Add the medication-class to target-vital map and the adherence-to-vital
storyline so the Coach can surface, association-only, when an adherence dip
lines up with a drift in the vital that medication targets. Never causal,
never a dose-change suggestion; dose questions defer to the prescriber.
Add an in-repo CUSUM / binary-segmentation changepoint detector over DAY
rollups with a high firing bar (persistence plus within-segment-spread
magnitude) so the Coach can name a dated, sustained level shift instead of a
vague "lately", and stays silent when no break clears the bar.
Surface which source recovery is read from when two sources materially
diverge, with the honest reason, instead of silently averaging.
Lay the n-of-1 experiment substrate: a nullable encrypted outcome column on
coach_plans (migration 0197), the review_due / reviewed lifecycle statuses
wired into the encrypted-column registry and rotation script, and a daily
worker that reads the experiment result back in the user's own before/after
numbers — association-only, honest-null, no cheerlead on an adverse trend,
and routing a worsened vital to the clinician. The user-visible read-back
stays behind COACH_EXPERIMENT_VERDICT until its review cases clear.
Extend the outbound dose screen to catch experiment-shaped dose changes
(halve / skip / stop a medication to test it). Add evaluation cases and unit
tests for each new surface.
Replace the fixed three-beat assessment skeleton with a menu of moves the
model orders freely, and reframe the daily briefing from per-signal
enumeration to a verdict-first read that weaves the few salient signals into
one associative story. Score cards now name both the helped and the hurt
contributors, translate the band into a forward read for the day, and close
on one grounded nudge; the deterministic fallback gains the same band-meaning
sentence so provider-less and demo accounts read as well.
Add a hash-seeded opener-archetype rotation (deterministic, testable) so
consecutive cards and days no longer open the same way, a day-rotated sampling
seed for the period narrative in place of the fixed reference seed, and a
shared paragraph formatting contract. Add a score-number grounding gate and
extend the briefing grounding allow-set to the glucose, labs, preventive-care
and workout aggregate blocks so real figures can be cited without opening the
gate. Gate the daily score warm on an input fingerprint so a quiet day skips
the model call.
The daily briefing's always-on feature payload carried only the core vitals,
mood, sleep, steps and medication compliance, leaving four captured domains
invisible to it. Add four blocks to the aggregated features:
- Labs: recent flagged biomarkers (out-of-range or trending vs the prior
reading). Retired markers are excluded; qualitative results stay neutral with
no fabricated verdict.
- Preventive care: due and overdue items off the server-authoritative next-due
date, so an overdue screening can prompt an early nudge.
- Workouts: a provider-agnostic count, duration and distance aggregate over the
trailing windows (the briefing previously saw activity only as step counts).
- Glucose: the present-and-trend block the other vitals carry, where glucose
previously reached the briefing only as a single signal-of-the-day row.
The three extra reads are scoped to the wide briefing window; the tight
snapshot window that already carries its own context skips them. Every block is
omitted when its domain is empty rather than emitting zeros, and user-entered
labels are sanitised before they reach the prompt.
Add a rolling 14-21 day early-detection pass to the FDR correlation engine
so an emerging shift (illness pulling HRV and sleep down together) surfaces
before the 180-day retrospective scan accrues the n>=20 it needs. The recent
pass re-runs the same engine over the trailing window with a lower paired-day
floor, a tighter Benjamini-Hochberg target, and a no-faint effect bar, then
drops any pair the retrospective scan already established so a pattern is never
reported as both established and emerging.
Extend the curated behaviour x outcome matrix with three clinically meaningful
links: medication adherence -> next-day blood pressure (systolic and diastolic,
the latter previously never a candidate channel), sleep as a behaviour
(poor sleep -> next-day mood / HRV), and a separate point-vs-window labs ->
outcome pass (each draw against the contemporaneous outcome mean) since sparse
lab draws cannot meet a daily-lag floor. Every pass keeps FDR control, the
effect-size floor, and shrinkage; thin data degrades to absent, never a
fabricated link. All associations stay descriptive, never causal.
