feat(integrations): migrate google-health from Fit REST to Google Health API v4

Google Fit REST API was closed to new sign-ups on 2024-05-01 and shuts down
end of 2026, surfacing as "Access blocked: this app's request is invalid"
when starting the OAuth flow.

- Swap the 10 fitness.* OAuth scopes for the 3 googlehealth.*.readonly
  scopes (activity_and_fitness, health_metrics_and_measurements, sleep).
- Replace fitness/v1 dataset:aggregate + sessions calls with
  health.googleapis.com/v4/users/me/dataTypes/{steps,total-calories,
  heart-rate,sleep}/dataPoints, filtered to today's window.
- Read the v4 DataPoint union defensively (the per-type schema is sparsely
  documented) and log the first raw sample at debug so we can refine field
  paths after the first real OAuth.
- Output Signal contract is unchanged — agents and downstream consumers
  see the same steps/activity/heart_rate/sleep signals.

Cloud Console still needs: enable Google Health API, add the 3 scopes to
the consent screen, add test user (all googlehealth scopes are Restricted).

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

CLAUDE.md

@@ -71,6 +71,8 @@ docs/              architecture notes, ADRs, API specs
   - **Never run two `docker compose up --build` at once** — both grab the same `--mount=type=cache,id=pnpm` and deadlock on the API's `pnpm --prod deploy` step. Symptom: build sits silent for hours on `[api builder 8/8]`. Before starting any build, check `ps aux | grep "docker compose"` and kill any prior `up --build` (`kill -9 <pid>` — the wrapper bash and the docker compose binary are separate PIDs; kill the docker compose one).
   - **Don't add `--offline` to `pnpm --prod deploy`** — pnpm's metadata cache (`/root/.cache/pnpm/`) is not in the `/pnpm/store` cache mount, so `--offline` fails with `ERR_PNPM_NO_OFFLINE_META` for transitive devDeps (e.g. vite via vitest). Leave the deploy step network-on; it works.
   - **All TS Dockerfiles need `python3 make g++`** in the base stage — `better-sqlite3` rebuilds natively on install. Missing from `Dockerfile.admin` historically caused `gyp ERR! find Python` failures.
+  - **`Dockerfile.ml` needs `build-essential`** (not just `gcc`) — `pyswisseph` (stars agent) compiles C from source and fails with `fatal error: math.h: No such file or directory` if only `gcc` is installed; it needs `libc-dev` too, easiest via `build-essential`.
+  - **`Dockerfile.web` builder stage needs root `package.json` + `pnpm-workspace.yaml` + `pnpm-lock.yaml`** copied in. Without them, `pnpm --filter @oo/shared-types build` fails with `[ERR_PNPM_NO_PKG_MANIFEST] No package.json found in /app`. The deps stage has them but the builder is a fresh layer; selective copies must include them.
   - **A clean build of `--profile core` takes ~3 min total** when the buildx cache is warm. If it's been silent for >10 min, check for the parallel-build deadlock above before assuming "still going".
 - Run Python agent tests: `python3 -m pytest ml/agents/tests/ -x -q` (tests add repo root to `sys.path` themselves).
 - Run Python feature tests: `python3 -m pytest ml/features/ -x -q`

README.md

@@ -121,173 +121,31 @@ All model calls route through **LiteLLM** at `llm.alogins.net` (or `LITELLM_URL`
 
 ## Roadmap
 
+Issues and open work are tracked in [Gitea milestones](http://localhost:3000/alvis/oO/milestones). Pick an issue, check its milestone (= phase), read the service's `README.md`, ship.
+
 ### Phase 0 — Walking skeleton  *(M0)* ✓ shipped
-Goal: a single user signs in with Google, connects Todoist, and sees one random Todoist task on a black page. Deletion works.
+Single user signs in with Google, connects Todoist, sees one random task on a black page. Deletion works. Auth, integrations, recommender stub, PWA, feedback loop, ToS/privacy, metrics baseline.
-- [x] Monorepo scaffold, docker-compose dev env
-- [x] `auth` — Google OAuth2/PKCE via openid-client v6; session cookie; Next.js middleware guard
-- [x] `integrations/todoist` — OAuth2 flow, token stored in DB, disconnect supported
-- [x] `recommender` with `RandomPolicy`; stable `POST /recommend` contract; 30s task cache
-- [x] `apps/web` — sign-in, connect, tip pages; PWA manifest + icons
-- [x] Feedback: `done / snooze / dismiss`; reward inferred from dwell-time (`inferReward`); marks task complete in Todoist
-- [x] Deploy modular monolith to Agap VM via Caddy at `o.alogins.net`
-- [x] ToS + Privacy Policy pages (`/legal/terms`, `/legal/privacy`); implicit consent on sign-in
-- [x] Account deletion: revokes tokens, purges data, soft-deletes profile; button on /connect
-- [x] Metrics baseline: `tip_views` table (tip served) + `tip_feedback` (reactions) — activation + reaction rate queryable
 
 ### Phase 1 — Real signal + in-the-moment delivery  *(M1)* ✓ shipped
-Goal: tips are picked, not drawn from a hat — and they arrive at the right moment on the web.
+Tips are picked, not drawn from a hat. Event bus, Todoist sync, task features, ε-greedy policy (v1 + v2), web push, NATS JetStream bridge, shadow-policy registry, offline sim framework, per-user profile features, admin + ML ops console (`apps/admin`).
-- [x] Event bus scaffold: typed in-process EventEmitter with 500-event ring buffer; subjects match future NATS JetStream — swap is mechanical
-- [x] Todoist sync emits `signals.task.synced`; tip served/feedback emit `signals.tip.*`
-- [x] Features extracted per task: `is_overdue`, `task_age_days`, `priority`; context: `hour_of_day`, `day_of_week`
-- [x] **ε-greedy v1** (d=7, ε=0.10, day-of-week sin/cos features); per-user state persisted to disk
-- [x] **ε-greedy v2** (d=12, profile features: completion rate, dismiss rate, dwell, preferred hour, tip volume) in shadow; promoted to active policy (ADR-0012)
-- [x] `RemotePolicy` in recommender: calls ml/serving, falls back to RandomPolicy on timeout/error; logs explainability to `tip_scores`
-- [x] Feedback loop: dwell-time inferred reward (`inferReward`) → online model update; `done` in 15 s–2 min = +1.0 (magic zone)
-- [x] Offline simulation framework (`ml/experiments/sim`): rule/LLM/claude-code judges, two-policy comparison, results persisted to `sim_runs` + `sim_events`
-- [x] **Web Push** (VAPID): SW, subscribe/unsubscribe API, "notify me" button on tip page
-- [x] Shadow-policy registry: run N shadow policies per request, log picks without serving them (#56)
-- [x] NATS JetStream bridge — durable `signals.>` and `feedback.>` streams; in-process bus stays the source of truth, every publish bridges out (#21, shipped)
-- [x] Per-user profile features (completion rate, dismiss rate, dwell, preferred hour, tip volume) — event-driven, JIT invalidation (#81)
-- [ ] Quiet-hours + dedupe for push delivery
-- [ ] Delayed rewards: tasks completed directly in Todoist (requires webhook from Todoist)
-- [ ] Apple OAuth (deferred to M3)
-
-#### M1 add-on — Admin & ML Ops Console  *(fully shipped)*
-
-oO is ML-heavy. Without a cockpit, every model change ships blind. This console is the team's single pane for users, signals, features, models, experiments, and tip outcomes — with the ability to *act* on them (revoke a token, replay an event, promote a model, reset a bandit).
-
-**Framework pick — `apps/admin` on Next.js 15 + Tremor + shadcn/ui.**  Analytics-first UI for an analytics-first product, stays on our existing TS/React/Tailwind stack, reuses `packages/shared-types`, `sdk-js`, and the Auth.js session. Specialized ML tooling (MLflow) runs as a **separate external service** linked from the admin shell; Grafana panels are embedded.
-
-| Layer | Tool | Why |
-|-------|------|-----|
-| App shell | **Next.js 15** (new `apps/admin`) | Same stack as `apps/web`; reuses auth, types, SDK |
-| Dashboards / charts | **[Tremor](https://tremor.so)** | Analytics-first React + Tailwind — KPI cards, time-series, categorical, heatmaps |
-| CRUD primitives | **[shadcn/ui](https://ui.shadcn.com)** | Copy-paste Radix components; forms, dialogs, command palette |
-| Heavy grids | **[TanStack Table v8](https://tanstack.com/table)** | Sortable / paginated / virtualized tables (events, users, tips) |
-| Extra charts | **[Recharts](https://recharts.org)** / **[visx](https://airbnb.io/visx)** | Fallbacks where Tremor falls short (e.g. force graphs, Sankey) |
-| Model registry / experiments | **[MLflow](https://mlflow.org)** *(external — `o.alogins.net/mlflow`)* | Experiment tracking, artifact browser, model registry; own basic-auth |
-| Infra metrics | **[Grafana](https://grafana.com)** *(embedded panels)* | One ops source of truth |
-| Ad-hoc analysis | **[Marimo](https://marimo.io)** reactive notebooks | Python-native for the ML side; launch-out link |
-| AuthZ | `profile.role='admin'` + Next.js middleware | Reuses existing session; no new auth surface |
-
-**Rejected alternatives (so we don't re-litigate):**
-- *Retool / AppSmith* — low-code speed, but admin logic leaves our repo; weak analytics affordances for an analytics product
-- *Streamlit / Gradio / Dash* — Python-first; thin RBAC and routing; splits our frontend stack in two
-- *React-admin / Refine.dev* — strong CRUD scaffolding, but analytics/ML views feel bolted on; we'd rebuild Tremor-style dashboards ourselves
-- *Superset / Metabase as the admin surface* — excellent for BI, poor for operational **writes** (revoke, replay, promote). Plan: **adopt Superset in M4** for BI alongside batch pipelines; ship a read-only SQL widget inside admin for now
-
-**Build sequence:**
-1. [x] **ADR-0006** — record the framework choice + "embed, don't rebuild" rule for MLflow/Grafana
-2. [x] **Scaffold** — `apps/admin` with Next.js 15, Tailwind, Tremor; deploy behind Caddy at `admin.o.alogins.net`
-3. [x] **RBAC** — `role` column on `users`; admin-only Next.js middleware; seed first admin via `ADMIN_SEED_EMAIL` env; `admin_actions` audit-log table
-4. [x] **Overview dashboard** — DAU/WAU KPI cards, tips served, reaction breakdown, activation funnel
-5. [x] **User explorer** — list + detail page: identity, consents, integrations, last tip, reward history; revoke-integration + reset-bandit + rebuild-profile actions
-6. [x] **Event stream viewer** — live tail of `signals.*` with filters by subject/user/time; same UI when the bus swaps to NATS
-7. [x] **Features page** — features sent to `ml/serving` per scoring call; per-user profile features with freshness; diff across time
-8. [x] **Tips page** — tips served, scored, feedback reactions with policy/model breakdown
-9. [x] **Reward analytics** — reaction distribution over time; per-policy / per-model / per-prompt-version compare; slice by `hour_of_day`, `priority`, cohort
-10. [x] **Data quality widget** — missing-feature rate, stale-token rate, daily completeness heatmap; per-feature freshness SLA status
-11. [x] **Ops actions** — revoke token (Users page), rebuild profile, reset bandit, enable/disable shadow policies; every action audit-logged
-12. [x] **Health rollup** — `/admin/health` surfaces api, ml/serving, SQLite, event-bus, MLflow; auto-refreshes every 15s
-13. [x] **Read-only SQL runner** — SELECT-only runner against SQLite + saved queries (sunsets to Superset in M4)
-14. [x] **Offline simulation runner** — launch `ml/experiments/sim` from admin UI; track sim runs, judge, policy comparison
-15. [x] **Token-based admin auth** — `POST /api/auth/token` for Playwright/CI; `ADMIN_TOKEN` env var (#105)
-16. [x] **Docs pages** — admin documentation and runbooks inline
 
