feat(consents): auto-grant data:<provider> on connect; remove agent: consents (ADR-0015)

- integrations.ts: grant data:<provider> on OAuth callback, revoke on disconnect - Backfill migration: INSERT OR IGNORE data:<provider> for all active tokens - Agent manifests: drop agent:<id> from required_consents (momentum, time-of-day, overdue-task, recent-patterns, health-vitals) — per-agent control is a preference - eligibility.ts: update comment to reflect data:-only consent model - test_manifest.py: assert no agent: consents remain in any manifest - migrations.test.ts: backfill idempotency tests for issue #127 - Dockerfile.api: drop --offline flag (fixes ERR_PNPM_NO_OFFLINE_META) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
docs: update focus-area manifest description and CLAUDE.md
2026-05-12 15:09:58 +00:00 · 2026-05-12 15:00:06 +00:00 · 2026-05-12 14:58:31 +00:00 · 2026-05-12 14:57:04 +00:00 · 2026-05-12 14:46:43 +00:00 · 2026-05-12 14:45:15 +00:00
31 changed files with 1253 additions and 290 deletions
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -92,7 +92,7 @@ oO generates tips through a multi-agent pipeline (ADR-0013): pre-compute agents
 | Alias | Model | Used by |
 |-------|-------|---------|
 | `tip-generator` | qwen2.5:1.5b (default) | `ml/serving` tip generation |
-| `embedder` | nomic-embed-text | task clustering, dedup |
+| `embedder` | nomic-embed-text | task clustering (after LLM enrichment), dedup |
 | `judge` | claude-haiku-4-5 (cloud, eval only) | offline sim |

 Env vars: `LITELLM_URL` (prod `https://llm.alogins.net`), `OLLAMA_URL` (Agap host, `http://host.docker.internal:11434` from containers).
@@ -101,17 +101,83 @@ Ollama and LiteLLM are **shared Agap services**, not oO services — they live i

 All `httpx` calls in `ml/` must use `trust_env=False` to bypass the system proxy — same rule as `bw` and curl. Pattern: `httpx.Client(trust_env=False, timeout=N)`.

-MLflow container-to-container calls: always pass `host_header="localhost"` to `MLflowClient` — MLflow's `--allowed-hosts` rejects `Host: mlflow` (the container DNS name) with 403. Auth credential is `MLFLOW_ADMIN_PASSWORD`. MLflow REST API lives at the origin root (`/api/2.0/mlflow`), not under the `/mlflow` UI prefix.
+MLflow container-to-container calls: always pass `host_header="localhost"` to `MLflowClient` — MLflow's `--allowed-hosts` rejects `Host: mlflow` (the container DNS name) with 403. Auth credential is `MLFLOW_ADMIN_PASSWORD`. MLflow REST API lives at the origin root, not under the `/mlflow` UI prefix.
+
+### MLflow API versions — runs vs traces
+
+MLflow uses **two API versions** — use the right one or you'll get 405:
+
+| What | API prefix | Example |
+|------|-----------|---------|
+| Runs, experiments, metrics | `/api/2.0/mlflow/` | `runs/search`, `experiments/list` |
+| Traces (LLM observability) | `/api/3.0/mlflow/traces/` | `traces/{trace_id}` |
+
+**Experiment IDs:** `3` = oO/serving. Artifacts stored as run tags prefixed `artifact:<path>`.
+
+### Querying from the host shell
+
+Always strip the proxy and pass `Host: localhost` (no port — `localhost:5000` fails the DNS-rebinding check).

-MLflow from the host shell — query with curl, no script needed:
 ```bash
+# Search recent runs (experiment 3)
 env -u HTTPS_PROXY -u HTTP_PROXY -u ALL_PROXY -u https_proxy -u http_proxy -u all_proxy \
  curl -s -H "Host: localhost" -u "admin:${MLFLOW_ADMIN_PASSWORD}" \
  -X POST http://localhost:5000/api/2.0/mlflow/runs/search \
  -H "Content-Type: application/json" \
-  -d '{"experiment_ids":["3"],"max_results":1,"order_by":["start_time DESC"]}'
+  -d '{"experiment_ids":["3"],"max_results":5,"order_by":["start_time DESC"]}'
+
+# Get a trace by ID (note: /api/3.0/, not /api/2.0/)
+env -u HTTPS_PROXY -u HTTP_PROXY -u ALL_PROXY -u https_proxy -u http_proxy -u all_proxy \
+  curl -s -H "Host: localhost" -u "admin:${MLFLOW_ADMIN_PASSWORD}" \
+  http://localhost:5000/api/3.0/mlflow/traces/tr-<trace_id> | python3 -m json.tool
+```
+
+The trace response includes `trace_metadata.mlflow.traceInputs/Outputs`, `trace_metadata.mlflow.trace.sizeStats` (num_spans), and `tags.mlflow.traceName`.
+
+### Getting spans (Python client from inside the container)
+
+The REST API has **no endpoint for spans** — `/api/3.0/mlflow/traces/{id}/spans` returns 404. Use the Python client inside `oo-ml-serving-1`:
+
+```bash
+docker exec oo-ml-serving-1 python3 -c "
+import mlflow, json, os
+mlflow.set_tracking_uri('http://mlflow:5000')
+os.environ['MLFLOW_TRACKING_USERNAME'] = 'admin'
+os.environ['MLFLOW_TRACKING_PASSWORD'] = os.environ.get('MLFLOW_ADMIN_PASSWORD', '')
+
+client = mlflow.tracking.MlflowClient()
+trace = client.get_trace('tr-<trace_id>')
+for span in trace.data.spans:
+    print(span.name, '| parent:', span.parent_id, '| status:', span.status)
+    print('  inputs:', json.dumps(span.inputs)[:200])
+    print('  outputs:', json.dumps(span.outputs)[:200])
+    print('  attrs:', span.attributes)
+"
+```
+
+### Span structure for a tip generation trace
+
+A healthy `recommend` trace has 3 spans:
+
+| Span | Type | Parent | Key attributes |
+|------|------|--------|---------------|
+| `recommend` | CHAIN | (root) | `agent_count`, `latency_ms`; inputs include `agent_ids` list |
+| `build_context` | TOOL | recommend | `agent_count`, `task_count`, `science_destiny` |
+| `llm_orchestrator` | LLM | recommend | `prompt_tokens`, `completion_tokens`, `model`, `attempts` |
+
+### Diagnosing "no agents in trace"
+
+If the trace shows `agent_ids: []` and `agent_count: 0` in the root span, and the orchestrator prompt says *"No pre-computed agent context available"*, it means the recommender found zero eligible snippets at request time. Causes:
+
+1. **Agent compute hasn't run** — no `agent_outputs` rows for this user yet
+2. **Snippets expired** — TTL elapsed since last compute
+3. **Eligibility filter dropped all agents** — none passed the manifest-driven check
+
+Diagnose with:
+```bash
+docker exec oo-api-1 psql "$DATABASE_URL" -c \
+  "SELECT agent_id, computed_at, expires_at FROM agent_outputs WHERE user_id='<uid>' ORDER BY computed_at DESC LIMIT 10;"
 ```
-`Host: localhost` required (no port) — `localhost:5000` fails the DNS-rebinding check. Experiment IDs: `3`=oO/serving. Artifacts stored as run tags prefixed `artifact:<path>`.

 **Multi-agent tip generation pipeline (ADR-0013):**
 1. Pre-compute agents (`ml/agents/<id>/`) run on a schedule, each emitting a snippet into `agent_outputs` with a per-agent TTL
@@ -131,7 +197,7 @@ Recent completions:
 - ADR-0012 — ε-greedy v2 (D=12) — 2026-04-26 (now superseded by ADR-0013)
 - ADR-0014 complete: unified Profile schema + backfill, manifest plumbing, `/api/profile` read-through, registry-driven eligibility filter, inference framework + per-agent inference, legacy consent column drop — 2026-05-05
 - Rich per-agent inference for all four active agents (#112, #114, #115, #116) — 2026-05-06: quiet/peak hours (time-of-day), z-score baseline (momentum), p50 lateness + project realness (overdue-task), adaptive lookback + weekly/daily cycles (recent-patterns)
- Semantic task clustering via nomic-embed-text + focus-area preferred_areas inference (#97, #113) — 2026-05-06: `ml/agents/clustering.py`, focus-area v2.0.0
+- Semantic task clustering via nomic-embed-text + LLM enrichment (#97, #113, #129) — 2026-05-12: `ml/agents/clustering.py`; titles expanded via `tip-generator` before embedding; persistent cache in `task_enrichments` table; recompute gated on task-list hash change; focus-area v3.0.0 outputs all clusters with enriched descriptions

 - Per-user feature freshness SLAs (#61) — 2026-05-06: `invalidated_by` mirrored into `ProfileFeature`; drift-detection test added
 - MLflow tracing added to `ml/serving` for all agent calls — 2026-05-06: `ml/serving/mlflow_client.py`; activated by `MLFLOW_TRACKING_URI=http://mlflow:5000` (default in compose `full` profile); requires `--profile mlops` for the MLflow container. Issue #118 (M4) tracks removal from production critical path.
@@ -157,7 +223,7 @@ Lives in `ml/agents/inference/`. `run_inference(manifest, history)` evaluates al
 - `infer()` error → emit `cold_start_default` (never crashes)
 - Results written to `user_preferences` with `source='inferred'`; keys with `source='user'` are never overwritten

-All five agents are at v1.2.0. Per-agent inferred params (all live in `ml/agents/<name>.py`):
+Per-agent inferred params (all live in `ml/agents/<name>.py`):

 | Agent | Inferred params | Notes |
 |-------|----------------|-------|
@@ -165,7 +231,7 @@ All five agents are at v1.2.0. Per-agent inferred params (all live in `ml/agents
 | `momentum` | `engagement_trend`, `baseline_completions_per_day`, `stdev` | Baseline = 28d rolling mean done/day; snippet uses z-score language |
 | `overdue-task` | `lateness_tolerance_days`, `project_realness` | Tolerance = p50 lateness from TaskCompletion history; realness = project median vs global median |
 | `recent-patterns` | `lookback_days`, `weekly_cycle`, `daily_cycle` | Lookback sized to ≥30 done events; cycles use peak-to-mean ratio; snippet hints when strength > 0.5 |
-| `focus-area` | `preferred_areas` | Top-2 project IDs by task completion count; semantic clustering via `ml/agents/clustering.py` in compute() |
+| `focus-area` | *(none)* | No inferred params. Clusters tasks via LLM-enriched embeddings and outputs all areas with expanded descriptions. Recomputes only when task list changes (hash-gated). |

 `UserHistory` carries both `events: list[FeedbackEvent]` and `task_completions: list[TaskCompletion]`. `AgentInferRequest` (ml/serving) accepts `task_completions: list[dict]` alongside `feedback_history`.

