08:00-22:00 window yielded a +/-7h half-span tolerance that
exceeded half the 12h inter-slot gap, so the evening write collapsed onto
the morning slot. Weekly and single-dose schedules (one slot/day) were
immune.
snapToleranceMs now caps the tolerance at half the minimum gap between
adjacent sorted timesOfDay entries (the midnight wrap counted as one of
those gaps) whenever a schedule has more than one slot, keeping the two
nearest slots' capture zones disjoint. Single-slot schedules keep the
full half-window-span behaviour, and the >=1-minute drift floor is
preserved, so weekly injectables and once-daily orals are unchanged.
Adds unit coverage for the wide-window two-dose case (distinct slots, no
collapse) plus an explicit weekly single-dose regression, and an
integration test driving both same-day writes through the DB-backed slot
resolver and canonical-slot upsert to assert two distinct live rows.
Extend the Fitbit / Google Health provider with the remaining sync
resources, driven by the existing syncUserFitbit orchestrator.
Activity (daily cumulative): steps -> ACTIVITY_STEPS, distance ->
WALKING_RUNNING_DISTANCE, the active-energy portion -> ACTIVE_ENERGY_BURNED,
floors -> FLIGHTS_CLIMBED, and VO2 max -> VO2_MAX. Each daily total mints a
stats:<tag>:<YYYY-MM-DD> externalId so a re-fetched day overwrites in place
rather than duplicating, matching the Apple-Health daily-total contract. The
running totals preserve a legitimate zero (a rest day is real data); VO2 max
stays strictly positive (daily latest-wins).
Sleep: map a session's per-stage segments to per-stage SLEEP_DURATION rows
(minutes; measuredAt = the stage end), harmonised onto the shared SleepStage
enum (light -> CORE, deep -> DEEP, rem -> REM, awake/restless -> AWAKE,
in-bed -> IN_BED, classic asleep -> ASLEEP) so the night-total and hypnogram
readers consume the same shape they already read for WHOOP and Apple.
Workouts: map exercise sessions to Workout rows keyed (userId, FITBIT,
externalId), with sport-type, start/end, duration, energy, distance, and HR.
Cross-source twins stay distinct at ingest; the read-time canonical picker
collapses them via the source ladder (Fitbit already ranked below WHOOP).
All three join the orchestrator's resource list and ride the per-resource
403 soft-skip so an independently-granted Restricted bundle that the user
withheld skips only its own resource. Rollups recompute and status-insight
caches invalidate through the shared upsert. No new MeasurementType, no
migration, no new dependency.
Extend the Fitbit/Google Health provider with the read path: the
dataPoints.list client half, the metrics-bundle sync, the poll + backfill
jobs, and the connection-management routes. Builds on the existing OAuth +
credentials + token-refresh layer.
- client: paginated `fetchDataPoints` walker over `safeFetch` keyed on
`nextPageToken`, encoding each data type kebab-case in the path and
snake_case in the filter predicate; defensive per-type mappers (finite +
positive guards) for weight, body fat, daily SpO2, daily HRV, daily resting
heart rate, daily respiratory rate, intraday heart rate, sleep-temperature
derivations, and a height profile-seed extractor. `FITBIT_FIELD_MAP` +
`mapping.md` are the source of truth.
- mapping (no new MeasurementType): weight→WEIGHT, body-fat→BODY_FAT,
SpO2→OXYGEN_SATURATION, HRV→HEART_RATE_VARIABILITY (SDNN slot, not
HRV_RMSSD), resting-HR→RESTING_HEART_RATE, respiratory-rate→RESPIRATORY_RATE,
heart-rate→PULSE, sleep-temp→WRIST_TEMPERATURE, height→User.heightCm when
null. Steps/distance/energy/floors/VO2 + sleep + workouts stay for a later
wave.