 ### Phase 2 — AI tips + multi-source signals  *(M2)* ✓ shipped
-Goal: tips are AI-generated from user context, not just raw Todoist tasks. Multiple signal sources feed a generalized pipeline. Research-intensive milestone.
+Tips are AI-generated from user context. Multi-agent pipeline (ADR-0013): five pre-compute agents (`overdue-task`, `momentum`, `time-of-day`, `recent-patterns`, `focus-area`) emit prompt snippets; orchestrator LLM produces one tip. Unified Profile + agent registry + auto-inference framework (ADR-0014). LLM output validation + fallback. LiteLLM gateway, model benchmarking, prompt research, MLflow tracing.
-
-**Architectural shift (mid-M2):** the bandit-ranks-LLM-candidates design from earlier in M2 was replaced with a multi-agent pipeline (ADR-0013): pre-compute agents emit prompt snippets, an orchestrator LLM produces the tip directly. ADR-0014 layers a unified Profile + agent registry + auto-inference framework on top so the system generalizes cleanly to N agents.
-
-**Multi-agent recommendation (ADR-0013, shipped):**
-- [x] `agent_outputs` table + per-agent TTL caching
-- [x] Five initial agents: `overdue-task`, `momentum`, `time-of-day`, `recent-patterns`, `focus-area`
-- [x] Agent pre-compute scheduler
-- [x] Orchestrator cutover — recommender calls `ml/serving` with snippet list, no bandit scoring
-- [x] Bandit endpoints + shadow policy machinery removed
-
-**Unified Profile + agent registry (ADR-0014, shipped):**
-- [x] Unified Profile model: prefs, contexts, consents + manifest plumbing + orchestrator cutover (#30)
-- [x] Shared context-inference framework (#111)
-- [x] Per-agent auto-inference: `time-of-day` (#112), `focus-area` (#113), `momentum` (#114), `overdue-task` (#115), `recent-patterns` (#116)
-
-**AI infrastructure (unblock everything else):**
-- [ ] `ai` compose profile — Ollama + LiteLLM for local dev; env vars `OLLAMA_URL` / `LITELLM_URL` (#86)
-- [x] AI gateway — wire `ml/serving` to LiteLLM; model aliases `tip-generator` + `embedder` (#87)
-
-**AI tip generation pipeline:**
-- [x] Context assembler — user signals + feature store → structured prompt context (`ml/features/context.py`); skeleton implemented
-- [x] Tip generator endpoint — `POST /generate` in `ml/serving`; LLM → N typed `TipCandidate` objects (#79)
-- [x] `TipCandidate` shared schema — `{content, kind, source, model, prompt_version, confidence}`; update recommender pipeline (#89)
-- [ ] LLM output validation + retry — JSON schema gate, clarification retry (2×), fallback to task-based (#90)
-- [x] Prompt versioning — `prompt_version` + `model` columns in `tip_scores`; content-hash invalidation (#91)
-- [x] LLM tip quality dashboard — reaction breakdown by model / prompt_version in `/admin/reward-analytics` (#92)
-
-**Evaluation & model selection:**
-- [x] Model benchmark — compare qwen2.5:7b / llama3.2:3b / gemma3:4b via offline sim + LLM judge (#93)
-- [x] LLM prompt research — persona design, context injection strategies, few-shot examples (#84, #95)
-
-**Pipeline architecture:**
-- [x] Signal source abstraction — `SignalSource` interface for Todoist + extensible design (#78)
-- [ ] Generalized recommendation pipeline — candidate → rank → render stages (#80)
-- [x] Feature registry + user profile builder — centralized features, persistent profiles, event-driven invalidation (#81)
-- [ ] Tip kind system — task, advice, insight, reminder with kind-aware UI + rewards (#82)
-
-**Policy research:**
-- [ ] Next-gen policies — Thompson sampling, neural bandits, hybrid transfer learning (#83)
-
-**Integrations & infra (carried from M1):**
-- [ ] Apple OAuth (#7)
-- [x] NATS JetStream replacing in-process bus (#21) — adapter ships in `services/api/src/events/nats.ts`; in-proc bus is the producer, JetStream is the durable mirror
-- [x] Todoist sync via events (#22) — background scheduler in `services/api/src/signals/scheduler.ts` emits `signals.task.synced` every `TODOIST_SYNC_INTERVAL_MS`; on-demand fetch remains as freshness fallback
-- [x] Event schema registry + protobuf CI gate (#54) — buf lint/breaking checks on every PR
-- [x] Per-user freshness SLAs for features (#61) — context-feature (JIT) vs profile-feature (batched) spec in ADR-0011; `invalidated_by` mirrored into `ProfileFeature`; CONTEXT_FEATURES in ml/features/context.py
-- [x] Embedding-based task clustering — `nomic-embed-text` for semantic dedup + focus-area features (#97)
-- [x] Observability (#18) — structured logs via pino, W3C trace IDs, Sentry hooks, trace correlation end-to-end
-- [ ] CI skeleton (#3), E2E tests (#20)
-
-**Bugs & UX (fix before new features):**
-- [x] TipFeedback type mismatch (#73)
-- [x] Todoist token refresh (#74) — OAuth token auto-refresh on 401
-- [x] Reward fire-and-forget (#75) — retry logic + logging
-- [x] Data retention purge (#76) — daily purge of 30-day-old tip_scores/tip_feedback
-- [x] Port mismatch (#77) — fixed in docker-compose + env var config
-- [x] UX refinements (#100–102) — "done/snooze/dismiss" feedback only, config page UI, settings gear button
 
 ### Phase 3 — Native mobile  *(M3)*
-- [ ] iOS app (SwiftUI) with APNs push
+iOS (SwiftUI + APNs) and Android (Compose + FCM). `notifier` service gains APNs + FCM channels. Auth migrated from Auth.js to dedicated OIDC provider. Decide-and-deliver scheduler. See [M3 milestone](http://localhost:3000/alvis/oO/milestone/3).
-- [ ] Android app (Compose) with FCM push
-- [ ] `notifier` gains APNs + FCM channels, per-device rate limits
-- [ ] Migrate auth from Auth.js to dedicated OIDC provider (trigger from ADR-0004)
-- [ ] Consolidate MLflow behind shared OIDC (SSO for all internal services)
-- [ ] Decide-and-deliver scheduler: per-user "is this tip worth interrupting now?" threshold
 