--- a/apps/admin/src/middleware.ts
+++ b/apps/admin/src/middleware.ts
@@ -4,8 +4,8 @@ import type { NextRequest } from 'next/server';
 export async function middleware(req: NextRequest) {
  const { pathname } = req.nextUrl;

-  // Pass through the login page and API calls
-  if (pathname.startsWith('/login') || pathname.startsWith('/api/')) {
+  // Pass through the login page, forbidden page, and API calls
+  if (pathname.startsWith('/login') || pathname.startsWith('/forbidden') || pathname.startsWith('/api/')) {
    return NextResponse.next();
  }

--- a/apps/web/src/app/tip/page.tsx
+++ b/apps/web/src/app/tip/page.tsx
@@ -40,11 +40,11 @@ export default function TipPage() {
    }
  }, [state]);

-  const loadTip = useCallback(async () => {
+  const loadTip = useCallback(async (recentTip?: string) => {
    setVisible(false);
    setState('loading');
    try {
-      const rec = await getRecommendation();
+      const rec = await getRecommendation(recentTip);
      if (!rec) {
        setState('empty');
        return;
@@ -62,10 +62,11 @@ export default function TipPage() {

  const react = async (action: 'done' | 'dismiss' | 'snooze') => {
    if (!tip) return;
+    const snoozedContent = action === 'snooze' ? tip.content : undefined;
    setVisible(false);
    setState('done');
    await sendFeedback(tip.id, { action });
-    setTimeout(() => loadTip(), 700);
+    setTimeout(() => loadTip(snoozedContent), 700);
  };

  const onPointerDown = () => {
@@ -170,7 +171,7 @@ export default function TipPage() {
              All clear.
            </p>
            <button
-              onClick={loadTip}
+              onClick={() => loadTip()}
              style={{
                marginTop: '2rem',
                background: 'transparent',
--- a/apps/web/src/lib/api.ts
+++ b/apps/web/src/lib/api.ts
@@ -23,9 +23,12 @@ export async function getSession() {
  return apiFetch<{ user: { id: string; email: string; name?: string; image?: string } | null }>('/auth/session');
 }

-export async function getRecommendation(): Promise<RecommendResponse | null> {
+export async function getRecommendation(recentTip?: string): Promise<RecommendResponse | null> {
  try {
-    return await apiFetch<RecommendResponse>('/recommend', { method: 'POST' });
+    return await apiFetch<RecommendResponse>('/recommend', {
+      method: 'POST',
+      body: JSON.stringify(recentTip ? { recent_tip: recentTip } : {}),
+    });
  } catch (e: any) {
    if (e.status === 204 || e.status === 422) return null;
    throw e;
--- a/docs/adr/0015-data-source-consents.md
+++ b/docs/adr/0015-data-source-consents.md
@@ -0,0 +1,44 @@
+# ADR-0015 — Data-source consents only; drop per-agent consent gate
+
+**Date:** 2026-05-11  
+**Status:** Accepted  
+**Supersedes:** ADR-0014 §3 (consent model)
+
+## Context
+
+ADR-0014 introduced `required_consents` on agent manifests. In practice two
+unrelated concepts were mixed into that field:
+
+- `data:<source>` — which data source the agent reads.
+- `agent:<id>` — whether the user opted into this specific agent.
+
+No UI ever granted `agent:<id>` consents, so the eligibility filter at
+`services/api/src/profile/eligibility.ts` dropped every agent for every real
+user. The symptom was confirmed by MLflow trace
+`tr-591449ea8a72af8e81b6a585234a86ab`: user `ODGp4Gkr7JWemMsqcMLMn` had five
+fresh `agent_outputs` rows but the orchestrator received `agent_ids: []`.
+
+## Decision
+
+Collapse to a single consent dimension: **data source**.
+
+1. `required_consents` entries must all start with `data:`. Agent manifests no
+   longer list `agent:<id>` entries.
+2. Connecting a data source via the OAuth flow automatically grants
+   `data:<provider>` in `user_consents`. Disconnecting sets `revoked_at`.
+3. `data:core` continues to be auto-granted on signup.
+4. Per-agent control becomes a **preference** (`user_preferences[scope='agent:<id>', key='enabled']`), not a consent. The eligibility filter already honours this — the only change is removing the `agent:*` consent check that was always failing.
+5. Eligibility rule (final): an agent is eligible iff every `data:*` it
+   declares is granted and not revoked, no active context is in
+   `silenced_in_contexts`, and the `enabled` preference is not `false`.
+
+## Consequences
+
+- Agents that only require `data:core` (time-of-day, momentum, recent-patterns)
+  become eligible immediately after signup.
+- Agents requiring `data:todoist` or `data:google-health` become eligible as
+  soon as the user connects the integration — no extra consent step.
+- A backfill migration grants `data:<provider>` for every existing active
+  `integration_tokens` row, unblocking users who connected before this change.
+- `ml/agents/tests/test_manifest.py` asserts all `required_consents` start
+  with `data:`, preventing regression.
--- a/infra/docker/Dockerfile.api
+++ b/infra/docker/Dockerfile.api
@@ -16,7 +16,7 @@ COPY pnpm-lock.yaml ./
 RUN --mount=type=cache,id=pnpm,target=/pnpm/store pnpm fetch
 COPY . .
 RUN --mount=type=cache,id=pnpm,target=/pnpm/store \
-    pnpm install --frozen-lockfile --offline \
+    pnpm install --frozen-lockfile \
      --filter @oo/api... --filter @oo/shared-types
 RUN pnpm --filter @oo/shared-types build
 RUN pnpm --filter @oo/api build
--- a/ml/agents/base.py
+++ b/ml/agents/base.py
@@ -20,6 +20,9 @@ class AgentInput:
    # precedence over 'inferred' source; the caller resolves priority before
    # passing this dict in.
    agent_prefs: dict = field(default_factory=dict)
+    # Pre-fetched enrichment cache: {content_hash -> description}. Populated by
+    # the TS caller from the task_enrichments DB table to avoid redundant LLM calls.
+    enrichment_cache: dict = field(default_factory=dict)


@dataclass
--- a/ml/agents/clustering.py
+++ b/ml/agents/clustering.py
@@ -1,14 +1,24 @@
-"""Semantic task clustering via nomic-embed-text (issue #97).
+"""Semantic task clustering via nomic-embed-text (issue #97, #129).

 Public API:
-    cluster_tasks(tasks, ollama_url) -> list[Cluster]
+    cluster_tasks(tasks) -> list[Cluster]

 Each task dict must have a "content" key. Tasks without content are placed in a
-fallback "other" bucket. If Ollama is unreachable, falls back to grouping by
-project_id so compute() always returns something useful.
+fallback "other" bucket. If the embedding service is unreachable, falls back to
+grouping by project_id so compute() always returns something useful.
+
+Pipeline (ported from taskpile experiments/clustering_eval, prompt v1):
+  1. Expand each raw title via LiteLLM `tip-generator` (qwen2.5:1.5b) into a
+     3-sentence description. Cached in-memory by content hash within a compute
+     cycle so duplicate titles cost one LLM call.
+  2. Prefix the expanded text with "clustering: " (nomic-embed-text task prefix).
+  3. Batch-embed via LiteLLM `embedder` (nomic-embed-text).
+  Falls back to embedding raw titles when LLM expansion fails, and to
+  project-based grouping when embeddings are unavailable.
 """
 from __future__ import annotations

+import hashlib
 import logging
 import math
 import os
@@ -22,7 +32,17 @@ log = logging.getLogger(__name__)
 _SIM_THRESHOLD = 0.72
 # Never produce more than this many clusters regardless of task count.
 _MAX_CLUSTERS = 6
-_EMBED_TIMEOUT = 10.0
+_EMBED_TIMEOUT = 15.0
+_ENRICH_TIMEOUT = 30.0
+
+_ENRICH_PROMPT_V1 = (
+    "You are helping categorize a personal task. "
+    "Write exactly 3 sentences in English describing what the task likely involves, "
+    "what context or skills it needs, and why it might matter. "
+    "Be concise and specific. Do not use bullet points or numbering.\n"
+    "Task: {title}\n"
+    "Description:"
+)


@dataclass
@@ -39,20 +59,132 @@ class Cluster:
        return sum(1 for t in self.tasks if t.get("is_overdue"))


-def _embed(text: str, ollama_url: str) -> list[float] | None:
+# ---------------------------------------------------------------------------
+# LLM enrichment
+# ---------------------------------------------------------------------------
+
+def _content_hash(text: str) -> str:
+    return hashlib.md5(text.encode()).hexdigest()
+
+
+def _enrich_title(title: str, litellm_url: str) -> str | None:
+    """Expand a terse task title into a 3-sentence description via LiteLLM."""
+    try:
+        with httpx.Client(trust_env=False, timeout=_ENRICH_TIMEOUT) as c:
+            r = c.post(
+                f"{litellm_url}/chat/completions",
+                json={
+                    "model": "tip-generator",
+                    "messages": [{"role": "user", "content": _ENRICH_PROMPT_V1.format(title=title)}],
+                    "max_tokens": 120,
+                    "temperature": 0.3,
+                },
+            )
+            r.raise_for_status()
+            return r.json()["choices"][0]["message"]["content"].strip()
+    except Exception as exc:
+        log.debug("enrich_failed title=%r error=%s", title[:40], exc)
+        return None
+
+
+def _enrich_batch(
+    titles: list[str],
+    persistent_cache: dict[str, str] | None = None,
+) -> tuple[list[str], dict[str, str]]:
+    """Return (descriptions, new_entries) for each title.
+
+    Checks persistent_cache (pre-fetched from DB) first, then falls back to
+    calling LiteLLM. new_entries contains only hashes generated this call —
+    the caller should persist these to the DB.
+    """
+    litellm_url = os.getenv("LITELLM_URL")
+    if not litellm_url:
+        log.debug("enrich_batch: no LITELLM_URL, skipping enrichment")
+        return titles, {}
+
+    db_cache = persistent_cache or {}
+    session_cache: dict[str, str] = {}  # dedup within this call
+    new_entries: dict[str, str] = {}
+    results = []
+
+    for title in titles:
+        h = _content_hash(title)
+        if h in db_cache:
+            results.append(db_cache[h])
+        elif h in session_cache:
+            results.append(session_cache[h])
+        else:
+            desc = _enrich_title(title, litellm_url)
+            value = desc if desc else title
+            session_cache[h] = value
+            if desc:  # only persist successful enrichments
+                new_entries[h] = desc
+            results.append(value)
+
+    return results, new_entries
+
+
+# ---------------------------------------------------------------------------
+# Embedding
+# ---------------------------------------------------------------------------
+
+def _embed_via_litellm(texts: list[str], litellm_url: str) -> list[list[float]] | None:
+    """Batch embed via LiteLLM OpenAI-compatible /embeddings endpoint."""
    try:
        with httpx.Client(trust_env=False, timeout=_EMBED_TIMEOUT) as c:
            r = c.post(
-                f"{ollama_url}/api/embeddings",
-                json={"model": "nomic-embed-text", "prompt": text, "keep_alive": 0},
+                f"{litellm_url}/embeddings",
+                json={"model": "embedder", "input": texts},
            )
            r.raise_for_status()
-            return r.json().get("embedding")
+            data = r.json().get("data", [])
+            ordered = sorted(data, key=lambda x: x["index"])
+            return [item["embedding"] for item in ordered]
    except Exception as exc:
-        log.debug("embed_failed text=%r error=%s", text[:40], exc)
+        log.debug("litellm_embed_failed error=%s", exc)
        return None


+def _embed_via_ollama(texts: list[str], ollama_url: str) -> list[list[float]] | None:
+    """Batch embed via Ollama /api/embed endpoint."""
+    try:
+        results = []
+        with httpx.Client(trust_env=False, timeout=_EMBED_TIMEOUT) as c:
+            for text in texts:
+                r = c.post(
+                    f"{ollama_url}/api/embed",
+                    json={"model": "nomic-embed-text", "input": text},
+                )
+                r.raise_for_status()
+                body = r.json()
+                # /api/embed returns {"embeddings": [[...]]}
+                embeddings = body.get("embeddings")
+                if not embeddings:
+                    return None
+                results.append(embeddings[0])
+        return results
+    except Exception as exc:
+        log.debug("ollama_embed_failed error=%s", exc)
+        return None
+
+
+def _embed_batch(texts: list[str]) -> list[list[float]] | None:
+    """Embed a list of texts, preferring LiteLLM over direct Ollama."""
+    litellm_url = os.getenv("LITELLM_URL")
+    if litellm_url:
+        vecs = _embed_via_litellm(texts, litellm_url)
+        if vecs is not None:
+            return vecs
+        log.info("cluster: litellm embed failed, trying ollama fallback")
+
+    ollama_url = os.getenv("OLLAMA_URL", "http://host.docker.internal:11434")
+    return _embed_via_ollama(texts, ollama_url)
+
+
+# ---------------------------------------------------------------------------
+# Clustering
+# ---------------------------------------------------------------------------
+
 def _cosine(a: list[float], b: list[float]) -> float:
    dot = sum(x * y for x, y in zip(a, b))
    na = math.sqrt(sum(x * x for x in a))
@@ -109,17 +241,18 @@ def _fallback_by_project(tasks: list[dict]) -> list[Cluster]:

 def cluster_tasks(
    tasks: list[dict],
-    ollama_url: str | None = None,
-) -> list[Cluster]:
+    ollama_url: str | None = None,  # kept for test compatibility; env vars take precedence
+    enrichment_cache: dict[str, str] | None = None,
+) -> tuple[list[Cluster], dict[str, str]]:
    """Cluster tasks by semantic similarity.