- sync: `syncUserFitbit` orchestrator with the reauth short-circuit, the
per-data-class 403 soft-skip (the six Restricted bundles grant
independently), the all-soft-skipped success guard, and incremental-vs-full
windows; `upsertFitbitMeasurements` keyed (userId,type,FITBIT,externalId)
with a 24 h overlap for after-the-fact daily re-rolls, recomputing rollups
and invalidating status caches. Google refresh tokens do not rotate, so the
stored refresh token is kept unless a new one is returned.
- jobs: hourly `fitbit-sync` poll (poll-only; Pub/Sub deferred), the
self-converging boot backfill, and the daily OAuth-state sweep — every queue
registered in both `allQueues` and a `boss.work` binding, staggered off the
WHOOP/Withings crons.
- routes: status / sync / disconnect mirroring the WHOOP shape.
Tests cover the mappers, the casing invariant, `fetchDataPoints`, the
orchestrator soft-skip guard, the idempotent upsert, the backfill discovery,
and the queue registration; an integration test pins the upsert idempotency,
the DAY rollup fold, and the Fitbit-vs-Withings per-source no-collapse against
the Postgres testcontainer.
Land the OAuth + credentials half of the Fitbit / Google Health provider,
cloning the WHOOP provider structure and adapting it to the Google identity
flow.
- Client OAuth half (src/lib/fitbit/client.ts): Google authorize-URL builder
(access_type=offline + prompt=consent), authorization-code exchange, token
refresh, and a profile fetch for the connection's external user id. Client
credentials ride in an HTTP Basic-auth header on the token endpoint; the
four launch Restricted read scopes are pinned in FITBIT_OAUTH_SCOPE. All
egress goes through safeFetch.
- Non-rotating refresh tokens: getValidToken refreshes at expiry-minus-five-
minutes, persists the new access token + expiry, and overwrites the stored
refresh token ONLY when the response carries a fresh one — Google keeps the
refresh token stable, unlike WHOOP.
- BYO credentials route (PUT/GET/DELETE /api/fitbit/credentials): per-user
client id/secret stored encrypted on the User columns, built field-by-field
under requireAuth.
- OAuth state + connect + callback routes: a random base64url nonce backed by
a 10-minute FitbitOAuthState ledger row carries the (nonce -> userId)
mapping; the callback validates the cookie/URL state with timingSafeEqual,
consumes the row atomically (replay/expired/cross-user reason tags),
exchanges the code, upserts the encrypted FitbitConnection, clears prior
reauth state, and enqueues the self-converging history backfill.
- Response classifier mirrors WHOOP's HTTP-status buckets; Google signals a
revoked grant with a 401, so there is no invalid_grant body special-case.
Tests cover the authorize URL, Basic-auth token exchange, the no-rotation
refresh branch (token preserved when omitted, overwritten when present), the
classifier status table, and the OAuth-state nonce mint.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Swap the hand-authored body silhouette in the injection-site picker for a
clean, gender-neutral medical line-art figure with correct proportions
(arms to mid-thigh, head-to-body ≈1:7.5, separated legs, feet). The figure
ships as a small transparent raster (~8 KB) and is painted theme-aware: its
alpha drives an SVG mask over a currentColor rect, so the strokes take the
picker's text colour and tint correctly in light and dark.
Keeping the figure inside the SVG (rather than a CSS-masked sibling) means
it shares the dots' 100×200 viewBox, so alignment stays responsive by
construction. Recalibrate SITE_COORDS so the upper-arm, abdomen-quadrant
and thigh anchors land on the new figure's anatomy; the pointer overlay,
keyboard targets, recommendation ring, last-used marker and disabled-set
logic are unchanged.
Lay the data-model foundation for the Fitbit/Pixel integration over the
Google Health API, mirroring the WHOOP provider. No OAuth, sync, or UI yet.
- Prisma: FitbitConnection (1:1 per user, encrypted access/refresh tokens)
and FitbitOAuthState models, two encrypted BYO-credential columns on User,
and FITBIT appended to the MeasurementSource enum. Migration
0117_v1120_fitbit_integration is additive only — one enum value plus two
tables and two nullable columns, no new MeasurementType (every launch
metric maps onto an existing type).