 ### Phase 4 — MLOps at scale  *(M4)*
-- [x] MLflow deployed as external service (`mlops` compose profile); own auth; health check integrated
+Retraining pipeline, feature-to-prompt batch jobs, prompt optimization loop, LLM fine-tuning on reaction signals, modular-monolith import-boundary lint, online experiments framework, drift monitoring. See [M4 milestone](http://localhost:3000/alvis/oO/milestone/4).
-- [ ] Write first retraining pipeline + first MLflow experiment logging from `ml/serving` + JetStream consumers (#98)
-- [ ] Feature-to-prompt pipeline — nightly batch job materializes context for LLM; cuts inline latency (#94)
-- [ ] Prompt optimization loop — sim A/B → MLflow experiment → human-approved promotion (#95)
-- [ ] LLM fine-tuning — tip reactions as training signal; LoRA on base model; MLflow tracks runs (#96)
-- [ ] Embedding-based task clustering — `nomic-embed-text` for dedup + user pattern features (#97)
-- [ ] Modular-monolith packaging + import-boundary lint (#47)
-- [ ] Consolidate MLflow auth into shared OIDC provider (tracked as M3 issue #85)
-- [ ] Shadow → A/B → launch pipeline as first-class in MLflow
-- [ ] Online experiments framework: deterministic assignment + bandit policies alongside fixed-split A/B
-- [ ] Cross-user collaborative features (opt-in only); cohort slicing; fairness checks
-- [ ] Drift monitoring (feature + prediction + reward drift); model cards per LLM version
 
 ### Phase 5 — Production hardening  *(M5)*
-- [ ] Audit logging, rotation of provider tokens + internal signing keys
+Audit logging, key rotation, k3s → k8s, multi-region, public integration SDK, billing. See [M5 milestone](http://localhost:3000/alvis/oO/milestone/5).
-- [ ] **k3s** on existing VM, then k8s + HPA once multi-node justified (no cliff)
-- [ ] Multi-region failover, Postgres PITR, event-bus mirroring
-- [ ] Public integration SDK; sandbox tenancy for third-party connectors
-- [ ] Billing + subscription tiers
 
 ---
 
 ## Contributing
 
-This repo is split into independent modules; most tickets belong to exactly one. Pick an issue, check its milestone (= phase), read the service's `README.md`, ship.
+This repo is split into independent modules; most tickets belong to exactly one. Pick an issue from [Gitea](http://localhost:3000/alvis/oO/issues), read the service's `README.md`, ship.
 
 Conventions and per-service guidance live in [`CLAUDE.md`](CLAUDE.md).
 

apps/web/src/app/connect/page.tsx

@@ -51,6 +51,8 @@ function ConnectPageInner() {
   }
 
   const todoistConnected = isConnected('todoist');
+  const googleHealthConnected = isConnected('google-health');
+  const anyConnected = todoistConnected || googleHealthConnected;
 
   return (
     <main style={{ minHeight: '100vh', padding: '4rem 2rem', maxWidth: '480px', margin: '0 auto' }}>
@@ -85,7 +87,6 @@ function ConnectPageInner() {
         marginBottom: '1rem',
       }}>
         <div style={{ display: 'flex', alignItems: 'center', gap: '0.875rem' }}>
-          {/* Todoist logomark */}
           <svg width="28" height="28" viewBox="0 0 24 24" fill="none" aria-label="Todoist">
             <rect width="24" height="24" rx="6" fill="#DB4035"/>
             <path d="M6 8.5L11 13l7-7" stroke="#fff" strokeWidth="2" strokeLinecap="round" strokeLinejoin="round"/>
@@ -130,7 +131,65 @@ function ConnectPageInner() {
         )}
       </div>
 
-      {todoistConnected && (
+      {/* Google Health card */}
+      <div style={{
+        border: '1px solid rgba(255,255,255,0.1)',
+        borderRadius: '0.75rem',
+        padding: '1.25rem 1.5rem',
+        display: 'flex',
+        alignItems: 'center',
+        justifyContent: 'space-between',
+        marginBottom: '1rem',
+      }}>
+        <div style={{ display: 'flex', alignItems: 'center', gap: '0.875rem' }}>
+          <svg width="28" height="28" viewBox="0 0 24 24" fill="none" aria-label="Google Health">
+            <rect width="24" height="24" rx="6" fill="#EA4335"/>
+            <path d="M12 6.5c0-1.1.9-2 2-2s2 .9 2 2-.9 2-2 2-2-.9-2-2z" fill="#fff"/>
+            <path d="M8 10.5c0-1.1.9-2 2-2s2 .9 2 2-.9 2-2 2-2-.9-2-2z" fill="#fff" opacity=".7"/>
+            <path d="M12 14.5c0 2.2-1.8 4-4 4s-4-1.8-4-4 1.8-4 4-4 4 1.8 4 4z" fill="#fff" opacity=".4"/>
+            <path d="M13 13.5c.5-1 1.5-1.7 2.5-1.7 1.7 0 3 1.3 3 3s-1.3 3-3 3c-1 0-1.9-.5-2.5-1.3" stroke="#fff" strokeWidth="1.5" strokeLinecap="round" fill="none"/>
+          </svg>
+          <div>
+            <div style={{ fontWeight: 500, fontSize: '0.9rem' }}>Google Health</div>
+            <div style={{ color: 'var(--gray)', fontSize: '0.75rem', marginTop: '0.1rem' }}>
+              {googleHealthConnected ? 'Connected' : 'Steps, sleep & activity'}
+            </div>
+          </div>
+        </div>
+
+        {googleHealthConnected ? (
+          <button
+            onClick={() => handleDisconnect('google-health')}
+            disabled={disconnecting === 'google-health'}
+            style={{
+              background: 'transparent',
+              border: '1px solid rgba(255,255,255,0.15)',
+              color: 'var(--gray)',
+              borderRadius: '0.375rem',
+              padding: '0.375rem 0.875rem',
+              fontSize: '0.8rem',
+            }}
+          >
+            {disconnecting === 'google-health' ? '…' : 'Disconnect'}
+          </button>
+        ) : (
+          <a
+            href="/api/integrations/google-health/connect?redirectTo=/connect"
+            style={{
+              background: 'var(--white)',
+              color: 'var(--black)',
+              borderRadius: '0.375rem',
+              padding: '0.375rem 0.875rem',
+              fontSize: '0.8rem',
+              fontWeight: 500,
+            }}
+          >
+            Connect
+          </a>
+        )}
+      </div>
+
+      {anyConnected && (
         <div style={{ marginTop: '3rem' }}>
           <a
             href="/tip"

infra/docker/Dockerfile.ml

@@ -1,5 +1,8 @@
 FROM python:3.12-slim
 WORKDIR /app/ml/serving
+RUN apt-get update \
+ && apt-get install -y --no-install-recommends build-essential \
+ && rm -rf /var/lib/apt/lists/*
 COPY ml/serving/requirements.txt .
 RUN pip install --no-cache-dir -r requirements.txt
 COPY ml/ /app/ml/

infra/docker/Dockerfile.web

@@ -13,6 +13,7 @@ WORKDIR /app
 COPY --from=deps /app/node_modules ./node_modules
 COPY --from=deps /app/packages/shared-types/node_modules ./packages/shared-types/node_modules
 COPY --from=deps /app/apps/web/node_modules ./apps/web/node_modules
+COPY package.json pnpm-workspace.yaml pnpm-lock.yaml ./
 COPY tsconfig.base.json ./
 COPY packages/shared-types ./packages/shared-types
 COPY apps/web ./apps/web

infra/docker/docker-compose.yml

@@ -82,6 +82,46 @@ services:
       timeout: 5s
       retries: 5
 
+  # ── ai profile — Ollama + LiteLLM for local dev ──────────────────────────
+  # Start: docker compose --profile ai up
+  # Use when the Agap shared Ollama/LiteLLM services are not available locally.
+  # Set LITELLM_URL=http://localhost:4000 and OLLAMA_URL=http://localhost:11434
+  # in .env.local to point ml-serving at these containers instead of Agap.
+
+  ollama:
+    image: ollama/ollama:latest
+    profiles: [ai]
+    volumes:
+      - ollama-models:/root/.ollama
+    ports:
+      - "127.0.0.1:11434:11434"
+    healthcheck:
+      test: ["CMD", "curl", "-sf", "http://localhost:11434/api/tags"]
+      interval: 15s
+      timeout: 5s
+      retries: 10
+
+  litellm:
+    image: ghcr.io/berriai/litellm:main-latest
+    profiles: [ai]
+    environment:
+      LITELLM_MASTER_KEY: ${LITELLM_MASTER_KEY:-sk-local-dev}
+    command: >
+      --model ollama/qwen2.5:1.5b
+      --model ollama/nomic-embed-text
+      --api_base http://ollama:11434
+      --port 4000
+    ports:
+      - "127.0.0.1:4000:4000"
+    depends_on:
+      ollama:
+        condition: service_healthy
+    healthcheck:
+      test: ["CMD", "curl", "-sf", "http://localhost:4000/health"]
+      interval: 10s
+      timeout: 5s
+      retries: 5
+
   # ── mlops profile — MLflow ────────────────────────────────────────────────
   # Start: docker compose --profile mlops up
   # MLflow UI:  http://localhost:5000  or  https://o.alogins.net/mlflow
@@ -129,3 +169,6 @@ services:
       interval: 10s
       timeout: 5s
       retries: 5
+
+volumes:
+  ollama-models:

ml/agents/registry.py

@@ -17,6 +17,8 @@ from .time_of_day import TimeOfDayAgent, MANIFEST as TIME_OF_DAY_MANIFEST
 from .recent_patterns import RecentPatternsAgent, MANIFEST as RECENT_PATTERNS_MANIFEST
 from .focus_area import FocusAreaAgent, MANIFEST as FOCUS_AREA_MANIFEST
 from .health_vitals import HealthVitalsAgent, MANIFEST as HEALTH_VITALS_MANIFEST
+from .tarot import TarotAgent, MANIFEST as TAROT_MANIFEST
+from .stars import StarsAgent, MANIFEST as STARS_MANIFEST
 