-    Returns a non-empty list of Cluster objects. Falls back to project-based
-    grouping if Ollama is unavailable or tasks have no content.
+    Returns (clusters, new_enrichments). new_enrichments contains LLM-generated
+    descriptions produced this call that were not in the persistent cache — the
+    caller should persist these. Falls back to project-based grouping if the
+    embedding service is unavailable or tasks have no content.
    """
    if not tasks:
-        return []
-
-    url = ollama_url or os.getenv("OLLAMA_URL", "http://host.docker.internal:11434")
+        return [], {}

    # Separate tasks with usable content from those without.
    with_content = [(t, t.get("content", "").strip()) for t in tasks]
@@ -127,26 +260,31 @@ def cluster_tasks(
    no_content = [t for t, c in with_content if not c]

    if not embeddable:
-        return _fallback_by_project(tasks)
+        return _fallback_by_project(tasks), {}

-    # Fetch embeddings (best-effort; None means Ollama unavailable).
-    embedded: list[tuple[dict, list[float]]] = []
-    failed = False
-    for task, content in embeddable:
-        vec = _embed(content, url)
-        if vec is None:
-            failed = True
-            break
-        embedded.append((task, vec))
+    task_objs = [t for t, _ in embeddable]
+    raw_titles = [c for _, c in embeddable]

-    if failed or not embedded:
-        log.info("cluster_tasks: ollama unavailable, falling back to project grouping")
-        return _fallback_by_project(tasks)
+    # Step 1: LLM-enrich titles → richer semantic signal before embedding.
+    descriptions, new_enrichments = _enrich_batch(raw_titles, persistent_cache=enrichment_cache)

+    # Attach enriched description to each task dict so consumers (e.g. focus-area)
+    # can show the expanded text instead of the terse raw title.
+    for task, desc in zip(task_objs, descriptions):
+        task["enriched_description"] = desc
+
+    # Step 2: Prefix with nomic-embed-text task prefix, then batch-embed.
+    prefixed = [f"clustering: {d}" for d in descriptions]
+    vecs = _embed_batch(prefixed)
+
+    if vecs is None or len(vecs) != len(prefixed):
+        log.info("cluster_tasks: embedding unavailable, falling back to project grouping")
+        return _fallback_by_project(tasks), new_enrichments
+
+    embedded = list(zip(task_objs, vecs))
    clusters = _greedy_cluster(embedded)

-    # Tasks without content get their own bucket if any.
    if no_content:
        clusters.append(Cluster(label="Other tasks", tasks=no_content))

-    return clusters
+    return clusters, new_enrichments
--- a/ml/agents/focus_area.py
+++ b/ml/agents/focus_area.py
@@ -1,113 +1,70 @@
 from __future__ import annotations

-from collections import Counter
 from typing import ClassVar

 from .base import BaseAgent, AgentInput, AgentOutput
 from .clustering import cluster_tasks
-from .inference.history import UserHistory
-from .manifest import AgentManifest, InferredParam
-
-
-def _infer_preferred_areas(history: UserHistory) -> list[str]:
-    """Top-2 project IDs by completed task count (last 90 days worth of data)."""
-    counts: Counter[str] = Counter()
-    for tc in history.task_completions:
-        if tc.project_id:
-            counts[tc.project_id] += 1
-    return [pid for pid, _ in counts.most_common(2)]
+from .manifest import AgentManifest


 MANIFEST = AgentManifest(
    id="focus-area",
-    version="2.0.0",  # semantic clustering via nomic-embed-text (#97, #113)
-    description="Identifies the most congested semantic focus area in the user's task list.",
-    pref_schema={
-        "type": "object",
-        "additionalProperties": False,
-        "properties": {
-            "preferred_areas": {
-                "type": "array",
-                "items": {"type": "string"},
-                "default": [],
-                "description": "Project IDs or label names to prioritise when multiple areas tie.",
-            },
-        },
-    },
+    version="3.0.0",  # output all clusters as context; no scoring (#129)
+    description="Clusters tasks semantically, enriches titles via LLM, and outputs a full area summary with expanded descriptions for the orchestrator.",
+    pref_schema={"type": "object", "additionalProperties": False, "properties": {}},
    context_schema=["todoist.tasks"],
-    required_consents=["data:core", "data:todoist", "agent:focus-area"],
+    required_consents=["data:core", "data:todoist"],
    output_contract={"type": "snippet", "format": "free_text"},
-    ttl_sec=43_200,
-    inferred_params=[
-        InferredParam(
-            key="preferred_areas",
-            ttl_sec=86_400,
-            cold_start_default=[],
-            min_history=0,   # use task_completions, not feedback events; handle empty inside
-            infer=_infer_preferred_areas,
-        ),
-    ],
+    ttl_sec=86_400,
+    inferred_params=[],
 )


 class FocusAreaAgent(BaseAgent):
-    """Identifies the most congested semantic focus area in the user's task list."""
+    """Clusters tasks and outputs a full area summary for the orchestrator."""
    agent_id: ClassVar[str] = MANIFEST.id
    ttl_seconds: ClassVar[int] = MANIFEST.ttl_sec
-    version: ClassVar[str] = MANIFEST.version
+    version: ClassVar[str] = MANIFEST.version  # 3.0.0

    def compute(self, inp: AgentInput) -> AgentOutput:
-        preferred: list[str] = inp.agent_prefs.get("preferred_areas", [])
-
        if not inp.tasks:
            return self._make_output(
                inp,
-                "No tasks available to identify a focus area.",
-                {"cluster_count": 0, "strategy": "none"},
+                "No tasks available to identify focus areas.",
+                {"cluster_count": 0},
            )

-        clusters = cluster_tasks(inp.tasks)
+        clusters, new_enrichments = cluster_tasks(inp.tasks, enrichment_cache=inp.enrichment_cache)

        if not clusters:
            return self._make_output(
                inp,
-                "No tasks available to identify a focus area.",
-                {"cluster_count": 0, "strategy": "none"},
+                "No tasks available to identify focus areas.",
+                {"cluster_count": 0},
            )

-        strategy = "semantic" if len(clusters) > 1 or len(inp.tasks) > 1 else "fallback"
+        lines = [f"The user's tasks are grouped into {len(clusters)} area(s):"]
+        for i, cluster in enumerate(clusters, 1):
+            descs = [
+                t.get("enriched_description") or t.get("content", "")
+                for t in cluster.tasks
+                if t.get("content")
+            ]
+            descs = [d.strip() for d in descs if d.strip()]
+            descs_str = "; ".join(f'"{d}"' for d in descs[:8])
+            if len(descs) > 8:
+                descs_str += f" (and {len(descs) - 8} more)"
+            lines.append(f"{i}. {cluster.label} — {cluster.task_count} task(s): {descs_str}")

-        def score(cluster) -> float:
-            base = sum(2.0 if t.get("is_overdue") else 1.0 for t in cluster.tasks)
-            boosted = any(p in cluster.label for p in preferred) if preferred else False
-            return base + (0.5 if boosted else 0.0)
-
-        top = max(clusters, key=score)
-        boosted = bool(preferred) and any(p in top.label for p in preferred)
-
-        parts = [
-            f'The user\'s most active focus area is "{top.label}" '
-            f"({top.task_count} task{'s' if top.task_count != 1 else ''}, "
-            f"{top.overdue_count} overdue). "
-            f"(Note: task titles may be in any language — always write the tip in English.)"
-        ]
-        if boosted:
-            parts.append("This area matches the user's stated focus preferences.")
-        if top.overdue_count >= 3:
-            parts.append("Consider surfacing an action from this area.")
-        if len(clusters) > 1:
-            other_total = sum(c.task_count for c in clusters if c is not top)
-            parts.append(
-                f"{len(clusters) - 1} other area{'s' if len(clusters) > 2 else ''} "
-                f"contain {other_total} task{'s' if other_total != 1 else ''}."
-            )
+        lines.append("(Task titles may be in any language — always write the tip in English.)")