- Wire the two new encrypted columns and the FitbitConnection token columns
into scripts/rotate-encryption-key.ts.
- FITBIT touchpoints: the read measurementSourceEnum (not the writable set),
the DEFAULT_SOURCE_PRIORITY and workout-source ladders, the sources-section
label map, the IntegrationKey union plus its display name, and a
fitbit()/fitbitStatus() query-key pair. The source-rank SQL and read-tier
collapse pick FITBIT up from the ladders with no code change.
- Add the FITBIT source label to all six locale bundles and regenerate the
OpenAPI contract so the source enum carries FITBIT for the iOS decoder.
- Mood discovery footer no longer pairs a mood-only numerator with the
full behaviour-by-outcome denominator. Reword it to surface the mood
relationships out of all day-to-day pairs the engine tested across
every metric, so the count it reports is honest about its scope.
Update all six locales.
- Rank the better-days board on a comparable scale: standardize the tag
mood-point delta into a Cohen's-d effect (delta over the pooled
with/without mood SD, clamped to [0,1]) so it sits alongside a metric
|r| instead of the old |delta|/2 heuristic. Fall back to |delta|/2 only
when the pooled SD is undefined. The pooled SD is ranking-only and is
stripped before the row enters the snapshot payload; the raw delta and
r the cards show are unchanged.
- Normalize the Withings and Whoop resume buttons to the min-h-11 scale
token, dropping the arbitrary min-h-[44px] spelling.
- Give the API token copy button a 44px tap target on small viewports,
scaling back to the tighter icon size from sm up.
- Drop the dead cached prop from InsightStatusCard and every caller; the
card has carried no cached affordance since the badge was removed.
Apple Health writes an unspecified ASLEEP aggregate alongside the granular
CORE/DEEP/REM partition for the same period. The asleep total already preferred
the granular partition, but the per-night/per-session `stages` map and the
hypnogram `segments` list recorded every stage row unconditionally, so a night
ended up carrying the granular set PLUS its ~equal bare twin — a double-height
green ASLEEP block on the stacked bar and the hypnogram, and a doubled total in
the analytics stage breakdown. Route the `stages` map and the `segments` list
through the same granular-over-bare gate as the asleep total: when a session
carries a granular stage, drop the redundant bare ASLEEP (and stage-less) rows
everywhere. WHOOP never emits a bare ASLEEP, so WHOOP nights are unaffected.
Apply the gate per session, not over the merged night pool, so a granular
overnight no longer strips a same-day bare-only nap's minutes from the night
total. Render the hypnogram per-segment in the stage palette (one ReferenceArea
per segment) instead of a single flat cyan line, and show the breakdown plus
totals footer whenever any per-stage minutes exist — reserving the unavailable
state for a truly empty session — so a legacy stage-summary night still surfaces
its breakdown. Thread the user's source priority into the CSV export's sleep
dedup, and honour offset with a true pre-slice night count on the sleep list.
GET /api/sleep/night?date=YYYY-MM-DD returns one reconstructed night for
the phase-progression view: the canonical source's stage segments (each
with an absolute start/end span), the per-stage breakdown, the asleep/
in-bed/awake totals, the mid-sleep awakenings count, and same-wake-day
naps surfaced separately. Session-clustered, wake-day-keyed, one
canonical source per night so two sources never overlay. date omitted
returns the most recent night. A view over existing per-stage rows — no
schema, no new measurement type. OpenAPI regenerated.
The CSV + JSON export emitted a wall of per-stage sleep minutes with no
night attribution. Collapse SLEEP_DURATION to one record per night by
default — the main session's TIME ASLEEP per the nap convention — with
the stage breakdown, nap count, and awakenings in the notes column so
the single uniform export reads as one night. `granularity=raw` keeps
the per-stage rows for power users. Storage is untouched.
Add reconstructSleepSessions — the session-level sibling to
reconstructSleepNights that keeps each sleep session separate (rather
than merging same-wake-day sessions under one key) and resolves every
stage row to its absolute span (start = measuredAt − duration), picks
one canonical source per session, and counts mid-sleep awakenings.