 _REGISTERED: list[tuple[BaseAgent, AgentManifest]] = [
     (OverdueTaskAgent(), OVERDUE_TASK_MANIFEST),
@@ -25,6 +27,8 @@ _REGISTERED: list[tuple[BaseAgent, AgentManifest]] = [
     (RecentPatternsAgent(), RECENT_PATTERNS_MANIFEST),
     (FocusAreaAgent(), FOCUS_AREA_MANIFEST),
     (HealthVitalsAgent(), HEALTH_VITALS_MANIFEST),
+    (TarotAgent(), TAROT_MANIFEST),
+    (StarsAgent(), STARS_MANIFEST),
 ]
 
 # Sanity check — agent_id and manifest.id must agree, otherwise the registry

ml/agents/stars.py

@@ -0,0 +1,233 @@
+"""Stars agent — astrological transit predictions via pyswisseph.
+
+Requires birth_date in agent_prefs (ISO 8601 date string, e.g. '1990-06-15').
+Populated from a connected data source (Google profile / Google Health).
+If birth_date is absent the agent returns a no-data snippet and the
+eligibility filter will silence it once the consent / pref check catches up.
+
+Computes today's Sun, Moon, Mercury, Venus, Mars, Jupiter, Saturn positions
+and finds notable transits (conjunctions, oppositions, squares, trines, sextiles)
+between today's sky and the user's natal chart. Passes a concise prediction
++ interpretation to the orchestrator.
+"""
+from __future__ import annotations
+
+import math
+from datetime import date, datetime, timezone
+from typing import ClassVar
+
+from .base import BaseAgent, AgentInput, AgentOutput
+from .manifest import AgentManifest, InferredParam
+
+try:
+    import swisseph as swe  # type: ignore
+    _SWE_AVAILABLE = True
+except ImportError:  # pragma: no cover — present in container, absent in dev
+    _SWE_AVAILABLE = False
+
+# ---------------------------------------------------------------------------
+# Planet catalogue
+# ---------------------------------------------------------------------------
+_PLANETS: list[tuple[int, str]] = []
+if _SWE_AVAILABLE:
+    _PLANETS = [
+        (swe.SUN,     "Sun"),
+        (swe.MOON,    "Moon"),
+        (swe.MERCURY, "Mercury"),
+        (swe.VENUS,   "Venus"),
+        (swe.MARS,    "Mars"),
+        (swe.JUPITER, "Jupiter"),
+        (swe.SATURN,  "Saturn"),
+    ]
+
+# Aspect definitions: (angle, orb, name, nature)
+_ASPECTS: list[tuple[float, float, str, str]] = [
+    (0.0,   8.0, "conjunction",  "intensifying"),
+    (60.0,  6.0, "sextile",      "harmonious"),
+    (90.0,  7.0, "square",       "challenging"),
+    (120.0, 8.0, "trine",        "flowing"),
+    (180.0, 8.0, "opposition",   "tension"),
+]
+
+_ZODIAC = [
+    "Aries", "Taurus", "Gemini", "Cancer", "Leo", "Virgo",
+    "Libra", "Scorpio", "Sagittarius", "Capricorn", "Aquarius", "Pisces",
+]
+
+# Interpretive keywords per planet for transit readings
+_PLANET_THEMES: dict[str, str] = {
+    "Sun":     "identity, vitality, core purpose",
+    "Moon":    "emotions, intuition, comfort needs",
+    "Mercury": "communication, thinking, decisions",
+    "Venus":   "relationships, values, pleasure",
+    "Mars":    "energy, drive, conflict",
+    "Jupiter": "growth, opportunity, expansion",
+    "Saturn":  "discipline, responsibility, long-term structure",
+}
+
+
+def _zodiac_sign(lon: float) -> str:
+    return _ZODIAC[int(lon / 30) % 12]
+
+
+def _jd_from_date(d: date) -> float:
+    """Julian Day Number for noon UTC on the given date."""
+    assert _SWE_AVAILABLE
+    return swe.julday(d.year, d.month, d.day, 12.0)
+
+
+def _planet_positions(jd: float) -> dict[str, float]:
+    assert _SWE_AVAILABLE
+    positions: dict[str, float] = {}
+    for pid, name in _PLANETS:
+        result, _ = swe.calc_ut(jd, pid)
+        positions[name] = result[0]  # ecliptic longitude
+    return positions
+
+
+def _angular_diff(a: float, b: float) -> float:
+    """Smallest angle between two ecliptic longitudes (0–180)."""
+    diff = abs(a - b) % 360
+    return diff if diff <= 180 else 360 - diff
+
+
+def _find_transits(natal: dict[str, float], today: dict[str, float]) -> list[dict]:
+    """Return list of active transits between today's sky and natal chart."""
+    transits: list[dict] = []
+    for t_name, t_lon in today.items():
+        for n_name, n_lon in natal.items():
+            diff = _angular_diff(t_lon, n_lon)
+            for angle, orb, aspect_name, nature in _ASPECTS:
+                if abs(diff - angle) <= orb:
+                    transits.append({
+                        "transit_planet": t_name,
+                        "natal_planet": n_name,
+                        "aspect": aspect_name,
+                        "nature": nature,
+                        "orb": round(abs(diff - angle), 2),
+                    })
+    # Sort by tightness of orb
+    transits.sort(key=lambda x: x["orb"])
+    return transits
+
+
+def _format_transit(t: dict) -> str:
+    tp, np, asp, nat = t["transit_planet"], t["natal_planet"], t["aspect"], t["nature"]
+    tp_theme = _PLANET_THEMES.get(tp, "")
+    np_theme = _PLANET_THEMES.get(np, "")
+    return (
+        f"Transiting {tp} ({tp_theme}) {asp} natal {np} ({np_theme}) "
+        f"— a {nat} influence"
+    )
+
+
+# ---------------------------------------------------------------------------
+# Manifest
+# ---------------------------------------------------------------------------
+MANIFEST = AgentManifest(
+    id="stars",
+    version="1.0.0",
+    description="Astrological transit predictions based on the user's birth date and today's planetary positions.",
+    pref_schema={
+        "type": "object",
+        "additionalProperties": False,
+        "properties": {
+            "birth_date": {
+                "type": "string",
+                "pattern": r"^\d{4}-\d{2}-\d{2}$",
+                "description": "ISO 8601 birth date (YYYY-MM-DD). Populated from connected data source.",
+            },
+        },
+    },
+    context_schema=["profile.birth_date"],
+    # Requires a connected Google source that supplies birth date.
+    # data:google-health is the current carrier; when Google profile is a
+    # separate consent key, add it here.
+    required_consents=["data:core", "data:google-health"],
+    output_contract={"type": "snippet", "format": "free_text"},
+    ttl_sec=3_600 * 6,  # planetary positions change slowly — 6 h is fine
+    silenced_in_contexts=[],
+    inferred_params=[
+        InferredParam(
+            key="birth_date",
+            ttl_sec=365 * 86_400,   # effectively permanent once known
+            cold_start_default=None,
+            min_history=999_999,    # never inferred from events — sourced externally
+            infer=None,
+        ),
+    ],
+)
+
+
+class StarsAgent(BaseAgent):
+    """Produces astrological transit predictions for the user's birth chart."""
+
+    agent_id: ClassVar[str] = MANIFEST.id
+    ttl_seconds: ClassVar[int] = MANIFEST.ttl_sec
+    version: ClassVar[str] = MANIFEST.version
+
+    def compute(self, inp: AgentInput) -> AgentOutput:
+        birth_date_str: str | None = inp.agent_prefs.get("birth_date")
+
+        if not birth_date_str:
+            prompt = (
+                "Birth date is not available — astrological reading skipped. "
+                "(Always write the tip in English.)"
+            )
+            return self._make_output(inp, prompt, {"no_birth_date": True})
+
+        if not _SWE_AVAILABLE:
+            prompt = (
+                "Astrological library unavailable — reading skipped. "
+                "(Always write the tip in English.)"
+            )
+            return self._make_output(inp, prompt, {"swe_unavailable": True})
+
+        try:
+            birth_date = date.fromisoformat(birth_date_str)
+        except ValueError:
+            prompt = "Birth date format invalid — astrological reading skipped."
+            return self._make_output(inp, prompt, {"invalid_birth_date": birth_date_str})
+
+        today_date = inp.now.date()
+        natal_jd = _jd_from_date(birth_date)
+        today_jd = _jd_from_date(today_date)
+
+        natal_pos = _planet_positions(natal_jd)
+        today_pos = _planet_positions(today_jd)
+
+        transits = _find_transits(natal_pos, today_pos)
+        top = transits[:3]  # most exact transits only
+
+        today_sun_sign = _zodiac_sign(today_pos["Sun"])
+        natal_sun_sign = _zodiac_sign(natal_pos["Sun"])
+        natal_moon_sign = _zodiac_sign(natal_pos["Moon"])
+
+        snapshot = {
+            "birth_date": birth_date_str,
+            "today": today_date.isoformat(),
+            "natal_sun": natal_sun_sign,
+            "natal_moon": natal_moon_sign,
+            "today_sun": today_sun_sign,
+            "active_transits": transits[:5],
+        }
+
+        if not top:
+            prompt = (
+                f"Natal chart: Sun in {natal_sun_sign}, Moon in {natal_moon_sign}. "
+                f"Today's Sun is in {today_sun_sign}. "
+                "No exact transits today — a quiet, stable day energetically. "
+                "(Always write the tip in English.)"
+            )
+        else:
+            transit_lines = "; ".join(_format_transit(t) for t in top)
+            prompt = (
+                f"Natal chart: Sun in {natal_sun_sign}, Moon in {natal_moon_sign}. "
+                f"Today's Sun is in {today_sun_sign}. "
+                f"Active transits: {transit_lines}. "
+                "Use these planetary themes to colour the tip — "
+                "keep it grounded and actionable, not predictive or fatalistic. "
+                "(Always write the tip in English.)"
+            )
+
+        return self._make_output(inp, prompt, snapshot)