        snapshot = {
-            "top_cluster_label": top.label,
-            "top_task_count": top.task_count,
-            "top_overdue_count": top.overdue_count,
            "cluster_count": len(clusters),
-            "strategy": strategy,
-            "preferred_areas": preferred,
+            "clusters": [
+                {"label": c.label, "task_count": c.task_count,
+                 "tasks": [t.get("content", "") for t in c.tasks]}
+                for c in clusters
+            ],
+            "_new_enrichments": new_enrichments,
        }
-        return self._make_output(inp, " ".join(parts), snapshot)
+        return self._make_output(inp, "\n".join(lines), snapshot)
--- a/ml/agents/health_vitals.py
+++ b/ml/agents/health_vitals.py
@@ -0,0 +1,134 @@
+from __future__ import annotations
+
+from typing import ClassVar
+
+from .base import BaseAgent, AgentInput, AgentOutput
+from .manifest import AgentManifest, InferredParam
+from .inference.history import UserHistory
+
+
+def _infer_step_goal(history: UserHistory) -> int:
+    """Return median daily step count as the personal goal baseline (min 1000)."""
+    if not history.task_completions:
+        return 7_000
+    # task_completions reused as a generic history mechanism here;
+    # step history arrives via agent_prefs.step_history when available.
+    return 7_000
+
+
+MANIFEST = AgentManifest(
+    id="health-vitals",
+    version="1.0.0",
+    description="Summarises today's health signals: steps, sleep, activity, and heart rate.",
+    pref_schema={
+        "type": "object",
+        "additionalProperties": False,
+        "properties": {
+            "step_goal": {
+                "type": "integer",
+                "minimum": 1000,
+                "default": 7000,
+                "description": "Daily step goal.",
+            },
+            "sleep_goal_hours": {
+                "type": "number",
+                "minimum": 4,
+                "maximum": 12,
+                "default": 7,
+                "description": "Target sleep duration in hours.",
+            },
+        },
+    },
+    context_schema=["google-health.steps", "google-health.sleep", "google-health.activity", "google-health.heart_rate"],
+    required_consents=["data:core", "data:google-health"],
+    output_contract={"type": "snippet", "format": "free_text"},
+    ttl_sec=1800,  # refresh every 30 min — health data changes during the day
+    silenced_in_contexts=[],
+    inferred_params=[
+        InferredParam(
+            key="step_goal",
+            ttl_sec=7 * 86_400,
+            cold_start_default=7000,
+            min_history=0,
+            infer=lambda h: 7000,  # static default; override via user pref
+        ),
+    ],
+)
+
+
+class HealthVitalsAgent(BaseAgent):
+    """Summarises today's health signals into an orchestrator 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:
+        step_goal = int(inp.agent_prefs.get("step_goal", 7000))
+        sleep_goal = float(inp.agent_prefs.get("sleep_goal_hours", 7.0))
+
+        health = [t for t in inp.tasks if t.get("source") == "google-health"]
+
+        if not health:
+            prompt = "No health data available from Google Fit today. (Always write the tip in English.)"
+            return self._make_output(inp, prompt, {"no_data": True})
+
+        steps_sig = next((t for t in health if str(t.get("id", "")).endswith(":steps")), None)
+        sleep_sig = next((t for t in health if str(t.get("id", "")).endswith(":sleep")), None)
+        activity_sig = next((t for t in health if str(t.get("id", "")).endswith(":activity")), None)
+        hr_sig = next((t for t in health if str(t.get("id", "")).endswith(":heart_rate")), None)
+
+        insights: list[str] = []
+        snapshot: dict = {}
+
+        if steps_sig is not None:
+            steps = int(steps_sig.get("step_count", 0))
+            pct = round(steps / step_goal * 100) if step_goal else 0
+            snapshot["step_count"] = steps
+            snapshot["step_goal_pct"] = pct
+            if pct < 30:
+                insights.append(f"only {steps:,} steps today ({pct}% of {step_goal:,} goal — significantly behind)")
+            elif pct < 60:
+                insights.append(f"{steps:,} steps today ({pct}% of {step_goal:,} goal)")
+            elif pct >= 100:
+                insights.append(f"{steps:,} steps today (daily goal reached!)")
+            else:
+                insights.append(f"{steps:,} steps today ({pct}% of goal)")
+
+        if sleep_sig is not None:
+            hours = float(sleep_sig.get("sleep_hours", 0))
+            deficit = max(0.0, sleep_goal - hours)
+            snapshot["sleep_hours"] = hours
+            snapshot["sleep_deficit_hours"] = deficit
+            if deficit >= 1.5:
+                insights.append(f"only {hours:.1f}h sleep last night ({deficit:.1f}h below the {sleep_goal:.0f}h goal)")
+            elif deficit > 0:
+                insights.append(f"{hours:.1f}h sleep last night (slightly below {sleep_goal:.0f}h goal)")
+            else:
+                insights.append(f"{hours:.1f}h sleep last night (goal met)")
+
+        if activity_sig is not None:
+            active_mins = int(activity_sig.get("active_minutes", 0))
+            calories = int(activity_sig.get("calories_burned", 0))
+            snapshot["active_minutes"] = active_mins
+            snapshot["calories_burned"] = calories
+            if active_mins < 10:
+                insights.append(f"only {active_mins} active minutes today — largely sedentary")
+            elif active_mins >= 30:
+                insights.append(f"{active_mins} active minutes and {calories} kcal burned today")
+
+        if hr_sig is not None:
+            bpm = int(hr_sig.get("resting_bpm", 0))
+            snapshot["resting_bpm"] = bpm
+            if bpm > 90:
+                insights.append(f"elevated resting heart rate: {bpm} bpm")
+            elif bpm > 0:
+                insights.append(f"resting heart rate: {bpm} bpm")
+
+        if not insights:
+            prompt = "Health data is available but no notable signals today. (Always write the tip in English.)"
+        else:
+            body = "; ".join(insights)
+            prompt = f"Health snapshot: {body}. (Always write the tip in English.)"
+
+        return self._make_output(inp, prompt, snapshot)
--- a/ml/agents/momentum.py
+++ b/ml/agents/momentum.py
@@ -121,7 +121,7 @@ MANIFEST = AgentManifest(
        },
    },
    context_schema=["profile.features"],
-    required_consents=["data:core", "agent:momentum"],
+    required_consents=["data:core"],
    output_contract={"type": "snippet", "format": "free_text"},
    ttl_sec=21_600,
    inferred_params=[
--- a/ml/agents/overdue_task.py
+++ b/ml/agents/overdue_task.py
@@ -70,7 +70,7 @@ MANIFEST = AgentManifest(
        },
    },
    context_schema=["todoist.tasks"],
-    required_consents=["data:core", "data:todoist", "agent:overdue-task"],
+    required_consents=["data:core", "data:todoist"],
    output_contract={"type": "snippet", "format": "free_text"},
    ttl_sec=3600,
    silenced_in_contexts=["vacation"],
--- a/ml/agents/recent_patterns.py
+++ b/ml/agents/recent_patterns.py
@@ -131,7 +131,7 @@ MANIFEST = AgentManifest(
        },
    },
    context_schema=["tip_feedback", "profile.features"],
-    required_consents=["data:core", "agent:recent-patterns"],
+    required_consents=["data:core"],
    output_contract={"type": "snippet", "format": "free_text"},
    ttl_sec=86_400,
    inferred_params=[
--- a/ml/agents/registry.py
+++ b/ml/agents/registry.py
@@ -16,6 +16,7 @@ from .momentum import MomentumAgent, MANIFEST as MOMENTUM_MANIFEST
 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

 _REGISTERED: list[tuple[BaseAgent, AgentManifest]] = [
    (OverdueTaskAgent(), OVERDUE_TASK_MANIFEST),
@@ -23,6 +24,7 @@ _REGISTERED: list[tuple[BaseAgent, AgentManifest]] = [
    (TimeOfDayAgent(), TIME_OF_DAY_MANIFEST),
    (RecentPatternsAgent(), RECENT_PATTERNS_MANIFEST),
    (FocusAreaAgent(), FOCUS_AREA_MANIFEST),
+    (HealthVitalsAgent(), HEALTH_VITALS_MANIFEST),
 ]

 # Sanity check — agent_id and manifest.id must agree, otherwise the registry
--- a/ml/agents/tests/test_agents.py
+++ b/ml/agents/tests/test_agents.py
@@ -213,40 +213,41 @@ class TestFocusAreaAgent:
        out = self.agent.compute(_inp())
        assert "no tasks" in out.prompt_text.lower()

-    def test_single_project(self):
-        tasks = [_task(f"T{i}", project_id="Work") for i in range(3)]
-        out = self.agent.compute(_inp(tasks=tasks))
-        assert '"Work"' in out.prompt_text
-        assert "3 tasks" in out.prompt_text
-
-    def test_most_congested_wins(self):
+    def test_lists_all_clusters(self):
        tasks = (
-            [_task(f"W{i}", project_id="Work") for i in range(5)]
+            [_task(f"W{i}", project_id="Work") for i in range(3)]
            + [_task(f"H{i}", project_id="Home") for i in range(2)]
        )
        out = self.agent.compute(_inp(tasks=tasks))
-        assert '"Work"' in out.prompt_text
+        assert "Work" in out.prompt_text
+        assert "Home" in out.prompt_text

-    def test_overdue_weighting(self):
-        # Home has 2 tasks (1 overdue), Work has 3 non-overdue tasks
-        # Home score = 2+1 = 3; Work score = 3 — Home should win due to overdue weight
-        tasks = (
-            [_task("Home1", project_id="Home", is_overdue=True),
-             _task("Home2", project_id="Home")]
-            + [_task(f"W{i}", project_id="Work") for i in range(3)]
-        )
+    def test_includes_task_titles(self):
+        tasks = [_task("Buy milk", project_id="Personal"), _task("Write report", project_id="Personal")]
        out = self.agent.compute(_inp(tasks=tasks))
-        assert '"Work"' not in out.prompt_text or '"Home"' in out.prompt_text
+        assert '"Buy milk"' in out.prompt_text
+        assert '"Write report"' in out.prompt_text
+
+    def test_task_count_in_output(self):
+        tasks = [_task(f"T{i}", project_id="Work") for i in range(3)]
+        out = self.agent.compute(_inp(tasks=tasks))
+        assert "3 task" in out.prompt_text

    def test_default_project_fallback(self):
        out = self.agent.compute(_inp(tasks=[_task("No project task")]))
-        # Tasks without project_id fall back to a "Tasks" bucket
        assert "Tasks" in out.prompt_text

    def test_snapshot_keys(self):
        out = self.agent.compute(_inp(tasks=[_task("T1", project_id="A")]))
-        assert {"top_cluster_label", "top_task_count", "top_overdue_count", "cluster_count",
-                "strategy", "preferred_areas"} == set(out.signals_snapshot)
+        public_keys = {k for k in out.signals_snapshot if not k.startswith("_")}
+        assert {"cluster_count", "clusters"} == public_keys
+
+    def test_snapshot_clusters_shape(self):
+        tasks = [_task("Buy milk", project_id="P1"), _task("Fix bug", project_id="P2")]
+        out = self.agent.compute(_inp(tasks=tasks))
+        clusters = out.signals_snapshot["clusters"]
+        assert isinstance(clusters, list)
+        assert all("label" in c and "task_count" in c and "tasks" in c for c in clusters)


 # ── Registry ─────────────────────────────────────────────────────────────────
@@ -255,7 +256,7 @@ 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"}
+        assert ids == {"overdue-task", "momentum", "time-of-day", "recent-patterns", "focus-area", "health-vitals"}

    def test_get_agent(self):
        a = get_agent("momentum")
--- a/ml/agents/tests/test_clustering.py
+++ b/ml/agents/tests/test_clustering.py
@@ -1,6 +1,6 @@
-"""Unit tests for ml.agents.clustering (issue #97).
+"""Unit tests for ml.agents.clustering (issue #97, #129).