Add pickMainNightAndNaps to make the nap convention explicit: the main
night is the session with the most asleep minutes; every other session
on the same wake day is a nap, surfaced separately and never folded into
the headline.
These views are pure functions over existing per-stage rows — no schema
change, no new measurement type. They back the per-night list collapse,
the analytics reconciliation, and the hypnogram.
After the graceful collection-403 soft-skip, a genuine grant revoke 403s every
data class: each resource soft-skips (returns 0, records no failure), so the
orchestrator sees no error and stamps recordSyncSuccess — leaving the dead
connection looking healthy until the token-refresh path catches the 401 up to
~1 h later. Track the soft-skip count across the resource loop via an
AsyncLocalStorage tracker scoped to the cycle; when every resource soft-skipped
and nothing imported, leave the status as-is instead of stamping success. A
partial cycle (any import, or any resource that did not soft-skip) stamps as
normal.
A revoked refresh token returns HTTP 400 with an OAuth body of
`error: invalid_grant` from the token endpoint. The status-only classifier
buckets every 400 as persistent, so the connection never prompts a reconnect
even though the grant is gone. Store the upstream OAuth error on WhoopApiError
and lift a 400 invalid_grant to reauth_required in classifyWhoopError; every
other 400 (a malformed request, invalid_client) stays persistent.
Apple Health (and some WHOOP shapes) write BOTH an unspecified ASLEEP
aggregate row AND the granular CORE/DEEP/REM breakdown for the same
sleep period. The per-night reconstruction summed both, so a real ~8 h
night surfaced as ~16 h across every sleep surface — the chart showed
~16-18 h points and the target-range 30-day average read ~16.4 h/day.
Prefer the granular partition: when any CORE/DEEP/REM stage is present
for a night/source, count only those; fall back to the bare ASLEEP (or
a stage-less legacy/manual) row only when no granular stage exists.
IN_BED and AWAKE stay excluded; dual-source nights still collapse to one
canonical source via the user's sleep priority ladder. The same rule
applies inside the per-source asleep tiebreak so a source writing both
shapes is not over-weighted.
Align the surfaces that bypassed the shared helper:
- insights/targets: SLEEP_DURATION was averaged as raw per-stage rows
(minutes) and fed into a target band expressed in hours, producing the
bogus 16.4 h/day. Reconstruct per-night time-asleep, convert to hours,
and classify + roll up consistency on the nightly hour totals.
- coach snapshot: route the sleep block through reconstructSleepNights
so the prompt narrates the same nightly numbers (was summing every
stage row, including IN_BED + AWAKE).
- sleep card chart: read /api/measurements/series?kind=sleep (night
reconstruction, hours) instead of the generic measurement
daily-average path, and label the axis in hours instead of minutes.
Add regression fixtures: a night with both a bare ASLEEP row and the
granular breakdown asserts the granular total (no double-count); a
dual-source night with both shapes per source; and a multi-night 30-day
average that lands at ~8 h, not ~16 h.
Two changes that turn the descriptive relations surfaces into a
statistically-gated discovery view on the mood page.
Mood as an outcome: the FDR-controlled all-pairs correlation discovery
engine treated mood only as a behaviour (a lag source). Promote it to an
outcome channel too, so the scan also surfaces "behaviour today → next-day
mood" relations (e.g. more daylight today → better mood tomorrow) alongside
the existing "mood today → next-day outcome" direction. The self-pair is
skipped and Benjamini-Hochberg already controls the larger pair family this
opens up. The discovery route now feeds the shared mood daily-mean series
into both roles.
Surface the discovery slice: the engine was computed but never rendered.
The mood Insights page now shows the mood-involving pairs that survive the
false-discovery correction, each phrased observationally with its paired-day
count, r, and BH q-value, plus the honest "X of N tested" footer. The
surface self-fetches the correlation route and degrades to nothing when the
operator has disabled it. Observational only, with the standing disclaimer.
Two new observational surfaces on the mood Insights page, both computed in
the single mood-aggregates engine so the charts and the coach snapshot stay
in sync.