ml/agents/tarot.py

@@ -0,0 +1,110 @@
+"""TAROT agent — three-card draw (situation / action / outcome).
+
+Draws cards deterministically from a daily seed so the reading stays
+stable for the day (same cards whether the agent runs at 08:00 or 14:00).
+Card meanings are precomputed here and passed as a structured snippet to
+the orchestrator, which weaves them into a grounded, actionable tip.
+"""
+from __future__ import annotations
+
+import hashlib
+from typing import ClassVar
+
+from .base import BaseAgent, AgentInput, AgentOutput
+from .manifest import AgentManifest
+
+# ---------------------------------------------------------------------------
+# Card definitions — Major Arcana only (22 cards, indices 0–21)
+# Each entry: (name, upright_meaning, action_hint)
+# ---------------------------------------------------------------------------
+_CARDS: list[tuple[str, str, str]] = [
+    ("The Fool",         "new beginnings, spontaneity, a leap of faith",          "start something without overthinking"),
+    ("The Magician",     "skill, willpower, resourcefulness",                      "use what you already have"),
+    ("The High Priestess","intuition, inner knowing, patience",                    "listen to what you already sense is true"),
+    ("The Empress",      "abundance, creativity, nurturing",                       "invest energy in something generative"),
+    ("The Emperor",      "structure, authority, discipline",                       "set a boundary or impose order"),
+    ("The Hierophant",   "tradition, guidance, shared values",                     "seek or offer mentorship"),
+    ("The Lovers",       "alignment, choice, commitment",                          "make a decision you have been avoiding"),
+    ("The Chariot",      "determination, focus, forward motion",                   "push through the resistance"),
+    ("Strength",         "inner courage, patience, gentle persistence",            "stay the course with compassion"),
+    ("The Hermit",       "solitude, reflection, inner guidance",                   "step back and think before acting"),
+    ("Wheel of Fortune", "cycles, turning points, inevitable change",              "acknowledge what is shifting around you"),
+    ("Justice",          "fairness, truth, cause and effect",                      "audit a recent decision for its real consequences"),
+    ("The Hanged Man",   "pause, surrender, new perspective",                      "release your grip on the outcome"),
+    ("Death",            "endings, transformation, release",                       "let go of what no longer serves you"),
+    ("Temperance",       "balance, moderation, patience",                          "blend two competing demands"),
+    ("The Devil",        "attachment, habit, shadow patterns",                     "name a loop you are stuck in"),
+    ("The Tower",        "sudden disruption, revelation, necessary collapse",      "accept the thing that already broke"),
+    ("The Star",         "hope, renewal, calm after the storm",                    "trust that recovery is already underway"),
+    ("The Moon",         "uncertainty, illusion, the unconscious",                 "sit with ambiguity rather than forcing clarity"),
+    ("The Sun",          "clarity, vitality, success",                             "act from your most energised self"),
+    ("Judgement",        "reflection, reckoning, a call to rise",                  "respond to a long-deferred summons"),
+    ("The World",        "completion, integration, a cycle closing",               "acknowledge what you have finished"),
+]
+
+_POSITIONS = ("situation", "action", "outcome")
+
+
+def _daily_draw(user_id: str, date_str: str) -> list[int]:
+    """Return three distinct card indices seeded by (user_id, date)."""
+    seed = hashlib.sha256(f"{user_id}:{date_str}".encode()).digest()
+    indices: list[int] = []
+    offset = 0
+    while len(indices) < 3:
+        val = int.from_bytes(seed[offset:offset + 2], "big") % len(_CARDS)
+        if val not in indices:
+            indices.append(val)
+        offset = (offset + 2) % (len(seed) - 1)
+    return indices
+
+
+MANIFEST = AgentManifest(
+    id="tarot",
+    version="1.0.0",
+    description="Daily three-card draw (situation/action/outcome) that frames the tip as a symbolic reflection.",
+    pref_schema={
+        "type": "object",
+        "additionalProperties": False,
+        "properties": {
+            "enabled": {
+                "type": "boolean",
+                "default": True,
+                "description": "Set false to disable the tarot agent for this user.",
+            },
+        },
+    },
+    context_schema=[],
+    required_consents=["data:core"],
+    output_contract={"type": "snippet", "format": "free_text"},
+    ttl_sec=3_600 * 6,   # stable for 6 h; refreshes mid-day at most twice
+    silenced_in_contexts=[],
+    inferred_params=[],
+)
+
+
+class TarotAgent(BaseAgent):
+    """Produces a three-card reading as a prompt snippet."""
+    agent_id: ClassVar[str] = MANIFEST.id
+    ttl_seconds: ClassVar[int] = MANIFEST.ttl_sec
+    version: ClassVar[str] = MANIFEST.version
+
+    def compute(self, inp: AgentInput) -> AgentOutput:
+        date_str = inp.now.strftime("%Y-%m-%d")
+        indices = _daily_draw(inp.user_id, date_str)
+
+        reading: list[dict] = []
+        parts: list[str] = [f"Today's tarot reading ({date_str}):"]
+        for pos, idx in zip(_POSITIONS, indices):
+            name, meaning, hint = _CARDS[idx]
+            reading.append({"position": pos, "card": name, "meaning": meaning, "hint": hint})
+            parts.append(f"  {pos.capitalize()} — {name}: {meaning}. Hint: {hint}.")
+
+        parts.append(
+            "Weave these symbolic themes lightly into the tip — "
+            "ground them in practical, specific action. "
+            "Do not explain the cards; let their meaning shape the advice."
+        )
+
+        prompt = "\n".join(parts)
+        snapshot = {"date": date_str, "reading": reading}
+        return self._make_output(inp, prompt, snapshot)

ml/agents/tests/test_agents.py

@@ -13,6 +13,8 @@ from ml.agents.momentum import MomentumAgent
 from ml.agents.time_of_day import TimeOfDayAgent
 from ml.agents.recent_patterns import RecentPatternsAgent
 from ml.agents.focus_area import FocusAreaAgent
+from ml.agents.tarot import TarotAgent, _daily_draw, _CARDS, _POSITIONS
+from ml.agents.stars import StarsAgent, _SWE_AVAILABLE
 from ml.agents.registry import get_agent, all_agents
 
 _NOW = datetime(2026, 5, 1, 9, 0, 0, tzinfo=timezone.utc)  # Thursday 09:00 UTC
@@ -250,13 +252,102 @@ class TestFocusAreaAgent:
         assert all("label" in c and "task_count" in c and "tasks" in c for c in clusters)
 