-Embedding calls are mocked so tests run without Ollama.
+LLM and embedding calls are mocked so tests run without Ollama or LiteLLM.
 """
 from __future__ import annotations

@@ -9,7 +9,7 @@ sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "..", ".."))

 from unittest.mock import patch

-from ml.agents.clustering import cluster_tasks, Cluster, _greedy_cluster, _cosine
+from ml.agents.clustering import cluster_tasks, Cluster, _greedy_cluster, _cosine, _embed_batch, _enrich_batch


 # ── helpers ──────────────────────────────────────────────────────────────────
@@ -82,54 +82,128 @@ class TestGreedyClustering:
        assert clusters[0].label == "Write report"


+# ── enrichment ───────────────────────────────────────────────────────────────
+
+class TestEnrichBatch:
+    def test_falls_back_to_raw_when_no_litellm_url(self, monkeypatch):
+        monkeypatch.delenv("LITELLM_URL", raising=False)
+        result, new = _enrich_batch(["Buy milk", "Fix bug"])
+        assert result == ["Buy milk", "Fix bug"] and new == {}
+
+    def test_uses_description_when_litellm_available(self, monkeypatch):
+        monkeypatch.setenv("LITELLM_URL", "http://fake-litellm")
+        with patch("ml.agents.clustering._enrich_title", return_value="Expanded description."):
+            result, new = _enrich_batch(["Buy milk"])
+        assert result == ["Expanded description."]
+        assert len(new) == 1
+
+    def test_falls_back_to_raw_title_on_enrich_failure(self, monkeypatch):
+        monkeypatch.setenv("LITELLM_URL", "http://fake-litellm")
+        with patch("ml.agents.clustering._enrich_title", return_value=None):
+            result, new = _enrich_batch(["Buy milk"])
+        assert result == ["Buy milk"]
+        assert new == {}  # failed enrichments are not persisted
+
+    def test_deduplicates_identical_titles(self, monkeypatch):
+        monkeypatch.setenv("LITELLM_URL", "http://fake-litellm")
+        call_count = {"n": 0}
+        def fake_enrich(title, url):
+            call_count["n"] += 1
+            return f"desc:{title}"
+        with patch("ml.agents.clustering._enrich_title", side_effect=fake_enrich):
+            result, new = _enrich_batch(["Buy milk", "Buy milk", "Fix bug"])
+        assert call_count["n"] == 2  # only 2 unique titles
+        assert result == ["desc:Buy milk", "desc:Buy milk", "desc:Fix bug"]
+
+    def test_uses_persistent_cache(self, monkeypatch):
+        monkeypatch.setenv("LITELLM_URL", "http://fake-litellm")
+        from ml.agents.clustering import _content_hash
+        h = _content_hash("Buy milk")
+        call_count = {"n": 0}
+        def fake_enrich(title, url):
+            call_count["n"] += 1
+            return "new desc"
+        with patch("ml.agents.clustering._enrich_title", side_effect=fake_enrich):
+            result, new = _enrich_batch(["Buy milk"], persistent_cache={h: "cached desc"})
+        assert call_count["n"] == 0  # cache hit, no LLM call
+        assert result == ["cached desc"]
+        assert new == {}
+
+
 # ── cluster_tasks integration ─────────────────────────────────────────────────

 class TestClusterTasks:
-    def test_empty_tasks(self):
-        result = cluster_tasks([])
-        assert result == []
+    def _no_enrich(self, titles, persistent_cache=None):
+        return titles, {}

-    def test_fallback_when_ollama_unavailable(self):
-        with patch("ml.agents.clustering._embed", return_value=None):
+    def test_empty_tasks(self):
+        clusters, new = cluster_tasks([])
+        assert clusters == [] and new == {}
+
+    def test_fallback_when_embed_unavailable(self):
+        with patch("ml.agents.clustering._enrich_batch", side_effect=self._no_enrich), \
+             patch("ml.agents.clustering._embed_batch", return_value=None):
            tasks = [_task("A", "p1"), _task("B", "p2"), _task("C", "p1")]
-            clusters = cluster_tasks(tasks)
+            clusters, _ = cluster_tasks(tasks)
        assert len(clusters) == 2
        labels = {c.label for c in clusters}
        assert "p1" in labels and "p2" in labels

    def test_fallback_groups_by_project(self):
-        with patch("ml.agents.clustering._embed", return_value=None):
+        with patch("ml.agents.clustering._enrich_batch", side_effect=self._no_enrich), \
+             patch("ml.agents.clustering._embed_batch", return_value=None):
            tasks = [_task("A", "work")] * 3 + [_task("B", "home")] * 2
-            clusters = cluster_tasks(tasks)
+            clusters, _ = cluster_tasks(tasks)
        by_label = {c.label: c.task_count for c in clusters}
        assert by_label["work"] == 3
        assert by_label["home"] == 2

    def test_tasks_without_content_go_to_other(self):
        v = [1.0, 0.0]
-        with patch("ml.agents.clustering._embed", return_value=v):
+        with patch("ml.agents.clustering._enrich_batch", side_effect=self._no_enrich), \
+             patch("ml.agents.clustering._embed_batch", return_value=[v]):
            tasks = [_task("Has content"), {"is_overdue": False}]
-            clusters = cluster_tasks(tasks)
+            clusters, _ = cluster_tasks(tasks)
        labels = {c.label for c in clusters}
        assert "Other tasks" in labels

    def test_semantic_clustering_groups_similar(self):
        v_work = [1.0, 0.0, 0.0]
        v_home = [0.0, 1.0, 0.0]
-        side_effects = [v_work, v_work, v_home, v_home]
-        with patch("ml.agents.clustering._embed", side_effect=side_effects):
+        batch_result = [v_work, v_work, v_home, v_home]
+        with patch("ml.agents.clustering._enrich_batch", side_effect=self._no_enrich), \
+             patch("ml.agents.clustering._embed_batch", return_value=batch_result):
            tasks = [
                _task("Write report"),
                _task("Review PR"),
                _task("Buy groceries"),
                _task("Cook dinner"),
            ]
-            clusters = cluster_tasks(tasks)
+            clusters, _ = cluster_tasks(tasks)
        assert len(clusters) == 2
        assert all(c.task_count == 2 for c in clusters)

    def test_all_tasks_no_content_fallback_by_project(self):
        tasks = [{"project_id": "p1", "is_overdue": False},
                 {"project_id": "p2", "is_overdue": False}]
-        clusters = cluster_tasks(tasks)
-        assert len(clusters) == 2
+        clusters, new = cluster_tasks(tasks)
+        assert len(clusters) == 2 and new == {}
+
+    def test_enrich_called_before_embed(self):
+        """Verify enrichment output (not raw title) is what gets embedded."""
+        v = [1.0, 0.0]
+        captured = {}
+        def fake_embed(texts):
+            captured["texts"] = texts
+            return [v] * len(texts)
+        with patch("ml.agents.clustering._enrich_batch", return_value=(["Expanded desc."], {})), \
+             patch("ml.agents.clustering._embed_batch", side_effect=fake_embed):
+            cluster_tasks([_task("Buy milk")])
+        assert captured["texts"] == ["clustering: Expanded desc."]
+
+    def test_new_enrichments_returned(self):
+        v = [1.0, 0.0]
+        with patch("ml.agents.clustering._enrich_batch", return_value=(["desc"], {"abc123": "desc"})), \
+             patch("ml.agents.clustering._embed_batch", return_value=[v]):
+            _, new = cluster_tasks([_task("Buy milk")])
+        assert new == {"abc123": "desc"}
--- a/ml/agents/tests/test_manifest.py
+++ b/ml/agents/tests/test_manifest.py
@@ -45,6 +45,7 @@ def test_manifest_required_fields(agent_id: str):
    assert isinstance(m.pref_schema, dict) and m.pref_schema.get("type") == "object"
    assert isinstance(m.required_consents, list) and m.required_consents
    assert "data:core" in m.required_consents, "every agent should require data:core"
+    assert all(c.startswith("data:") for c in m.required_consents), "only data: consents allowed; agent: consents have been removed"
    assert m.ttl_sec == get_agent(agent_id).ttl_seconds, "ttl divergence"


--- a/ml/agents/tests/test_per_agent_inference.py
+++ b/ml/agents/tests/test_per_agent_inference.py
@@ -627,86 +627,37 @@ class TestTimeOfDaySnippet:
        assert {"quiet_start", "quiet_end", "peak_hours", "tz"}.issubset(keys)


-# ── focus-area: preferred_areas wiring ───────────────────────────────────────
+# ── focus-area: cluster summary output ───────────────────────────────────────

-class TestFocusAreaPreferredAreas:
+class TestFocusAreaOutput:
    agent = FocusAreaAgent()

-    def _task(self, content: str, project_id: str, is_overdue: bool = False) -> dict:
-        return {"id": "t1", "content": content, "is_overdue": is_overdue,
+    def _task(self, content: str, project_id: str) -> dict:
+        return {"id": "t1", "content": content, "is_overdue": False,
                "task_age_days": 2.0, "priority": 1, "project_id": project_id}

-    def test_preferred_area_wins_tie(self):
-        tasks = [
-            self._task("Work thing", "work"),
-            self._task("Home thing", "home"),
-        ]
-        out = self.agent.compute(_inp(tasks=tasks, agent_prefs={"preferred_areas": ["work"]}))
-        assert "work" in out.prompt_text
-        assert "matches the user's stated focus preferences" in out.prompt_text
-
-    def test_no_preferred_areas_uses_congestion_score(self):
-        tasks = [
-            self._task("W1", "work"),
-            self._task("H1", "home"),
-            self._task("H2", "home"),
-        ]
-        out = self.agent.compute(_inp(tasks=tasks))
-        # home has more tasks → wins without any preference
-        assert "home" in out.prompt_text
-
-    def test_snapshot_includes_preferred_areas(self):
-        tasks = [self._task("T", "work")]
-        out = self.agent.compute(_inp(tasks=tasks, agent_prefs={"preferred_areas": ["work"]}))
-        assert out.signals_snapshot["preferred_areas"] == ["work"]
-
-    def test_version_bumped(self):
+    def test_version(self):
        from ml.agents.focus_area import MANIFEST as FA_MANIFEST
-        assert FA_MANIFEST.version == "2.0.0"
+        assert FA_MANIFEST.version == "3.0.0"

-    def test_snapshot_uses_cluster_keys(self):
+    def test_all_clusters_in_output(self):
+        tasks = [self._task("Work thing", "work"), self._task("Home thing", "home")]
+        out = self.agent.compute(_inp(tasks=tasks))
+        assert "work" in out.prompt_text.lower()
+        assert "home" in out.prompt_text.lower()
+
+    def test_task_titles_in_output(self):
+        tasks = [self._task("Buy milk", "personal")]
+        out = self.agent.compute(_inp(tasks=tasks))
+        assert '"Buy milk"' in out.prompt_text
+
+    def test_snapshot_shape(self):
        tasks = [self._task("T", "work")]
        out = self.agent.compute(_inp(tasks=tasks))
-        assert "top_cluster_label" in out.signals_snapshot
-        assert "cluster_count" in out.signals_snapshot
-        assert "strategy" in out.signals_snapshot
+        public_keys = {k for k in out.signals_snapshot if not k.startswith("_")}
+        assert public_keys == {"cluster_count", "clusters"}
+        assert isinstance(out.signals_snapshot["clusters"], list)

-
-# ── focus-area: preferred_areas inference from task_completions (#113) ────────
-
-class TestFocusAreaPreferredAreasInference:
-    from ml.agents.focus_area import MANIFEST as _FA_MANIFEST
-
-    def _completion(self, project_id: str) -> TaskCompletion:
-        return _completion(project_id, lateness_days=0.0)
-
-    def test_cold_start_no_completions(self):
-        history = _history(completions=[])
+    def test_no_inferred_params(self):
        from ml.agents.focus_area import MANIFEST as FA_MANIFEST
-        result = run_inference(FA_MANIFEST, history)
-        assert result["preferred_areas"] == []
-
-    def test_top_two_projects_returned(self):
-        completions = (
-            [_completion("p1", 0)] * 8
-            + [_completion("p2", 0)] * 5
-            + [_completion("p3", 0)] * 2
-        )
-        history = _history(completions=completions)
-        from ml.agents.focus_area import MANIFEST as FA_MANIFEST
-        result = run_inference(FA_MANIFEST, history)
-        assert result["preferred_areas"] == ["p1", "p2"]
-
-    def test_single_project_returns_one(self):
-        completions = [_completion("work", 0)] * 6
-        history = _history(completions=completions)
-        from ml.agents.focus_area import MANIFEST as FA_MANIFEST
-        result = run_inference(FA_MANIFEST, history)
-        assert result["preferred_areas"] == ["work"]
-
-    def test_none_project_id_ignored(self):
-        completions = [_completion(None, 0)] * 5 + [_completion("real", 0)] * 3
-        history = _history(completions=completions)
-        from ml.agents.focus_area import MANIFEST as FA_MANIFEST
-        result = run_inference(FA_MANIFEST, history)
-        assert result["preferred_areas"] == ["real"]
+        assert FA_MANIFEST.inferred_params == []
--- a/ml/agents/time_of_day.py
+++ b/ml/agents/time_of_day.py
@@ -126,7 +126,7 @@ MANIFEST = AgentManifest(
        },
    },
    context_schema=["profile.features"],
-    required_consents=["data:core", "agent:time-of-day"],
+    required_consents=["data:core"],
    output_contract={"type": "snippet", "format": "free_text"},
    ttl_sec=900,
    inferred_params=[
--- a/ml/serving/main.py
+++ b/ml/serving/main.py
@@ -196,6 +196,12 @@ class AgentComputeRequest(BaseModel):
    now_iso: Optional[str] = None  # ISO 8601; defaults to utcnow
    # Per-agent prefs from user_preferences (merged: user source overrides inferred).
    agent_prefs: dict = {}
+    # Pre-fetched enrichment cache: {content_hash -> description}. Avoids re-calling
+    # LiteLLM for task titles already expanded in a prior compute cycle.
+    enrichment_cache: dict[str, str] = {}
+    # MD5 of sorted task contents; stored in snapshot so the next cycle can skip
+    # recompute when the task list hasn't changed.
+    task_hash: Optional[str] = None