Clicking a type on the measurements list now drills into that metric's
insights sub-page when one exists, falling back to the existing
?type= filtered list when it does not. A reverse type-to-slug map backs
the navigation, preferring the dedicated single-metric page so an
ambiguous type resolves correctly (PULSE to pulse, not blood-pressure;
WEIGHT to weight, not bmi). No API change.
The comprehensive insight that feeds both the daily briefing and the
insights trend narrative is a single reasoning-heavy generation over the
full feature snapshot. On a large account it runs past the provider
client's fixed 60 s timeout and is aborted mid-stream, so the chain
reports an all-failed outcome, no cached block is written, and the
surface falls back to its static placeholder on every visit. The warm
paths bounded the same generation tighter still (45 s) and counted the
failure with no observable signal, so the blank state was permanent and
silent.
Make the per-request upstream timeout configurable
(CompletionParams.timeoutMs, default 60 s for every existing caller) and
raise it to 120 s for the comprehensive surface only, across all three
generation paths: the on-demand regenerate, the on-demand warm, and the
nightly pre-generation. Raise the warm-pass budget to 130 s so the
provider's own timeout governs a genuine completion rather than clipping
it early. Annotate the comprehensive failure in both the generator and
the nightly loop so a generation that keeps failing is queryable instead
of leaving the surface empty without a trace.
The A1–A3 source files carried v1.22.0 docstrings from before the work was
re-cut as the v1.21.2 point release. Comment-only sweep so blame and the
in-file version tags read consistently with the shipped version.
The builder is server-only by construction — only the metric-page route
imports it and it reads Prisma — and no other module in the tree depends on
the `server-only` package, so the import was both redundant and an undeclared
dependency. Removing it keeps the dead-code gate clean without weakening the
boundary.
Add a deterministic narrative layer the Coach narrates rather than invents:
- Briefing callbacks + forward-looks: the daily briefing recalls the prior
period and points ahead, anchored to a figure, one callback per surface.
- Tension Verdict: when the readiness composite's contributors disagree
(good sleep but a rising resting pulse), surface a brief honest internal
read on the health-score card. A real clinical red-flag is never reconciled
away into a calm verdict.
- Streaks + return-to-baseline: a pure detector over a DAY-bucket series
closes a worry once a metric returns inside the personal MAD band after a
genuine out-of-band run; a gap day breaks a streak.
- Coach prompt: add develop-discrepancy, roll-with-resistance, and an
anti-persuasion clause; ratchet PROMPT_VERSION.
The tension flag and streak event are computed server-side; the card lines
render resolved fields and the model narrates them.
Render a compact two-line "Coach read" above each metric sub-page chart:
an own-baseline line ("Your usual range is X–Y; today's Z sits within /
above / below") and one top lagged-association line from discovered
correlations, framed descriptively. It makes a metric read seen-in-context
rather than isolated.
The strip is server-authoritative: a new GET /api/insights/coach-read
resolves the band via the median ± k·MAD baseline engine (with its 7-day
history floor — below it the strip says "still learning your range" rather
than inventing a range) and the strongest on-metric driver from the
existing FDR-controlled, effect-size-floored correlation discovery (no
lowered floor; the line is omitted when nothing clears it). The client
component only renders the resolved DTO, so web and iOS read identical
numbers. DTO shapes and the pure selection logic live in a client-safe
sibling shared by the component and the unit test.
Wired through the HealthKit metric scaffold (every generic metric page) and
the bespoke weight, blood-pressure, and pulse pages.
The correlation discovery matrix gained two non-MeasurementType channels
(medication compliance, symptom severity), but the Coach get_correlations
reader still filtered its measurement query by hand and never built the new
channels. The channel keys leaked into the Postgres enum cast and threw for any
user with medication or illness data.