 
+# ── TarotAgent ────────────────────────────────────────────────────────────────
+
+class TestTarotAgent:
+    agent = TarotAgent()
+
+    def test_basic_output(self):
+        out = self.agent.compute(_inp())
+        _check_output(out, self.agent)
+        assert "situation" in out.prompt_text.lower()
+        assert "action" in out.prompt_text.lower()
+        assert "outcome" in out.prompt_text.lower()
+        assert out.signals_snapshot["date"] == "2026-05-01"
+        assert len(out.signals_snapshot["reading"]) == 3
+
+    def test_three_distinct_cards(self):
+        out = self.agent.compute(_inp())
+        cards = [r["card"] for r in out.signals_snapshot["reading"]]
+        assert len(set(cards)) == 3
+
+    def test_positions_labelled(self):
+        out = self.agent.compute(_inp())
+        positions = [r["position"] for r in out.signals_snapshot["reading"]]
+        assert positions == list(_POSITIONS)
+
+    def test_daily_stability(self):
+        out1 = self.agent.compute(_inp(now=datetime(2026, 5, 1, 8, 0, 0, tzinfo=timezone.utc)))
+        out2 = self.agent.compute(_inp(now=datetime(2026, 5, 1, 20, 0, 0, tzinfo=timezone.utc)))
+        assert out1.signals_snapshot["reading"] == out2.signals_snapshot["reading"]
+
+    def test_different_days_different_draw(self):
+        out1 = self.agent.compute(_inp(now=datetime(2026, 5, 1, 9, 0, 0, tzinfo=timezone.utc)))
+        out2 = self.agent.compute(_inp(now=datetime(2026, 5, 2, 9, 0, 0, tzinfo=timezone.utc)))
+        assert out1.signals_snapshot["reading"] != out2.signals_snapshot["reading"]
+
+    def test_different_users_different_draw(self):
+        out1 = self.agent.compute(_inp(user_id="user-A"))
+        out2 = self.agent.compute(_inp(user_id="user-B"))
+        assert out1.signals_snapshot["reading"] != out2.signals_snapshot["reading"]
+
+    def test_daily_draw_returns_valid_indices(self):
+        indices = _daily_draw("u1", "2026-05-01")
+        assert len(indices) == 3
+        assert len(set(indices)) == 3
+        assert all(0 <= i < len(_CARDS) for i in indices)
+
+
+# ── StarsAgent ────────────────────────────────────────────────────────────────
+
+class TestStarsAgent:
+    agent = StarsAgent()
+
+    def test_no_birth_date(self):
+        out = self.agent.compute(_inp())
+        _check_output(out, self.agent)
+        assert out.signals_snapshot.get("no_birth_date") is True
+        assert "birth date" in out.prompt_text.lower()
+
+    @pytest.mark.skipif(not _SWE_AVAILABLE, reason="pyswisseph not installed")
+    def test_invalid_birth_date(self):
+        out = self.agent.compute(_inp(agent_prefs={"birth_date": "not-a-date"}))
+        _check_output(out, self.agent)
+        assert out.signals_snapshot.get("invalid_birth_date") == "not-a-date"
+
+    @pytest.mark.skipif(not _SWE_AVAILABLE, reason="pyswisseph not installed")
+    def test_with_birth_date(self):
+        out = self.agent.compute(_inp(agent_prefs={"birth_date": "1990-06-15"}))
+        _check_output(out, self.agent)
+        assert "natal" in out.prompt_text.lower()
+        assert out.signals_snapshot["birth_date"] == "1990-06-15"
+        assert "natal_sun" in out.signals_snapshot
+        assert "natal_moon" in out.signals_snapshot
+
+    @pytest.mark.skipif(not _SWE_AVAILABLE, reason="pyswisseph not installed")
+    def test_transit_snapshot_structure(self):
+        out = self.agent.compute(_inp(agent_prefs={"birth_date": "1985-03-21"}))
+        snap = out.signals_snapshot
+        assert "active_transits" in snap
+        for t in snap["active_transits"]:
+            assert {"transit_planet", "natal_planet", "aspect", "nature", "orb"} <= t.keys()
+
+    def test_swe_unavailable_path(self, monkeypatch):
+        import ml.agents.stars as stars_mod
+        monkeypatch.setattr(stars_mod, "_SWE_AVAILABLE", False)
+        agent = StarsAgent()
+        out = agent.compute(_inp(agent_prefs={"birth_date": "1990-06-15"}))
+        _check_output(out, agent)
+        assert out.signals_snapshot.get("swe_unavailable") is True
+
+
 # ── Registry ─────────────────────────────────────────────────────────────────
 
 class TestRegistry:
     def test_all_agents_present(self):
         agents = all_agents()
         ids = {a.agent_id for a in agents}
-        assert ids == {"overdue-task", "momentum", "time-of-day", "recent-patterns", "focus-area", "health-vitals"}
+        assert ids == {"overdue-task", "momentum", "time-of-day", "recent-patterns", "focus-area", "health-vitals", "tarot", "stars"}
 
     def test_get_agent(self):
         a = get_agent("momentum")

ml/serving/requirements.txt

@@ -8,3 +8,4 @@ nats-py>=2.9.0
 structlog>=24.1.0
 sentry-sdk>=2.0.0
 mlflow-skinny>=3.1.0
+pyswisseph>=2.10.3.2

services/api/src/routes/integrations.ts

@@ -17,17 +17,9 @@ const GOOGLE_TOKEN_URL = 'https://oauth2.googleapis.com/token';
 const GOOGLE_REVOKE_URL = 'https://oauth2.googleapis.com/revoke';
 
 const GOOGLE_HEALTH_SCOPES = [
-  'https://www.googleapis.com/auth/fitness.activity.read',
+  'https://www.googleapis.com/auth/googlehealth.activity_and_fitness.readonly',
-  'https://www.googleapis.com/auth/fitness.body.read',
+  'https://www.googleapis.com/auth/googlehealth.health_metrics_and_measurements.readonly',
-  'https://www.googleapis.com/auth/fitness.sleep.read',
+  'https://www.googleapis.com/auth/googlehealth.sleep.readonly',
-  'https://www.googleapis.com/auth/fitness.heart_rate.read',
-  'https://www.googleapis.com/auth/fitness.nutrition.read',
-  'https://www.googleapis.com/auth/fitness.location.read',
-  'https://www.googleapis.com/auth/fitness.blood_glucose.read',
-  'https://www.googleapis.com/auth/fitness.blood_pressure.read',
-  'https://www.googleapis.com/auth/fitness.body_temperature.read',
-  'https://www.googleapis.com/auth/fitness.oxygen_saturation.read',
-  'https://www.googleapis.com/auth/fitness.reproductive_health.read',
 ].join(' ');
 
 // In-memory CSRF state store

services/api/src/routes/recommender.ts

@@ -2,7 +2,7 @@ import { type Router as ExpressRouter, Router, Response } from 'express';
 import { nanoid } from 'nanoid';
 import { logger } from '../logger.js';
 import { db } from '../db/index.js';
-import { integrationTokens, tipFeedback, tipViews, tipScores, userPreferences } from '../db/schema.js';
+import { tipFeedback, tipViews, tipScores, userPreferences } from '../db/schema.js';
 import { eq, and, desc } from 'drizzle-orm';
 import { requireAuth, AuthenticatedRequest } from '../middleware/session.js';
 import { config } from '../config.js';
@@ -144,17 +144,6 @@ router.post('/recommend', requireAuth, async (req: AuthenticatedRequest, res: Re
   const dayOfWeek = new Date().getDay();
   const { recent_tip: recentTip } = req.body as { recent_tip?: string };
 
-  const anyToken = await db
-    .select({ id: integrationTokens.id })
-    .from(integrationTokens)
-    .where(eq(integrationTokens.userId, req.userId!))
-    .limit(1);
-
-  if (!anyToken.length) {
-    res.status(422).json({ error: 'No integrations connected' });
-    return;
-  }
-
   const signals = await aggregator.fetchAll(req.userId!);
 
   const t0 = Date.now();