 class AgentComputeResponse(BaseModel):
@@ -206,6 +212,8 @@ class AgentComputeResponse(BaseModel):
    computed_at: str
    expires_at: str
    agent_version: str
+    # New enrichments generated during this compute cycle; caller persists to DB.
+    new_enrichments: dict[str, str] = {}


 class AgentInferRequest(BaseModel):
@@ -233,6 +241,7 @@ class RecommendRequest(BaseModel):
    hour_of_day: int = 12
    day_of_week: int = 0
    science_destiny: int = 50  # 0=science (data-driven), 100=destiny (intuitive)
+    recent_tip: Optional[str] = None  # content of last snoozed tip; LLM avoids repeating it


 class TipResult(BaseModel):
@@ -313,6 +322,7 @@ async def compute_agent(agent_id: str, req: AgentComputeRequest) -> AgentCompute
        feedback_history=req.feedback_history,
        now=now,
        agent_prefs=req.agent_prefs,
+        enrichment_cache=req.enrichment_cache,
    )
    try:
        output = agent.compute(inp)
@@ -320,6 +330,10 @@ async def compute_agent(agent_id: str, req: AgentComputeRequest) -> AgentCompute
        log.error("agent_compute_failed", agent_id=agent_id, user_id=req.user_id, error=str(exc))
        raise HTTPException(status_code=500, detail=f"Agent compute failed: {exc}")

+    if req.task_hash:
+        output.signals_snapshot["_task_hash"] = req.task_hash
+    new_enrichments: dict[str, str] = output.signals_snapshot.pop("_new_enrichments", {})
+
    log.info("agent_computed", agent_id=agent_id, user_id=req.user_id, expires_at=output.expires_at)
    span = _start_span(
        f"compute:{agent_id}",
@@ -338,6 +352,7 @@ async def compute_agent(agent_id: str, req: AgentComputeRequest) -> AgentCompute
        computed_at=output.computed_at,
        expires_at=output.expires_at,
        agent_version=output.agent_version,
+        new_enrichments=new_enrichments,
    )


@@ -430,6 +445,7 @@ async def recommend(req: RecommendRequest) -> RecommendResponse:
            hour_of_day=req.hour_of_day,
            day_of_week=req.day_of_week,
            science_destiny=req.science_destiny,
+            recent_tip=req.recent_tip,
        )
        _end_span(ctx_span, outputs={"message_count": len(messages)})

--- a/ml/serving/prompts.py
+++ b/ml/serving/prompts.py
@@ -161,16 +161,21 @@ def build_orchestrator_messages(
    hour_of_day: int,
    day_of_week: int,
    science_destiny: int = 50,
+    recent_tip: str | None = None,
 ) -> list[dict]:
    """Build the [system, user] message list for the orchestrator LLM call.

    agent_outputs: list of {agent_id, prompt_text} dicts.
    Falls back to raw task summary when agent_outputs is empty.
+    recent_tip: content of a tip the user just snoozed — generate something different.
    """
    style_hint = _science_destiny_instruction(science_destiny)
    system = _SYS_V4_ORCHESTRATOR + (f"\n\n{style_hint}" if style_hint else "")

    lines = [f"Current time: {hour_of_day:02d}:00, day_of_week={day_of_week}", ""]
+    if recent_tip:
+        lines.append(f"The user snoozed this tip (do NOT repeat it or anything similar): \"{recent_tip}\"")
+        lines.append("")
    if agent_outputs:
        lines.append("Context from analysis agents:")
        for s in agent_outputs:
--- a/packages/shared-types/src/http/integrations.ts
+++ b/packages/shared-types/src/http/integrations.ts
@@ -1,4 +1,4 @@
-export type IntegrationProvider = 'todoist';
+export type IntegrationProvider = 'todoist' | 'google-health';
 export type IntegrationStatus = 'connected' | 'disconnected' | 'error';

 export interface Integration {
--- a/packages/shared-types/src/http/signal.ts
+++ b/packages/shared-types/src/http/signal.ts
@@ -2,7 +2,7 @@
 export interface Signal {
  id: string;
  source: string;                             // e.g. 'todoist', 'google-calendar', 'manual'
-  kind: 'task' | 'event' | 'habit' | 'insight';
+  kind: 'task' | 'event' | 'habit' | 'insight' | 'health';
  content: string;
  metadata: Record<string, unknown>;          // source-specific raw fields
  features: Record<string, number | boolean>; // bandit-ready numeric/boolean features
--- a/services/api/src/db/tests/migrations.test.ts
+++ b/services/api/src/db/tests/migrations.test.ts
@@ -85,3 +85,45 @@ describe('runMigrations — idempotency', () => {
  });
 });

+describe('runMigrations — issue #127 backfill', () => {
+  it('grants data:<provider> consent for existing active integration tokens', () => {
+    const sqlite = freshDb();
+    runMigrations(sqlite);
+
+    // Seed a user + active Todoist token (simulates pre-#127 state)
+    sqlite.exec(`
+      INSERT INTO users (id, email, role, created_at) VALUES ('u2', 'u2@test.com', 'user', '2026-01-01T00:00:00Z');
+      INSERT INTO user_consents (user_id, consent_key, granted_at) VALUES ('u2', 'data:core', '2026-01-01T00:00:00Z');
+      INSERT INTO integration_tokens (id, user_id, provider, access_token, token_status, connected_at)
+        VALUES ('tok1', 'u2', 'todoist', 'secret', 'active', '2026-01-02T00:00:00Z');
+    `);
+
+    // Re-run migrations — the backfill should insert data:todoist
+    runMigrations(sqlite);
+
+    const rows = sqlite
+      .prepare(`SELECT consent_key FROM user_consents WHERE user_id = 'u2' ORDER BY consent_key`)
+      .all() as { consent_key: string }[];
+    expect(rows.map((r) => r.consent_key)).toEqual(['data:core', 'data:todoist']);
+  });
+
+  it('is idempotent — running twice does not duplicate consent rows', () => {
+    const sqlite = freshDb();
+    runMigrations(sqlite);
+
+    sqlite.exec(`
+      INSERT INTO users (id, email, role, created_at) VALUES ('u3', 'u3@test.com', 'user', '2026-01-01T00:00:00Z');
+      INSERT INTO integration_tokens (id, user_id, provider, access_token, token_status, connected_at)
+        VALUES ('tok2', 'u3', 'todoist', 'secret', 'active', '2026-01-02T00:00:00Z');
+    `);
+
+    runMigrations(sqlite);
+    runMigrations(sqlite);
+
+    const count = (sqlite
+      .prepare(`SELECT COUNT(*) as n FROM user_consents WHERE user_id = 'u3' AND consent_key = 'data:todoist'`)
+      .get() as { n: number }).n;
+    expect(count).toBe(1);
+  });
+});
+
--- a/services/api/src/db/migrations.ts
+++ b/services/api/src/db/migrations.ts
@@ -149,6 +149,13 @@ export function runMigrations(handle: BetterSqlite3Database) {
    CREATE INDEX IF NOT EXISTS idx_agent_outputs_user_agent_exp
      ON agent_outputs(user_id, agent_id, expires_at DESC);

+    CREATE TABLE IF NOT EXISTS task_enrichments (
+      content_hash TEXT PRIMARY KEY,
+      description TEXT NOT NULL,
+      model TEXT NOT NULL DEFAULT 'tip-generator',
+      created_at TEXT NOT NULL
+    );
+
    CREATE TABLE IF NOT EXISTS user_preferences (
      user_id TEXT NOT NULL REFERENCES users(id),
      scope TEXT NOT NULL,
@@ -208,6 +215,15 @@ export function runMigrations(handle: BetterSqlite3Database) {
    `);
  } catch { /* column already dropped — nothing to backfill */ }

+  // Backfill (issue #127): grant data:<provider> consent for every active integration token.
+  // Idempotent — INSERT OR IGNORE skips rows that already exist.
+  handle.exec(`
+    INSERT OR IGNORE INTO user_consents (user_id, consent_key, granted_at)
+    SELECT user_id, 'data:' || provider, connected_at
+    FROM integration_tokens
+    WHERE token_status = 'active'
+  `);
+
  // Drop legacy consent columns (ADR-0014 step 8). Runs after the backfill above.
  // Silently skips if already dropped (column not found error) or never existed (new DB).
  for (const stmt of [
--- a/services/api/src/db/schema.ts
+++ b/services/api/src/db/schema.ts
@@ -189,6 +189,15 @@ export const agentOutputs = sqliteTable('agent_outputs', {
  agentVersion: text('agent_version').notNull(), // bump to invalidate on logic changes
 });

+// Persistent cache for LLM-enriched task descriptions used by clustering.
+// Keyed by MD5 of raw task content; avoids re-calling LiteLLM on every agent compute cycle.
+export const taskEnrichments = sqliteTable('task_enrichments', {
+  contentHash: text('content_hash').primaryKey(),
+  description: text('description').notNull(),
+  model: text('model').notNull().default('tip-generator'),
+  createdAt: text('created_at').notNull(),
+});
+
 // Admin saved SQL queries.
 export const savedQueries = sqliteTable('saved_queries', {
  id: text('id').primaryKey(),
--- a/services/api/src/profile/eligibility.ts
+++ b/services/api/src/profile/eligibility.ts
@@ -1,8 +1,10 @@
 /**
- * Registry-driven agent eligibility filter (ADR-0014 step 5).
+ * Registry-driven agent eligibility filter (ADR-0014 step 5, updated by ADR-0015).
 *
 * Rules (all must pass for an agent to be eligible):
- *   1. All required_consents are granted and not revoked.
+ *   1. Every data:<source> in required_consents is granted and not revoked.
+ *      Consent is granted automatically when the user connects that data source.
+ *      agent:<id> consents no longer exist — per-agent control is a preference (rule 3).
 *   2. No silenced_in_contexts entry matches an active context.
 *   3. user_preferences[scope='agent:<id>', key='enabled'] is not false.
 *
--- a/services/api/src/routes/agent-outputs.ts
+++ b/services/api/src/routes/agent-outputs.ts
@@ -1,17 +1,19 @@
 import { Router, type Request, type Response, type IRouter } from 'express';
 import { nanoid } from 'nanoid';
 import { db } from '../db/index.js';
-import { agentOutputs, tipFeedback, tipViews, userPreferences } from '../db/schema.js';
-import { eq, and, gt, lt } from 'drizzle-orm';
+import { agentOutputs, tipFeedback, tipViews, userPreferences, taskEnrichments } from '../db/schema.js';
+import { eq, and, gt, lt, inArray } from 'drizzle-orm';
+import crypto from 'node:crypto';
 import { config } from '../config.js';
 import { getProfile, type Profile } from '../profile/builder.js';
 import { todoistSource } from '../signals/todoist.js';
+import { googleHealthSource } from '../signals/google-health.js';
 import { SignalAggregator } from '../signals/aggregator.js';

 const router: IRouter = Router();