Extract the route's two channel-series fetchers into a shared module
(correlation-channel-series.ts) so both consumers build the channels identically,
then wire the reader to derive its type filter from discoveryMeasurementTypes and
fold the fetched compliance + symptom series into the discovery input. The reader
now humanises the channel keys to "medication adherence" / "symptom severity".
Render daily medication-compliance rate and illness symptom-severity as
first-class series in the FDR discovery engine, so the adherence-dip →
symptom-flare link (and compliance → vital drift) can finally surface.
Compliance pools every active medication's dose-history ledger into a per-day
taken/(taken+missed) rate, re-keyed to the user's display timezone. Symptom
severity reads the illness day-log functional impact, zero-filling healthy days
only across real episode spans so a user with no episodes yields an empty
series rather than a constant. Both flow through the existing n >= 20, BH-FDR,
effect-size, and shrinkage gates unchanged; a sparse series degrades to absent.
Each channel forms its own metric family (no self-lag tautology) and keeps the
association-not-causation framing. Add discoveryMeasurementTypes() so every
caller drops the non-measurement channels before a Prisma type filter.
The rollup read path windows the 7/30/90-day regression on
startOfUtcDay(now - N days), while the live-SQL fallback filtered the
REGR_* aggregates on the wall-clock NOW() - INTERVAL bound. An account
crossing a coverage boundary (warm to cold) therefore saw the boundary
day's readings flip in or out of the slope window depending only on
whether the rollup tier was populated, returning a cache-dependent
slope for the same request.
Anchor both cold-fallback paths on date_trunc('day', NOW() AT TIME ZONE
'UTC') - INTERVAL 'N days', the SQL twin of startOfUtcDay, so warm and
cold return identical boundary-day membership. The avg7/avg30/median
windows stay wall-clock since the warm narrow query computes them the
same way.
Significance alone surfaced trivial drivers: a large-n pair with r=0.16
narrated as a confident "tends to go with" link while explaining ~2.5%
of variance. Add three gates before a pair is ranked as a driver:
- exclude same-metric-family lagged pairs (mood->mood, BP component
self-lag) as serial-autocorrelation tautologies, widening the existing
exact self-pair skip to the whole family.
- shrink each Pearson estimate toward null by n/(n+10) so a thin-data
correlation cannot out-rank a deep one on an inflated point estimate.
- floor the shrunk effect at 0.2 (drop below) and bind 0.2-0.3 to a
hedged "faint hint" phrasing tier, 0.3+ to confident phrasing scaled
by sample depth. The reported r and p stay the honest raw statistics.
Rank by shrunk effect magnitude, q as tie-break.
The coincident-deviation read grouped the latest day with a UTC date
slice while the sibling readiness read already used the tz-aware day key.
For a user east or west of UTC, a late-evening or early-morning reading
landed on the wrong calendar day, so a fired flag could compare a vital
from the wrong day against its band and narrate '2 or more vitals out of
band today' on the wrong day.
Thread the user's timezone into the latest-day read and mint the day key
with userDayKey, matching readiness. The Coach derived-snapshot and the
correlations reader pass the real account timezone through. Adds a
regression covering a UTC+2 user whose late-night reading rolls to the
next local day.
The hypertensive-crisis floors were defined in three modules with a
divergent diastolic value: the notification engine and the Coach acute
clause used 180/120, but the dashboard hero used 180/110. A reading like
170/112 lit the red critical-BP banner on the hero yet never tripped the
notification alarm or matched the Coach's stated acute number — one
surface said crisis, the other two stayed calm on the same row.
Promote the absolute floors into a dependency-free clinical-floors leaf
and have the hero, the safety-floor engine, the status registry's fever
band, and the illness escalation all import from it. The crisis diastolic
floor is the guideline-correct 120 (ACC/AHA hypertensive urgency); the
wider 110 hero net is dropped so the surfaces tell one story. A coverage
test pins that every consumer resolves the same constants.