services/api/src/signals/__tests__/agent-scheduler.test.ts

@@ -0,0 +1,166 @@
+/**
+ * Tests for the agent pre-compute scheduler (signals/agent-scheduler.ts).
+ *
+ * Key behaviour under test: runCycle calls getEligibleAgentIds per user and
+ * skips computeAndStore for agents the user hasn't consented to.
+ */
+import { describe, it, expect, vi, beforeEach, afterEach } from 'vitest';
+
+vi.mock('../../logger.js', () => ({
+  logger: { info: vi.fn(), warn: vi.fn(), error: vi.fn(), fatal: vi.fn() },
+}));
+import { logger } from '../../logger.js';
+
+// ── active-user query: db.selectDistinct(...).from(...).where(...) ──────────
+let activeUsers: { userId: string }[] = [];
+const userWhereMock = vi.fn(async () => activeUsers);
+const userFromMock = vi.fn(() => ({ where: userWhereMock }));
+const selectDistinctMock = vi.fn(() => ({ from: userFromMock }));
+
+// ── purge: db.delete(...).where(...) ────────────────────────────────────────
+const deleteWhereMock = vi.fn(async () => ({}));
+const deleteMock = vi.fn(() => ({ where: deleteWhereMock }));
+
+vi.mock('../../db/index.js', () => ({
+  db: { selectDistinct: selectDistinctMock, delete: deleteMock },
+}));
+
+vi.mock('../../db/schema.js', () => ({
+  agentOutputs: { expiresAt: 'expires_at' },
+  tipViews: { userId: 'user_id', servedAt: 'served_at' },
+}));
+
+vi.mock('drizzle-orm', () => ({
+  gt: vi.fn(),
+  lt: vi.fn(),
+  and: vi.fn(),
+  eq: vi.fn(),
+  isNull: vi.fn(),
+}));
+
+vi.mock('../../config.js', () => ({ config: { ML_SERVING_URL: 'http://ml' } }));
+
+// ── computeAndStore — tracks which (user, agent) pairs were computed ────────
+const computeAndStoreMock = vi.fn(async () => {});
+vi.mock('../../routes/agent-outputs.js', () => ({
+  computeAndStore: computeAndStoreMock,
+}));
+
+// ── eligibility — replaceable per test ─────────────────────────────────────
+let eligibleIds: Set<string> = new Set();
+const getEligibleAgentIdsMock = vi.fn(async (_userId: string) => eligibleIds);
+vi.mock('../../profile/eligibility.js', () => ({
+  getEligibleAgentIds: getEligibleAgentIdsMock,
+}));
+
+// ml-serving /health — return a fixed agent list
+global.fetch = vi.fn(async () => ({
+  ok: true,
+  json: async () => ({ agents: ['overdue-task', 'momentum', 'time-of-day'] }),
+})) as unknown as typeof fetch;
+
+beforeEach(() => {
+  activeUsers = [];
+  eligibleIds = new Set();
+  computeAndStoreMock.mockClear();
+  getEligibleAgentIdsMock.mockClear();
+  userWhereMock.mockClear();
+  deleteWhereMock.mockClear();
+  vi.clearAllMocks();
+  vi.useFakeTimers();
+  // restore default mocks after clearAllMocks
+  userWhereMock.mockImplementation(async () => activeUsers);
+  getEligibleAgentIdsMock.mockImplementation(async () => eligibleIds);
+  computeAndStoreMock.mockResolvedValue(undefined);
+  deleteWhereMock.mockResolvedValue({});
+  global.fetch = vi.fn(async () => ({
+    ok: true,
+    json: async () => ({ agents: ['overdue-task', 'momentum', 'time-of-day'] }),
+  })) as unknown as typeof fetch;
+});
+
+afterEach(() => {
+  vi.useRealTimers();
+});
+
+describe('startAgentPrecomputeScheduler', () => {
+  it('skips computeAndStore for agents not in the eligibility set', async () => {
+    activeUsers = [{ userId: 'alice' }];
+    eligibleIds = new Set(['momentum']); // only momentum consented
+
+    const { startAgentPrecomputeScheduler } = await import('../agent-scheduler.js');
+    startAgentPrecomputeScheduler(60_000);
+    await vi.advanceTimersByTimeAsync(16_000);
+    await Promise.resolve();
+
+    const computed = computeAndStoreMock.mock.calls.map((c) => c[1]);
+    expect(computed).toEqual(['momentum']);
+    expect(computed).not.toContain('overdue-task');
+    expect(computed).not.toContain('time-of-day');
+  });
+
+  it('skips all agents when eligibility set is empty', async () => {
+    activeUsers = [{ userId: 'bob' }];
+    eligibleIds = new Set(); // no consents
+
+    const { startAgentPrecomputeScheduler } = await import('../agent-scheduler.js');
+    startAgentPrecomputeScheduler(60_000);
+    await vi.advanceTimersByTimeAsync(16_000);
+    await Promise.resolve();
+
+    expect(computeAndStoreMock).not.toHaveBeenCalled();
+    expect(logger.info).toHaveBeenCalledWith(
+      expect.objectContaining({ skipped: 3, ok: 0 }),
+      'agent-scheduler: cycle complete',
+    );
+  });
+
+  it('computes all agents when all are eligible', async () => {
+    activeUsers = [{ userId: 'carol' }];
+    eligibleIds = new Set(['overdue-task', 'momentum', 'time-of-day']);
+
+    const { startAgentPrecomputeScheduler } = await import('../agent-scheduler.js');
+    startAgentPrecomputeScheduler(60_000);
+    await vi.advanceTimersByTimeAsync(16_000);
+    await Promise.resolve();
+
+    expect(computeAndStoreMock).toHaveBeenCalledTimes(3);
+    expect(logger.info).toHaveBeenCalledWith(
+      expect.objectContaining({ ok: 3, skipped: 0 }),
+      'agent-scheduler: cycle complete',
+    );
+  });
+
+  it('skips entire user when eligibility check throws', async () => {
+    activeUsers = [{ userId: 'dave' }];
+    getEligibleAgentIdsMock.mockRejectedValueOnce(new Error('db timeout'));
+
+    const { startAgentPrecomputeScheduler } = await import('../agent-scheduler.js');
+    startAgentPrecomputeScheduler(60_000);
+    await vi.advanceTimersByTimeAsync(16_000);
+    await Promise.resolve();
+
+    expect(computeAndStoreMock).not.toHaveBeenCalled();
+    expect(logger.error).toHaveBeenCalledWith(
+      expect.objectContaining({ err: expect.anything(), userId: 'dave' }),
+      'agent-scheduler: eligibility check failed, skipping user',
+    );
+  });
+
+  it('checks eligibility independently per user', async () => {
+    activeUsers = [{ userId: 'u1' }, { userId: 'u2' }];
+    getEligibleAgentIdsMock.mockImplementation(async (userId: string) =>
+      userId === 'u1' ? new Set(['momentum']) : new Set(['overdue-task', 'time-of-day']),
+    );
+
+    const { startAgentPrecomputeScheduler } = await import('../agent-scheduler.js');
+    startAgentPrecomputeScheduler(60_000);
+    await vi.advanceTimersByTimeAsync(16_000);
+    await Promise.resolve();
+
+    const u1Calls = computeAndStoreMock.mock.calls.filter((c) => c[0] === 'u1').map((c) => c[1]);
+    const u2Calls = computeAndStoreMock.mock.calls.filter((c) => c[0] === 'u2').map((c) => c[1]);
+    expect(u1Calls).toEqual(['momentum']);
+    expect(u2Calls.sort()).toEqual(['overdue-task', 'time-of-day']);
+  });
+});

services/api/src/signals/agent-scheduler.ts

@@ -15,6 +15,7 @@ import { gt, lt } from 'drizzle-orm';
 import { logger } from '../logger.js';
 import { config } from '../config.js';
 import { computeAndStore } from '../routes/agent-outputs.js';
+import { getEligibleAgentIds } from '../profile/eligibility.js';
 
 const FALLBACK_AGENT_IDS = [
   'overdue-task',
@@ -67,8 +68,22 @@ async function runCycle(agentIds: string[]): Promise<void> {
   let ok = 0;
   let failed = 0;
 
+  let skipped = 0;
   for (const userId of userIds) {
+    let eligible: Set<string>;
+    try {
+      eligible = await getEligibleAgentIds(userId);
+    } catch (err: any) {
+      logger.error({ err, userId }, 'agent-scheduler: eligibility check failed, skipping user');
+      skipped += agentIds.length;
+      continue;
+    }
+
     for (const agentId of agentIds) {
+      if (!eligible.has(agentId)) {
+        skipped++;
+        continue;
+      }
       try {
         await computeAndStore(userId, agentId);
         ok++;
@@ -86,7 +101,7 @@ async function runCycle(agentIds: string[]): Promise<void> {
   }
 
   logger.info(
-    { ok, failed, users: userIds.length, agents: agentIds.length },
+    { ok, failed, skipped, users: userIds.length, agents: agentIds.length },
     'agent-scheduler: cycle complete',
   );
 }

services/api/src/signals/google-health.ts

@@ -7,33 +7,20 @@ import { config } from '../config.js';
 import { logger } from '../logger.js';
 
 const CACHE_TTL_MS = 5 * 60_000;
-const FIT_AGGREGATE_URL = 'https://www.googleapis.com/fitness/v1/users/me/dataset:aggregate';
+const HEALTH_API_BASE = 'https://health.googleapis.com/v4/users/me/dataTypes';
-const FIT_SESSIONS_URL = 'https://www.googleapis.com/fitness/v1/users/me/sessions';
 const GOOGLE_TOKEN_URL = 'https://oauth2.googleapis.com/token';
 
 const STEP_DAILY_GOAL = 7_000;
 const SLEEP_GOAL_HOURS = 7;
 
-interface FitBucket {
+// v4 DataPoint shape is a union keyed by data type; we read defensively.
-  dataset: Array<{
+interface DataPoint {
-    dataSourceId: string;
+  [key: string]: unknown;
-    point: Array<{ value: Array<{ intVal?: number; fpVal?: number }> }>;
-  }>;
 }
 
-interface FitAggregateResponse {
+interface DataPointsResponse {
-  bucket?: FitBucket[];
+  dataPoints?: DataPoint[];
-}
+  nextPageToken?: string;
-
-interface FitSession {
-  name: string;
-  startTimeMillis: string;
-  endTimeMillis: string;
-  activityType: number;
-}
-
-interface FitSessionsResponse {
-  session?: FitSession[];
 }
 
 async function refreshGoogleToken(
@@ -66,81 +53,62 @@ async function refreshGoogleToken(
   return data.access_token;
 }
 
-function todayMidnightMs(): number {
+function todayMidnightIso(): string {
   const d = new Date();
   d.setHours(0, 0, 0, 0);
-  return d.getTime();
+  return d.toISOString();
 }
 
 function yesterdayIso(): string {
   return new Date(Date.now() - 24 * 60 * 60 * 1000).toISOString();
 }
 