Influence on mood (per tag): for each frequent flat and structured tag,
compare the average daily mood on days the tag is present against the
counterfactual baseline of days it is absent, with the delta and a
confidence band. The band comes from a Welch two-sample t-test reusing the
exact Student-t p-value already behind the correlation cards, not a naive
approximation. Both groups must clear a five-day floor; rows rank by
absolute delta and cap per axis. Sparse tags, single-day tags,
all-same-value days, and zero-delta tags are dropped cleanly — never NaN.
Better days board: one ranked list folding the tag deltas and the
mood x health-metric correlations (sleep, steps, pulse, weight, blood
pressure) into a single effect-size-ranked, confidence-gated surface — the
headline answer to "what goes with my good days?". Near-zero and
under-sampled correlations are excluded; the list is capped and observational
only, with the standing "association, not cause" caption.
Both surfaces ride the mood-status snapshot too so the assessment prose
never contradicts the on-screen relations. New copy in all six locales.
The target reference card carried an inline "Adjust target range" link,
which read as a control buried in a read surface. Lift the action to a
gear button in the category-page header, to the left of the Coach launch
icon, so the two sit as a consistent icon cluster beside the heading.
A small page-scoped provider bridges the two: the reference card
registers its editable target (one per page, except blood glucose which
fans out to several contexts and registers each) and the header gear
opens the per-metric editor sheet the provider owns. Single-target pages
open that target directly; glucose opens the primary context, since
target editing lives only on these pages — there is no separate targets
management surface. The card itself is now a pure read surface.
The gear carries the action's existing label as its accessible name,
clears the 44 px touch-target floor on mobile, and exposes a stable
`data-slot` for end-to-end coverage.
Per-stage SLEEP_DURATION rows carry the stage segment's END instant, so a
night where the user falls asleep before local midnight spread its stages
across two calendar days. Keying each stage by its own day split one
overnight sleep into two partial nights, and the headline last-night tile
showed only the post-midnight slice.
Reconstruct nights by clustering stage segments into sessions: a new session
starts only when the next segment's start (end minus its minute duration) is
more than three hours after the running session end. A single long stage
block no longer splits a night, a daytime nap stays separable from the
following overnight block, and the gap is measured on the absolute instant so
the clustering is DST-immune. Each session is keyed by the local wake day of
its latest segment end, matching the convention that a sleep session belongs
to the morning you wake up.
Collapse a multi-source night to one canonical source before summing, using
the user's sleep source-priority ladder (default WHOOP > Apple Health >
Withings), so a dual-source account no longer double-counts the total. Sources
are never blended within a night.
Bound the series sleep read to one year even on the 'Alle' range, matching the
slim slice's sleep window, so a multi-year dual-source account does not walk a
six-figure stage-row set into JS.
Add fixtures straddling local midnight, crossing a DST spring-forward, keeping
a nap separable, and collapsing a dual-source night.
Each Trends-row card carries a one-line caption below its mini-chart.
The first choice is the AI advisor's per-metric sentence, but on a cold
briefing — a web-only account, a pre-briefing cached payload, or the
advisor still generating — that sentence is null and every card fell
back to a static "Awaiting more data" hint, even with a full 30-day
series painted right above it.
Add a deterministic, rule-based descriptor derived from the same series
the mini-chart plots, and apply a three-tier caption precedence:
1. advisor annotation present → the AI sentence (unchanged).
2. else, a computable series → the deterministic descriptor:
direction (rising / falling / stable) plus the first-vs-last
magnitude over the window, e.g. "Rising over 30 days (+8 mmHg)",
"Falling over 30 days (−1.4 kg)", "Stable over the last 30 days".
Mood reads on a categorical 1–5 scale, so it is phrased in plain
terms ("Mood trended slightly higher over 30 days") with no raw
point delta.
3. else (genuinely < 2 points) → the real "Awaiting more data" hint.