Make the launch context's metric scope live instead of discarding it.
A conversation opened from a metric surface now narrows its snapshot to
that metric with a data-aware seed question, and the global FAB inherits
the page's ambient scope so drilling into a metric and tapping it no
longer opens a blank chat. The scope threads through the drawer into the
first turn of a fresh conversation.
The retrospective narrative was the one model surface that omitted the
shared tone contract, so it read colder than the daily briefing beside it.
Compose the tone fragment into both prompts and warm the hand-written tone
line to name a genuine win when the period earns it, while keeping the
descriptive-never-causal and no-alarm guards intact. The cross-surface
coverage test now asserts the contract reaches this surface too.
The per-score deterministic text named the score, its standing, and the
weakest contributor, then stopped at the diagnosis. When the band is not
green and the weakest driver is behaviourally addressable (sleep, mood,
consistency, timing), append a single grounded pointer drawn from that same
contributor. Physiology-only drivers add nothing, so the text affirms and
watches rather than manufacturing a step.
The no-key and timeout fallback text carried zero reference to the user's
own readings and, on a provider timeout, rendered as hasProvider:true —
indistinguishable from a fresh assessment. Compose each fallback from the
per-metric signal the card already builds: name the current value, place it
against the user's own baseline, and close with one plain-language pointer,
in the same warm voice as the model surfaces. Degrade to the prior generic
tip only when a metric has no usable history. The timeout envelope now
reports hasProvider:false so the UI surfaces it as the computed summary it
is rather than mislabelling it as provider prose.
The shared contracts cover chronic deferral (dose, diagnosis, drug-level
to a clinician) but had no acute branch. Add a closed-list crisis clause —
chest pain, syncope, sudden severe symptoms, hypertensive-crisis readings,
suicidal ideation — that points to prompt or emergency care without
diagnosing, and keep every other turn calm and non-alarmist. It rides the
single shared-contract source so it lands on the Coach, the per-metric
status cards, the comprehensive briefing, and the period narrative; the
coverage test asserts it on all four surfaces.
The floor reads Date.now() inside buildComparisonSnapshotForUser, so it
lands in [before - 400d, after - 400d] relative to the test's brackets.
The prior upper bound used before - 400d, which fails when a millisecond
ticks between the bracket read and the snapshot's clock read.
buildComparisonSnapshotForUser read a user's entire measurement history
per type with no date floor, then fed each series to summarize(). The
snapshot only consumes avg30 / avg30LastMonth / avg30LastYear, and the
widest of those reaches into the [365, 395)-day window (ageMs < 395 days
in meanOfWindow), so nothing older than 395 days can change a result.
Add a 400-day measuredAt.gte lower bound to every per-type read in both
copies of the builder — the page-blocking POST /api/insights/generate
handler and the shared comprehensive-generate path the nightly
pregenerate cron drives. The fan-out runs all seven snapshot types via
Promise.all, so on multi-year accounts the unbounded scan was tens of
thousands of rows per type. 400 days keeps a 5-day floor over the
strict-less-than 395-day reach.
Send the OpenAI JSON response format only when JSON is requested and no
tools are in play, so the Coach prose path (and the tool loop's final
round) is no longer corrupted into a JSON blob. Resolve the Anthropic
finish reason to tool_calls whenever tool_use blocks are present. Memoize
the per-request feature extraction and prefs read and project the feature
query, so the coach tool fan-out shares one read instead of six.
The F1 coach tools each rebuild a snapshot with a distinct single-source
scope, so the 60s snapshot LRU (keyed on the source list) never shares their
reads. On a multi-metric turn the model can fan out up to six tools that each
fire `extractFeatures` (a user lookup plus a windowed measurement read plus the
all-time extremes) and a prefs `findUnique` concurrently — up to twelve heavy
reads against the shared Prisma pool at once.