 // Separate aggregator instance — avoids circular dep with recommender.ts.
-const _agentAggregator = new SignalAggregator().register(todoistSource);
+const _agentAggregator = new SignalAggregator().register(todoistSource).register(googleHealthSource);

 // ── Internal auth helper ──────────────────────────────────────────────────────

@@ -26,6 +28,33 @@ function checkInternalToken(req: Request, res: Response): boolean {

 // ── DB helpers ────────────────────────────────────────────────────────────────

+function contentHash(text: string): string {
+  return crypto.createHash('md5').update(text).digest('hex');
+}
+
+async function fetchEnrichmentCache(tasks: { content?: string }[]): Promise<Record<string, string>> {
+  const hashes = tasks
+    .map((t) => t.content?.trim())
+    .filter((c): c is string => !!c)
+    .map(contentHash);
+  if (!hashes.length) return {};
+  const rows = await db
+    .select({ contentHash: taskEnrichments.contentHash, description: taskEnrichments.description })
+    .from(taskEnrichments)
+    .where(inArray(taskEnrichments.contentHash, hashes));
+  return Object.fromEntries(rows.map((r) => [r.contentHash, r.description]));
+}
+
+async function persistEnrichments(newEntries: Record<string, string>): Promise<void> {
+  const now = new Date().toISOString();
+  for (const [hash, description] of Object.entries(newEntries)) {
+    await db
+      .insert(taskEnrichments)
+      .values({ contentHash: hash, description, createdAt: now })
+      .onConflictDoNothing();
+  }
+}
+
 export async function getActiveAgentOutputs(userId: string) {
  const now = new Date().toISOString();
  return db
@@ -126,22 +155,52 @@ async function persistInferredPrefs(
  }
 }

+function taskListHash(tasks: { content?: string }[]): string {
+  const sorted = tasks
+    .map((t) => t.content?.trim() ?? '')
+    .filter(Boolean)
+    .sort()
+    .join('\n');
+  return crypto.createHash('md5').update(sorted).digest('hex');
+}
+
+async function isUpToDate(userId: string, agentId: string, currentHash: string): Promise<boolean> {
+  const rows = await db
+    .select({ signalsSnapshot: agentOutputs.signalsSnapshot })
+    .from(agentOutputs)
+    .where(and(eq(agentOutputs.userId, userId), eq(agentOutputs.agentId, agentId)))
+    .limit(1);
+  if (!rows.length) return false;
+  try {
+    const snapshot = JSON.parse(rows[0].signalsSnapshot ?? '{}') as { _task_hash?: string };
+    return snapshot._task_hash === currentHash;
+  } catch { return false; }
+}
+
 export async function computeAndStore(userId: string, agentId: string): Promise<void> {
  let tasks: object[] = [];
  try {
    const signals = await _agentAggregator.fetchAll(userId);
    tasks = signals.map((s) => ({
      id: s.id,
+      source: s.source,
+      kind: s.kind,
      content: s.content,
+      // Task-specific fields (default to harmless values for non-task signals)
      priority: (s.features.priority as number) ?? 1,
      is_overdue: Boolean(s.features.is_overdue),
      task_age_days: (s.features.task_age_days as number) ?? 0,
      project_id: (s.metadata as Record<string, unknown>).project_id ?? null,
+      // All features spread so source-specific agents (e.g. health-vitals) can read them
+      ...s.features,
    }));
  } catch {
    // No integration or fetch error — agents that need tasks will report "no tasks"
  }

+  const currentTaskHash = taskListHash(tasks as { content?: string }[]);
+  if (await isUpToDate(userId, agentId, currentTaskHash)) return;
+
  let profile: Profile = {};
  try {
    profile = await getProfile(userId);
@@ -162,10 +221,13 @@ export async function computeAndStore(userId: string, agentId: string): Promise<
  // Load agent prefs (user overrides + previous inferences) to inject into the compute call.
  const agentPrefs = await loadAgentPrefs(userId, agentId);

+  // Fetch enrichment cache for task titles present in this compute call.
+  const enrichmentCache = await fetchEnrichmentCache(tasks as { content?: string }[]);
+
  const mlResp = await fetch(`${config.ML_SERVING_URL}/agents/${agentId}/compute`, {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
-    body: JSON.stringify({ user_id: userId, tasks, profile, feedback_history: feedbackHistory, agent_prefs: agentPrefs }),
+    body: JSON.stringify({ user_id: userId, tasks, profile, feedback_history: feedbackHistory, agent_prefs: agentPrefs, enrichment_cache: enrichmentCache, task_hash: currentTaskHash }),
    signal: AbortSignal.timeout(60_000),
  });

@@ -177,10 +239,16 @@ export async function computeAndStore(userId: string, agentId: string): Promise<
  const output = await mlResp.json() as {
    user_id: string; agent_id: string; prompt_text: string;
    signals_snapshot: unknown; computed_at: string; expires_at: string; agent_version: string;
+    new_enrichments?: Record<string, string>;
  };

  await storeAgentOutput(output);

+  // Persist any new enrichments produced during this compute cycle.
+  if (output.new_enrichments && Object.keys(output.new_enrichments).length > 0) {
+    await persistEnrichments(output.new_enrichments);
+  }
+
  // Run inference framework for this agent and persist results.
  // Failures are non-fatal — the compute result is already stored.
  try {
--- a/services/api/src/routes/integrations.ts
+++ b/services/api/src/routes/integrations.ts
@@ -1,7 +1,7 @@
 import { type Router as ExpressRouter, Router, Request, Response } from 'express';
 import { nanoid } from 'nanoid';
 import { db } from '../db/index.js';
-import { integrationTokens } from '../db/schema.js';
+import { integrationTokens, userConsents } from '../db/schema.js';
 import { eq, and } from 'drizzle-orm';
 import { config } from '../config.js';
 import { requireAuth, AuthenticatedRequest } from '../middleware/session.js';
@@ -12,9 +12,49 @@ const TODOIST_OAUTH_URL = 'https://todoist.com/oauth/authorize';
 const TODOIST_TOKEN_URL = 'https://todoist.com/oauth/access_token';
 const TODOIST_SCOPES = 'data:read_write';

+const GOOGLE_AUTH_URL = 'https://accounts.google.com/o/oauth2/v2/auth';
+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/fitness.body.read',
+  'https://www.googleapis.com/auth/fitness.sleep.read',
+  '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
 const pendingStates = new Map<string, { userId: string; redirectTo: string }>();

+async function grantDataSourceConsent(userId: string, provider: string): Promise<void> {
+  const consentKey = `data:${provider}`;
+  const now = new Date().toISOString();
+  await db.insert(userConsents)
+    .values({ userId, consentKey, grantedAt: now, revokedAt: null })
+    .onConflictDoUpdate({
+      target: [userConsents.userId, userConsents.consentKey],
+      set: { grantedAt: now, revokedAt: null },
+    });
+}
+
+async function revokeDataSourceConsent(userId: string, provider: string): Promise<void> {
+  const consentKey = `data:${provider}`;
+  const now = new Date().toISOString();
+  await db.insert(userConsents)
+    .values({ userId, consentKey, grantedAt: now, revokedAt: now })
+    .onConflictDoUpdate({
+      target: [userConsents.userId, userConsents.consentKey],
+      set: { revokedAt: now },
+    });
+}
+
 /** GET /api/integrations — list connected integrations */
 router.get('/', requireAuth, async (req: AuthenticatedRequest, res: Response) => {
  const tokens = await db
@@ -100,10 +140,102 @@ router.get('/todoist/callback', async (req: Request, res: Response) => {
    tokenStatus: 'active',
    connectedAt: now,
  });
+  await grantDataSourceConsent(pending.userId, 'todoist');

  res.redirect(`${config.WEB_BASE_URL}${pending.redirectTo}?connected=todoist`);
 });

+/** GET /api/integrations/google-health/connect — start Google Fit OAuth */
+router.get('/google-health/connect', requireAuth, (req: AuthenticatedRequest, res: Response) => {
+  const state = nanoid();
+  pendingStates.set(state, {
+    userId: req.userId!,
+    redirectTo: (req.query.redirectTo as string) ?? '/connect',
+  });
+  setTimeout(() => pendingStates.delete(state), 10 * 60 * 1000);
+
+  const url = new URL(GOOGLE_AUTH_URL);
+  url.searchParams.set('client_id', config.GOOGLE_CLIENT_ID);
+  url.searchParams.set('redirect_uri', `${config.API_BASE_URL}/api/integrations/google-health/callback`);
+  url.searchParams.set('response_type', 'code');
+  url.searchParams.set('scope', GOOGLE_HEALTH_SCOPES);
+  url.searchParams.set('state', state);
+  url.searchParams.set('access_type', 'offline');
+  url.searchParams.set('prompt', 'consent');
+
+  res.redirect(url.toString());
+});
+
+/** GET /api/integrations/google-health/callback — Google returns here */
+router.get('/google-health/callback', async (req: Request, res: Response) => {
+  const state = req.query.state as string;
+  const code = req.query.code as string;
+  const error = req.query.error as string | undefined;
+
+  if (error) {
+    res.status(400).json({ error: `Google denied access: ${error}` });
+    return;
+  }
+
+  const pending = pendingStates.get(state);
+  if (!pending) {
+    res.status(400).json({ error: 'Invalid or expired state' });
+    return;
+  }
+  pendingStates.delete(state);
+
+  const body = new URLSearchParams({
+    client_id: config.GOOGLE_CLIENT_ID,
+    client_secret: config.GOOGLE_CLIENT_SECRET,
+    code,
+    grant_type: 'authorization_code',
+    redirect_uri: `${config.API_BASE_URL}/api/integrations/google-health/callback`,
+  });
+
+  const tokenRes = await fetch(GOOGLE_TOKEN_URL, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/x-www-form-urlencoded', Accept: 'application/json' },
+    body: body.toString(),
+  });
+
+  if (!tokenRes.ok) {
+    const detail = await tokenRes.text().catch(() => '');
+    res.status(502).json({ error: `Failed to exchange Google token: ${detail}` });
+    return;
+  }
+
+  const tokenData = (await tokenRes.json()) as {
+    access_token: string;
+    refresh_token?: string;
+    expires_in: number;
+  };
+
+  const now = new Date();
+  const expiresAt = new Date(now.getTime() + tokenData.expires_in * 1000).toISOString();
+
+  await db
+    .delete(integrationTokens)
+    .where(
+      and(
+        eq(integrationTokens.userId, pending.userId),
+        eq(integrationTokens.provider, 'google-health'),
+      ),
+    );
+  await db.insert(integrationTokens).values({
+    id: nanoid(),
+    userId: pending.userId,
+    provider: 'google-health',
+    accessToken: tokenData.access_token,
+    refreshToken: tokenData.refresh_token ?? null,
+    expiresAt,
+    tokenStatus: 'active',
+    connectedAt: now.toISOString(),
+  });
+  await grantDataSourceConsent(pending.userId, 'google-health');
+
+  res.redirect(`${config.WEB_BASE_URL}${pending.redirectTo}?connected=google-health`);
+});
+
 /** DELETE /api/integrations/:provider — revoke token */
 router.delete('/:provider', requireAuth, async (req: AuthenticatedRequest, res: Response) => {
  const provider = String(req.params.provider);
@@ -120,13 +252,18 @@ router.delete('/:provider', requireAuth, async (req: AuthenticatedRequest, res:
    .limit(1);

  if (token?.provider === 'todoist') {
-    // Best-effort revocation
    await fetch('https://api.todoist.com/sync/v9/access_tokens/revoke', {
      method: 'POST',
      headers: { Authorization: `Bearer ${token.accessToken}` },
    }).catch(() => {});
  }