The descriptor is observational and neutral by construction: direction
and magnitude only, with no causal, diagnostic, or medical claim and no
value judgement — a rising weight and a rising step count read the same
way here. A per-metric stability floor (the larger of an absolute and a
relative bound) keeps day-to-day noise from reading as a trend.
The descriptor logic lives in a pure, unit-tested helper. The caption
reads the same series the chart shows: mood reuses the existing
mood-analytics cache slot, and numeric metrics take a small dedicated
30-day daily-aggregate read keyed under a new query-key factory entry
that joins the measurement-write invalidation set. New descriptor copy
ships in all six locales. The pending shimmer while the advisor is in
flight is unchanged.
SLEEP_DURATION is stored in minutes, one row per sleep stage per night
(IN_BED / AWAKE / ASLEEP / CORE / DEEP / REM) since the HealthKit
category-sample ingest landed. The value is not cumulative, so reading
the single most-recent row surfaced one stage's minutes — never the
night. The dashboard sleep tile and the sleep series therefore showed a
fragment of the night rather than the night.
Add a shared `summarizeSleepNights` helper that groups the per-stage
rows by the wake-day calendar date in the user's timezone and sums the
asleep stages (CORE + DEEP + REM, plus a legacy bare-duration row),
excluding IN_BED and AWAKE per the AASM convention already used by the
sleep score. Wire it into:
- the dashboard summary tile, which now emits last night's time asleep
in hours with an explicit `unit: "h"` and a per-stage breakdown for a
future detail view;
- the sleep series, which now returns one point per night (time asleep
in hours) plus a top-level `unit` for every kind so clients never
infer the unit;
- the slim summaries slice, so the web dashboard tile and the insights
overview read per-night totals (kept in canonical minutes there; the
web tile converts to hours) — strictly more correct than averaging
across stage rows, and parity with the iOS value.
A measurement logged by hand on the iOS app while offline is mirrored
into HealthKit. On pairing a server, adopt-on-pair uploads it as a
MANUAL row while HealthKit background sync independently re-ingests the
same sample as an APPLE_HEALTH row. The two carry different external ids
and sources, so the composite unique index keeps both — doubling the
whole hand-logged history on first pair.
Collapse the pair at ingest: an incoming MANUAL or APPLE_HEALTH row is
dropped as a duplicate when a same-reading row of the opposite source
already exists (same type and value, measuredAt within two seconds).
The match is scoped to that one cross-source pair, so same-source rows
and rows from server-owned sources keep their existing contracts, and
two genuinely distinct readings at the same minute never collapse.
stats:* cumulative rows stay on their per-day overwrite lane.
The collapsed copy returns status "duplicate" with reason
"cross_source_merge" so the client cursor advances unchanged; no
schema, migration, or OpenAPI change.
WHOOP body fields are self-reported profile data; guard mapBody against
absent, NaN/Infinity, or non-positive values so a garbage reading never
seeds a WEIGHT measurement or a profile height of 0.
Extract handleCollectionFetchError so the five resource syncs share one
soft-skip-vs-rethrow path instead of five copies. Behaviour is unchanged:
a per-resource 403 returns 0 and keeps the connection connected, anything
else records a classified failure and rethrows.
The snapshot builder ran its independent reads sequentially on a cache
miss: feature extraction, the shared measurement query, the mood,
compliance, sleep and workout table reads, and the GLP-1, derived,
trajectory and memory helper blocks each awaited in turn. None consumes
another's result, so they now fire concurrently and resolve in batched
hops — feature extraction and the measurement read first (both gate the
per-metric blocks), then the remaining reads in one Promise.all. The
source guards are unchanged, so a disabled source still issues no query,
and the synchronous block assembly keeps its original order, so the
provenance envelope is byte-for-byte the same. Cuts several round-trips
off the cold build; the warm path is cached and unaffected.
The body-measurement endpoint was fetched by a function nobody called, so WHOOP
weight, max heart rate, and height never reached the database despite being
documented in the mapping table.