Memoise both reads on the request-scoped event cache, keyed on userId (and
windowDays for the features read), so the fan-out collapses to one of each
within a request while staying correct across requests. Project the feature
extractor's measurement read to the six columns its consumers actually use
(type, value, measuredAt, sleepStage, source, deviceType) so the 200k-row-class
PULSE / glucose windows stop pulling every column. Outputs are unchanged.
The windowed slope / r2 / sd readers compose from the per-bucket
regression accumulators (migration 0190). Between a deploy and the
boot-backfill completing, a tenant's pre-0190 DAY buckets still carry
NULL accumulators, so composeWindowedRegression returns a full miss and
the slim / comprehensive readers emit a null slope. The null is the
honest answer until the backfill refills the accumulators, but the miss
was passing as a plain rollup read with no signal, against the no-silent-
cap rule.
Add hasPendingAccumulatorBackfill to the read tier and key a new
regression_source annotation off it (unavailable_pending_backfill vs
rollup) on both warm paths. No live-SQL fallback is added; the design
intentionally avoids the heavy scan.
Store additive per-bucket regression accumulators (sum_x, sum_xy, sum_xx,
sum_yy) so windowed slope, r2 and standard deviation compose from the
rollup tier instead of a live raw-table scan on every warm request
(migration 0190, backfilled by the boot queue). Median and the exact
anomaly count stay on a minimal live query. Parity with the live REGR_*
path within float tolerance, well below the read rounding.
Replace the CompletionParams { systemPrompt, userPrompt } string pair with a
structured { system, messages[] } request across all five AI clients. One
refactor unlocks three things: provider prompt-caching (Anthropic cache_control
on the stable system prefix, OpenAI/Codex automatic prefix cache, with
cachedInputTokens surfaced for observability), the tool-calling wire surface,
and clean multi-turn.
Each client maps the new shape to its own wire and degrades unsupported
features silently: the local OpenAI-compatible server never receives a tools
field (most reject unknown keys), and prompt-caching is best-effort everywhere.
CompletionResult gains toolCalls, finishReason and cachedInputTokens; the tool
types and per-provider mapping ship now, the tool-use loop does not.
Vision inputs fold into the user turn's content parts via singleUserTurn, with
each provider's pre-existing block ordering preserved. The corrective-retry
paths append to the last user message rather than a flat string. The Coach
keeps assembling one user turn so its snapshot-once and grounding behaviour is
unchanged. No wire schema visible to API consumers changed.
Behaviour is unchanged across every surface; the full suite stays green.
The summaries slice (computeFromRollups) and the comprehensive aggregator
(buildFromRollups) each ran a narrow live $queryRaw over the raw
measurements partition on EVERY warm-rollup request to fold slope7/30/90
and r2_7/30/90 via REGR_SLOPE / REGR_R2 - the un-eliminated live scan on
the happy path.
Drop the six REGR_* columns from both narrow queries and compose the
windowed slope / r2 (and the population sd via composeRegression) from
the per-bucket accumulators stored in measurement_rollups. The 90-day
DAY buckets are loaded once (already loaded in comprehensive; one bounded
findMany added in the slim slice), source-collapsed to the canonical
source, then summed over the day-aligned 7/30/90-day sub-windows. A
window whose buckets lack accumulators (pre-backfill) composes to null,
the same shape the empty REGR_* FILTER returned, so a transitional read
never returns a wrong partial regression.
median (PERCENTILE_CONT) and the exact |z|>2 anomaly count stay on the
minimal live query - both are non-composable across buckets. The cold
coverage-miss path keeps the live REGR_* regression. Annotate
slope_source:"rollup" + median_source/anomaly_source:"live" so a
regression to the heavy path stays observable (no-silent-cap rule).