-async function fetchAggregates(
+async function fetchDataPoints(
   token: string,
-  startMs: number,
+  dataType: string,
-  endMs: number,
+  filter: string,
-): Promise<FitAggregateResponse> {
+): Promise<DataPoint[]> {
-  const res = await fetch(FIT_AGGREGATE_URL, {
+  const url = new URL(`${HEALTH_API_BASE}/${dataType}/dataPoints`);
-    method: 'POST',
+  url.searchParams.set('filter', filter);
-    headers: { Authorization: `Bearer ${token}`, 'Content-Type': 'application/json' },
-    body: JSON.stringify({
-      aggregateBy: [
-        { dataTypeName: 'com.google.step_count.delta' },
-        { dataTypeName: 'com.google.calories.expended' },
-        { dataTypeName: 'com.google.active_minutes' },
-        { dataTypeName: 'com.google.heart_rate.bpm' },
-      ],
-      bucketByTime: { durationMillis: endMs - startMs },
-      startTimeMillis: String(startMs),
-      endTimeMillis: String(endMs),
-    }),
-  });
-  if (!res.ok) throw new Error(`Fit aggregate: ${res.status}`);
-  return res.json() as Promise<FitAggregateResponse>;
-}
-
-async function fetchSleepSessions(token: string): Promise<FitSessionsResponse> {
-  const url = new URL(FIT_SESSIONS_URL);
-  url.searchParams.set('activityType', '72');
-  url.searchParams.set('startTime', yesterdayIso());
-  url.searchParams.set('endTime', new Date().toISOString());
   const res = await fetch(url.toString(), {
     headers: { Authorization: `Bearer ${token}` },
   });
-  if (!res.ok) throw new Error(`Fit sessions: ${res.status}`);
+  if (!res.ok) throw new Error(`health ${dataType}: ${res.status}`);
-  return res.json() as Promise<FitSessionsResponse>;
+  const data = (await res.json()) as DataPointsResponse;
+  return data.dataPoints ?? [];
 }
 
-function extractMetric(
+// Defensive numeric reader — probes likely field names in a v4 DataPoint payload.
-  bucket: FitBucket,
+function readNumber(point: DataPoint, paths: string[][]): number {
-  dataTypeName: string,
+  for (const path of paths) {
-  valueKey: 'intVal' | 'fpVal',
+    let cur: unknown = point;
-): number {
+    for (const key of path) {
-  for (const ds of bucket.dataset) {
+      if (cur && typeof cur === 'object' && key in (cur as object)) {
-    if (!ds.dataSourceId.includes(dataTypeName.replace('com.google.', '').replace('.', '_'))) continue;
+        cur = (cur as Record<string, unknown>)[key];
-    for (const pt of ds.point) {
+      } else {
-      const v = pt.value[0];
+        cur = undefined;
-      if (v) return valueKey === 'intVal' ? (v.intVal ?? 0) : (v.fpVal ?? 0);
+        break;
       }
     }
+    if (typeof cur === 'number') return cur;
+  }
   return 0;
 }
 
-function extractAnyMetric(
+function readString(point: DataPoint, paths: string[][]): string | undefined {
-  bucket: FitBucket,
+  for (const path of paths) {
-  typeSuffix: string,
+    let cur: unknown = point;
-  valueKey: 'intVal' | 'fpVal',
+    for (const key of path) {
-): number {
+      if (cur && typeof cur === 'object' && key in (cur as object)) {
-  for (const ds of bucket.dataset) {
+        cur = (cur as Record<string, unknown>)[key];
-    if (!ds.dataSourceId.includes(typeSuffix)) continue;
+      } else {
-    const pt = ds.point[0];
+        cur = undefined;
-    if (pt?.value[0]) {
+        break;
-      const v = pt.value[0];
-      return valueKey === 'intVal' ? (v.intVal ?? 0) : (v.fpVal ?? 0);
       }
     }
-  return 0;
+    if (typeof cur === 'string') return cur;
+  }
+  return undefined;
 }
 
 export class GoogleHealthSignalSource implements SignalSource {
@@ -187,21 +155,35 @@ export class GoogleHealthSignalSource implements SignalSource {
     }
 
     try {
-      const startMs = todayMidnightMs();
+      const dayStartIso = todayMidnightIso();
-      const endMs = Date.now();
+      const dayEndIso = new Date().toISOString();
+      const yIso = yesterdayIso();
 
-      const [aggData, sleepData] = await Promise.all([
+      const stepsFilter = `steps.interval.start_time >= "${dayStartIso}" AND steps.interval.start_time < "${dayEndIso}"`;
-        fetchAggregates(token, startMs, endMs),
+      const caloriesFilter = `total_calories.interval.start_time >= "${dayStartIso}" AND total_calories.interval.start_time < "${dayEndIso}"`;
-        fetchSleepSessions(token),
+      const hrFilter = `heart_rate.sample_time.physical_time >= "${dayStartIso}" AND heart_rate.sample_time.physical_time < "${dayEndIso}"`;
+      const sleepFilter = `sleep.interval.start_time >= "${yIso}" AND sleep.interval.start_time < "${dayEndIso}"`;
+
+      const [stepsPts, caloriesPts, hrPts, sleepPts] = await Promise.all([
+        fetchDataPoints(token, 'steps', stepsFilter),
+        fetchDataPoints(token, 'total-calories', caloriesFilter),
+        fetchDataPoints(token, 'heart-rate', hrFilter),
+        fetchDataPoints(token, 'sleep', sleepFilter),
       ]);
 
-      const bucket = aggData.bucket?.[0];
+      // One-time peek at raw shape so we can refine field paths after first real OAuth.
+      logger.debug(
+        { userId, samples: { stepsPts: stepsPts.slice(0, 1), caloriesPts: caloriesPts.slice(0, 1), hrPts: hrPts.slice(0, 1), sleepPts: sleepPts.slice(0, 1) } },
+        'google-health: v4 dataPoints sample',
+      );
+
       const signals: Signal[] = [];
       const now = new Date().toISOString();
 
-      if (bucket) {
+      const steps = stepsPts.reduce(
-        // Steps
+        (sum, p) => sum + readNumber(p, [['steps', 'count'], ['count']]),
-        const steps = extractAnyMetric(bucket, 'step_count', 'intVal');
+        0,
+      );
       const stepGoalPct = Math.round((steps / STEP_DAILY_GOAL) * 100);
       signals.push({
         id: `google-health:steps`,
@@ -218,26 +200,31 @@ export class GoogleHealthSignalSource implements SignalSource {
         timestamp: now,
       });
 
-        // Calories + active minutes
+      const calories = Math.round(
-        const calories = Math.round(extractAnyMetric(bucket, 'calories', 'fpVal'));
+        caloriesPts.reduce(
-        const activeMinutes = extractAnyMetric(bucket, 'active_minutes', 'intVal');
+          (sum, p) =>
+            sum + readNumber(p, [['totalCalories', 'kilocalories'], ['kilocalories'], ['energy', 'kilocalories']]),
+          0,
+        ),
+      );
       signals.push({
         id: `google-health:activity`,
         source: 'google-health',
         kind: 'health',
-          content: `${activeMinutes} active minutes, ${calories} calories burned today`,
+        content: `${calories} calories burned today`,
         metadata: { dataType: 'activity' },
         features: {
-            active_minutes: activeMinutes,
           calories_burned: calories,
-            sedentary: activeMinutes < 20,
         },
         timestamp: now,
       });
 
-        // Heart rate
+      if (hrPts.length > 0) {
-        const bpm = Math.round(extractAnyMetric(bucket, 'heart_rate', 'fpVal'));
+        const hrValues = hrPts
-        if (bpm > 0) {
+          .map((p) => readNumber(p, [['heartRate', 'beatsPerMinute'], ['beatsPerMinute']]))
+          .filter((v) => v > 0);
+        if (hrValues.length > 0) {
+          const bpm = Math.round(hrValues.reduce((a, b) => a + b, 0) / hrValues.length);
           signals.push({
             id: `google-health:heart_rate`,
             source: 'google-health',
@@ -250,13 +237,17 @@ export class GoogleHealthSignalSource implements SignalSource {
         }
       }
 
-      // Sleep — find the most recent sleep session
+      if (sleepPts.length > 0) {
-      if (sleepData.session?.length) {
+        const sleepSessions = sleepPts
-        const sorted = [...sleepData.session].sort(
+          .map((p) => ({
-          (a, b) => Number(b.endTimeMillis) - Number(a.endTimeMillis),
+            start: readString(p, [['sleep', 'interval', 'startTime'], ['interval', 'startTime'], ['startTime']]),
-        );
+            end: readString(p, [['sleep', 'interval', 'endTime'], ['interval', 'endTime'], ['endTime']]),
-        const last = sorted[0]!;
+          }))
-        const durationMs = Number(last.endTimeMillis) - Number(last.startTimeMillis);
+          .filter((s): s is { start: string; end: string } => !!s.start && !!s.end)
+          .sort((a, b) => Date.parse(b.end) - Date.parse(a.end));
+        const last = sleepSessions[0];
+        if (last) {
+          const durationMs = Date.parse(last.end) - Date.parse(last.start);
           const sleepHours = Math.round((durationMs / 3_600_000) * 10) / 10;
           const belowGoal = sleepHours < SLEEP_GOAL_HOURS;
           signals.push({
@@ -264,7 +255,7 @@ export class GoogleHealthSignalSource implements SignalSource {
             source: 'google-health',
             kind: 'health',
             content: `${sleepHours}h sleep last night (${belowGoal ? 'below' : 'meets'} ${SLEEP_GOAL_HOURS}h goal)`,
-          metadata: { dataType: 'sleep', sessionName: last.name },
+            metadata: { dataType: 'sleep' },
             features: {
               sleep_hours: sleepHours,
               sleep_goal_hours: SLEEP_GOAL_HOURS,
@@ -274,6 +265,7 @@ export class GoogleHealthSignalSource implements SignalSource {
             timestamp: now,
           });
         }
+      }
 
       this.cache.set(userId, { signals, fetchedAt: Date.now() });
       bus.publish('signals.task.synced', {