Add sync-body.ts and wire it into the per-user sync loop (and, through it, the
backfill):
- weight_kilogram → a WEIGHT Measurement (source = WHOOP) on the stable
externalId whoop:body:weight with overwrite semantics. A body measurement is
a single self-reported profile value, not a time series, so a re-sync updates
the same row in place rather than accumulating one row per poll. measuredAt is
the fetch time; the source-priority picker ranks a real scale above WHOOP.
- max_heart_rate → WhoopConnection.maxHeartRate (a profile constant).
- height_meter → User.heightCm (m→cm), written ONLY when the user has no height
yet — a user-set height is never overwritten and height is never minted as a
Measurement.
Every write is field-by-field and idempotent across reruns. Document the body
mapping and the watch-only blood-pressure stance in mapping.md.
A 403 on a single WHOOP collection endpoint was classified reauth_required,
which parked the whole connection at error_reauth and short-circuited every
future sync. A lower-tier user missing one gated data class would brick the
entire integration.
Treat a per-resource collection 403 as a soft skip of that data class: log,
return 0, and continue so sibling resources still sync. Reserve connection-wide
reauth for a 401 (token rejected) and for a 403 on the token-refresh / profile
path, which run outside the per-resource catch blocks and remain unchanged.
Add isCollectionForbidden as the shared gate and cover both branches in tests.
Extend the SSRF pin inventory to also assert the requirePublicHost pins
already present on the user/operator-supplied moodLog push/sync webhooks
and the ntfy sender, so the full set of overridable-host egress points
is CI-guarded and a future edit that drops one reds here.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The Prisma unique-constraint (P2002) narrowing predicate had been
hand-copied into five call sites. Fold them into a single isP2002 in
src/lib/prisma-errors.ts and import it everywhere (the two step-drain
sites keep their local isUniqueConstraintViolation name via an import
alias). Behaviour-identical; one place to chase a future P2002-semantics
change instead of five.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The eight v1.10 additive HealthKit signals (cardio recovery, six-minute
walk, stair ascent/descent speed, breathing disturbances, wrist
temperature, falls, walking steadiness) are writable dashboard widget
ids so the native client's Home-pin PUT validates them, but the web
dashboard renders strip tiles via hardcoded per-id blocks and has no
render path for them. Left as-is, the web Settings -> Dashboard list
would offer eight toggles that do nothing on web.
Name them in a new IOS_PIN_ONLY_WIDGET_IDS set (writable-but-not-
web-rendered) and filter that set out of the rendered settings list, so
the toggles never appear on web while the ids stay accepted by the
widgets PUT enum for the native client. Add tests asserting the eight
remain writable (catalogue membership) yet are absent from the web list.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The synchronous DAY rollup hook carries no pg-boss singleton, so two
measurement writes landing on the same (user, type, day) can interleave
their delete-then-insert and the losing createMany trips the per-source
unique index. The peer rewrote the identical source-collapsed partition
— the aggregate is a pure function of the same underlying rows — so the
rollup is already correct. Catch the P2002 in the single-chunk
transactional path, annotate it, and return rather than failing the
recompute. Any other Prisma error still bubbles.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
- Drop the unused `now` field from the coach `ExtractOpts` type; fact
extraction never read it (conversation-summary keeps its own `now`).
- Correct the no-ladder tiebreak docstring in measurement-read: the
collapse picks the alphabetically smallest source name, matching the
live-SQL `ORDER BY … source` tiebreak, not max count.
- Replace the hand-inlined canonical-source subquery in the summaries
rollup-path narrows query with `canonicalMeasurementsFrom(rank, "90 days")`
for live/rollup parity with its siblings.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Add eight widget ids (cardio recovery, six-minute walk, stair ascent/descent speed, breathing disturbances, wrist temperature, falls, walking steadiness) to the dashboard widget catalogue + default layout (hidden by default) so they can be pinned via the layout PUT.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Load the source-priority blob once and pass it into both
readBestGranularityRollups calls. Completes the B3 fast-path threading.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Load the source-priority blob once and pass it into both BP readers
instead of letting each re-query the user.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Load the source-priority blob once and pass it into the three
readRollupBuckets calls instead of letting each re-query the user.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