The comment claimed the average-per-night, sleep-debt, and chronotype
cards share one window and cannot disagree. They each read their own
sub-window — the average over the full scorable span, sleep-debt over
its trailing 14 nights, chronotype over CHRONOTYPE_WINDOW_NIGHTS — so
the figures are each internally honest rather than identical, and every
card's caption discloses its own nightsCounted. State that plainly; no
computation changes.
Compute the mean asleep minutes per night from the same canonical
per-night data the sleep-debt and chronotype cards read, exposed as a
new averagePerNight field on the sleep-rhythm DTO. Surface it as a third
peer card with identical chrome, so the three tiles sit side by side.
Calm partial state under a four-night floor; localised in six locales.
The cumulative model summed every nightly deficit over a 14-night window
with no credit for catch-up sleep, so a sleeper a modest amount short on
most nights carried hours of standing debt that never recovered while the
pattern held. Replace it with a running balance over a 5-night window:
short nights add their capped shortfall, nights above need pay the balance
down at a sub-linear recovery rate, floored at zero. The headline now
reflects what is still owed tonight and shrinks as the sleeper catches up.
Oura's daily_resilience collection reports a categorical level — limited,
adequate, solid, strong, exceptional. Encode it ordinally (limited=1 …
exceptional=5) into the numeric measurement value so it fits the existing
model with no new categorical column: add the RESILIENCE measurement type
(migration 0186), fetch and map daily_resilience in the Oura sync, and skip
an unknown or missing level rather than coercing it to zero.
RESILIENCE_LEVELS in the Oura client is the single source of truth for the
encoding. The day-keyed externalId keeps a re-sync idempotent so a re-scored
day overwrites in place. Extend the measurement-type label, unit, range,
category, chart-token, and PR-direction maps; add the metric label across all
six locales.
The comprehensive briefing's bounded feature read (extractFeatures) was
paid more than once where it should not be: the on-demand route and the
shared generator each run a downgrade ladder that re-reads with identical
arguments, and a nightly warm has no shared object across consumers in a
single tick.
Add a request/run-scoped feature cache: withFeatureCacheScope opens an
AsyncLocalStorage store, getCachedFeatures memoises the first compute per
(userId, day, includeRaw, sinceDays), gated on a cheap salient-input
fingerprint so a mid-scope ingest recomputes and a probe failure fails
safe. Outside a scope it is a pure passthrough. Wired into the
comprehensive generator, the on-demand POST handler, and the nightly
per-candidate loop + forceWarmUser. Coach keeps its own snapshot.
The slow-moving status input fingerprint covered the card's own metric
rows plus mood and height, but not the FDR discovery channels that also
feed the prompt via getRelevantCorrelationsForMetric. A correlation
newly surfacing off a behaviour channel (e.g. daylight -> next-day
weight) left the card's own rows untouched, so the input gate re-stamped
the stale assessment and the discovery never reached the prose.
computeStatusInputFingerprint gains includeCorrelationChannels: it widens
the same grouped probe by the discovery behaviour + outcome channels
(deduped, mood excluded as it is mood-entry backed), so a channel change
flips the hash. Wired into weight-status, the one caller that folds a
relations block.
Weight and BMI move on a weekly cadence, but the per-metric generators
rebuilt the full snapshot (bounded findMany + per-series rollup reads +
correlation math) every day and relied on the post-build content-hash gate
to skip only the model call. Add an input gate that runs first: a single
grouped probe over the salient inputs (per-type count + newest reading, plus
mood for weight and the profile height for BMI) fingerprints what the
snapshot would read. On a non-forced run whose fingerprint matches the cached
row, the cached assessment is re-stamped under today's key and the whole
build is skipped. A miss falls through to the normal build and the existing
post-build gate.
Consent is checked before any re-date so a revoked-consent user never has
stale text presented as current. Forced runs never gate but still record a
fresh fingerprint for the next day.
The warm narrow query and the cold heavy query each referenced the
canonical-source subquery twice — once in the window_stats CTE and once in
the outer body — so the DISTINCT-ON pick over the live 90-day rows ran twice
per rebuild. Extract canonicalMeasurementsCte (the SELECT body without the
alias wrapper), bind it once as WITH cm AS (...), and select FROM cm m from
both window_stats and the outer aggregate. Output is identical; the dominant
self-join runs once.
canonicalMeasurementsFrom now wraps the same body, byte-stable for the
single-reference call sites (pinned by a new parity test).
Bound the comprehensive briefing's feature read to a 400-day window instead of
walking the full measurement history on every generation. All-time min/max/avg
are sourced from one grouped aggregation over the whole history, so the
allTime* fields stay honest while the unbounded row materialisation is gone;
the oversize downgrade ladder remains as the safety net.
Fold illness-episode and cycle state into the briefing prompt, reusing the
server-authoritative builders the Coach already assembles. Both are
module-gated and cost nothing for users without them, so a mid-illness or
mid-cycle user finally gets context-aware prose.
The readiness same-day grouping and the mood-streak anchor/walk both
sliced the UTC instant for their day key, mis-bucketing a late-evening
reading for a logger east or west of UTC. Thread an optional tz (default
Europe/Berlin) through both paths and key the buckets via userDayKey so
the day boundary follows the user's wall clock. Centralise the inline
day-in-ms literal in a shared time-constants module and use it here.
Ground daily-briefing numbers against the signal features, guard Coach replies
against fabricated doses and risk scores, gate the period narrative, feed the six
status cards a precomputed signal block, add SSE cancellation, and cap the Coach
snapshot read.
BP, pulse, weight, BMI, mood and medication-compliance still asked the
model to derive the recent-vs-baseline comparison from raw buckets — the
exact arithmetic small models do unreliably, and the pattern the
base-system prompt already advertises as precomputed.
Build the SIGNAL block (current / baseline / signed delta / normal-swing
verdict) with buildMetricSignal from each card's graded series and inject
it into the snapshot; BP carries two signals, compliance one per
medication. Each card's prompt section now leads from the signal block
and is told to state, not recompute. The server-computed compliance %
is unchanged.
The period narrative was written straight to storage with no
post-generation check and no corrective retry, unlike Comprehensive
Insights. A populated context could still yield causal phrasing or a
number absent from the context block.
Add a validator that flags causal language (EN/DE) and any number not in
the structured context; on a trip, retry once with a corrective suffix,
then write nothing (the last good row stays) rather than persist an
ungrounded or causal story.
The daily-briefing block (paragraph / signalsOfDay / keyFindings) was
schema-shaped but never number-checked the way recommendations[] are: a
restated figure could drift from the snapshot with no server rejection.
Add a grounding cross-check that asserts every number the briefing prose
states traces to a features.signalsOfDay figure (latest / avg / delta)
within a rounding tolerance. Wire it into both generation sites with one
corrective retry, then strip the briefing rather than persist a
fabricated number. Also gate the higher-temperature paragraph reroll on
the same check so a phrasing reroll cannot drift a figure.
The raw BP pulls in the comprehensive aggregator and the bp-in-target live
fallback read every source's row for BLOOD_PRESSURE_SYS/DIA. BP is a
multi-source metric — a Withings reading mirrored into Apple Health is two
rows for one event — so each shared reading was counted twice in the
in-target denominator and twice in the recency-weighted grade. The rollup
branch of the same fast-path reads source-collapsed measurement_rollups, so a
rollup-warm account and a rollup-cold account returned different BP
percentages and grades for identical data — a web/iOS and cold/warm parity
break.
Route both raw pulls through canonicalMeasurementsFrom so the cold/live path
picks the ladder-winning source per (type, day), matching the warm/rollup
path. Per-event rows of the winning source survive the collapse, so the
downstream 5-minute pairing still sees individual readings.
Marc-André Bombeck