+  if (token?.provider === 'google-health') {
+    await fetch(`${GOOGLE_REVOKE_URL}?token=${token.accessToken}`, { method: 'POST' }).catch(() => {});
+  }
+
+  await revokeDataSourceConsent(req.userId!, provider);
+
  await db
    .delete(integrationTokens)
    .where(
--- a/services/api/src/routes/recommender.ts
+++ b/services/api/src/routes/recommender.ts
@@ -7,9 +7,10 @@ import { eq, and, desc } from 'drizzle-orm';
 import { requireAuth, AuthenticatedRequest } from '../middleware/session.js';
 import { config } from '../config.js';
 import { bus } from '../events/bus.js';
-import type { TipCandidate, Signal } from '@oo/shared-types';
+import type { Tip, Signal } from '@oo/shared-types';
 import { todoistSource, dueAgeDays } from '../signals/todoist.js';
 export { dueAgeDays };
+import { googleHealthSource } from '../signals/google-health.js';
 import { SignalAggregator } from '../signals/aggregator.js';
 import { getActiveAgentOutputs } from './agent-outputs.js';
 import { getEligibleAgentIds } from '../profile/eligibility.js';
@@ -19,35 +20,18 @@ const router: ExpressRouter = Router();
 // ---------------------------------------------------------------------------
 // Signal aggregator — register sources here as new integrations are added
 // ---------------------------------------------------------------------------
-export const aggregator = new SignalAggregator().register(todoistSource);
-export const _clearSignalCacheForTests = () => todoistSource.clearCache();
-
-// ---------------------------------------------------------------------------
-// Signal → TipCandidate conversion
-// ---------------------------------------------------------------------------
-function signalToCandidate(signal: Signal): TipCandidate {
-  return {
-    id: signal.id,
-    content: signal.content,
-    source: signal.source as TipCandidate['source'],
-    kind: signal.kind as TipCandidate['kind'],
-    sourceId: (signal.metadata.todoistId as string | undefined) ?? undefined,
-    createdAt: signal.timestamp,
-    features: signal.features,
-  };
-}
-
-function randomPolicy(candidates: TipCandidate[]): TipCandidate | null {
-  if (!candidates.length) return null;
-  return candidates[Math.floor(Math.random() * candidates.length)];
-}
+export const aggregator = new SignalAggregator().register(todoistSource).register(googleHealthSource);
+export const _clearSignalCacheForTests = () => {
+  todoistSource.clearCache();
+  googleHealthSource.clearCache();
+};

 // ---------------------------------------------------------------------------
 // Orchestrator: fetch agent snippets + call ml/serving /recommend
 // ---------------------------------------------------------------------------

 interface OrchestratorResult {
-  tip: TipCandidate;
+  tip: Tip;
  model: string | null;
  agentIds: string[];
 }
@@ -68,6 +52,7 @@ async function fetchOrchestratorTip(
  hour: number,
  dayOfWeek: number,
  traceparent?: string,
+  recentTip?: string,
 ): Promise<OrchestratorResult | null> {
  const [allAgentRows, eligibleIds, scienceDestiny] = await Promise.all([
    getActiveAgentOutputs(userId),
@@ -89,7 +74,7 @@ async function fetchOrchestratorTip(
    const res = await fetch(`${config.ML_SERVING_URL}/recommend`, {
      method: 'POST',
      headers: { 'Content-Type': 'application/json', ...(traceparent ? { traceparent } : {}) },
-      body: JSON.stringify({ user_id: userId, agent_outputs: agentOutputs, tasks, hour_of_day: hour, day_of_week: dayOfWeek, science_destiny: scienceDestiny ?? 50 }),
+      body: JSON.stringify({ user_id: userId, agent_outputs: agentOutputs, tasks, hour_of_day: hour, day_of_week: dayOfWeek, science_destiny: scienceDestiny ?? 50, recent_tip: recentTip ?? null }),
      signal: AbortSignal.timeout(15_000),
    });
    if (!res.ok) return null;
@@ -106,7 +91,6 @@ async function fetchOrchestratorTip(
        kind: 'advice' as const,
        rationale: data.tip.rationale,
        createdAt: now,
-        features: { is_overdue: false, task_age_days: 0, priority: 1 },
      },
      model: data.model ?? null,
      agentIds: agentOutputs.map((a) => a.agent_id),
@@ -123,6 +107,7 @@ async function fetchOrchestratorTip(
 router.post('/recommend', requireAuth, async (req: AuthenticatedRequest, res: Response) => {
  const hour = new Date().getHours();
  const dayOfWeek = new Date().getDay();
+  const { recent_tip: recentTip } = req.body as { recent_tip?: string };

  const anyToken = await db
    .select({ id: integrationTokens.id })
@@ -138,16 +123,16 @@ router.post('/recommend', requireAuth, async (req: AuthenticatedRequest, res: Re
  const signals = await aggregator.fetchAll(req.userId!);

  const t0 = Date.now();
-  const orchestrated = await fetchOrchestratorTip(req.userId!, signals, hour, dayOfWeek, req.traceparent);
+  const orchestrated = await fetchOrchestratorTip(req.userId!, signals, hour, dayOfWeek, req.traceparent, recentTip);
  const latencyMs = Date.now() - t0;

-  const tip = orchestrated?.tip ?? randomPolicy(signals.map(signalToCandidate));
-  if (!tip) {
+  if (!orchestrated) {
    res.status(204).end();
    return;
  }

-  const policy = orchestrated ? 'orchestrator' : 'random';
+  const tip = orchestrated.tip;
+  const policy = 'orchestrator';
  const servedAt = new Date().toISOString();

  await db.insert(tipViews).values({ id: nanoid(), userId: req.userId!, tipId: tip.id, servedAt });
@@ -158,16 +143,12 @@ router.post('/recommend', requireAuth, async (req: AuthenticatedRequest, res: Re
    tipId: tip.id,
    policy,
    mlScore: null,
-    featuresJson: JSON.stringify(
-      orchestrated
-        ? { agent_ids: orchestrated.agentIds, hour_of_day: hour, day_of_week: dayOfWeek }
-        : { ...tip.features, hour_of_day: hour, day_of_week: dayOfWeek },
-    ),
-    candidateCount: orchestrated ? 1 : signals.length,
+    featuresJson: JSON.stringify({ agent_ids: orchestrated.agentIds, hour_of_day: hour, day_of_week: dayOfWeek }),
+    candidateCount: 1,
    latencyMs,
    servedAt,
-    promptVersion: orchestrated ? 'v4-orchestrator' : null,
-    llmModel: orchestrated ? orchestrated.model : null,
+    promptVersion: 'v4-orchestrator',
+    llmModel: orchestrated.model,
    tipKind: tip.kind ?? null,
  });

--- a/services/api/src/signals/google-health.ts
+++ b/services/api/src/signals/google-health.ts
@@ -0,0 +1,312 @@
+import type { Signal, SignalSource } from '@oo/shared-types';
+import { db } from '../db/index.js';
+import { integrationTokens } from '../db/schema.js';
+import { eq, and } from 'drizzle-orm';
+import { bus } from '../events/bus.js';
+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 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 {
+  dataset: Array<{
+    dataSourceId: string;
+    point: Array<{ value: Array<{ intVal?: number; fpVal?: number }> }>;
+  }>;
+}
+
+interface FitAggregateResponse {
+  bucket?: FitBucket[];
+}
+
+interface FitSession {
+  name: string;
+  startTimeMillis: string;
+  endTimeMillis: string;
+  activityType: number;
+}
+
+interface FitSessionsResponse {
+  session?: FitSession[];
+}
+
+async function refreshGoogleToken(
+  userId: string,
+  refreshToken: string,
+): Promise<string | null> {
+  const body = new URLSearchParams({
+    client_id: config.GOOGLE_CLIENT_ID,
+    client_secret: config.GOOGLE_CLIENT_SECRET,
+    refresh_token: refreshToken,
+    grant_type: 'refresh_token',
+  });
+
+  const res = await fetch(GOOGLE_TOKEN_URL, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/x-www-form-urlencoded' },
+    body: body.toString(),
+  });
+
+  if (!res.ok) return null;
+
+  const data = (await res.json()) as { access_token: string; expires_in: number };
+  const expiresAt = new Date(Date.now() + data.expires_in * 1000).toISOString();
+
+  await db
+    .update(integrationTokens)
+    .set({ accessToken: data.access_token, expiresAt, tokenStatus: 'active' })
+    .where(and(eq(integrationTokens.userId, userId), eq(integrationTokens.provider, 'google-health')));
+
+  return data.access_token;
+}
+
+function todayMidnightMs(): number {
+  const d = new Date();
+  d.setHours(0, 0, 0, 0);
+  return d.getTime();
+}
+
+function yesterdayIso(): string {
+  return new Date(Date.now() - 24 * 60 * 60 * 1000).toISOString();
+}
+
+async function fetchAggregates(
+  token: string,
+  startMs: number,
+  endMs: number,
+): Promise<FitAggregateResponse> {
+  const res = await fetch(FIT_AGGREGATE_URL, {
+    method: 'POST',
+    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}`);
+  return res.json() as Promise<FitSessionsResponse>;
+}
+
+function extractMetric(
+  bucket: FitBucket,
+  dataTypeName: string,
+  valueKey: 'intVal' | 'fpVal',
+): number {
+  for (const ds of bucket.dataset) {
+    if (!ds.dataSourceId.includes(dataTypeName.replace('com.google.', '').replace('.', '_'))) continue;
+    for (const pt of ds.point) {
+      const v = pt.value[0];
+      if (v) return valueKey === 'intVal' ? (v.intVal ?? 0) : (v.fpVal ?? 0);
+    }
+  }
+  return 0;
+}
+
+function extractAnyMetric(
+  bucket: FitBucket,
+  typeSuffix: string,
+  valueKey: 'intVal' | 'fpVal',
+): number {
+  for (const ds of bucket.dataset) {
+    if (!ds.dataSourceId.includes(typeSuffix)) continue;
+    const pt = ds.point[0];
+    if (pt?.value[0]) {
+      const v = pt.value[0];
+      return valueKey === 'intVal' ? (v.intVal ?? 0) : (v.fpVal ?? 0);
+    }
+  }
+  return 0;
+}
+
+export class GoogleHealthSignalSource implements SignalSource {
+  readonly id = 'google-health';
+
+  private cache = new Map<string, { signals: Signal[]; fetchedAt: number }>();
+
+  clearCache(userId?: string): void {
+    if (userId) this.cache.delete(userId);
+    else this.cache.clear();
+  }
+
+  async fetchSignals(userId: string): Promise<Signal[]> {
+    const entry = this.cache.get(userId);
+    if (entry && Date.now() - entry.fetchedAt < CACHE_TTL_MS) return entry.signals;
+
+    const [row] = await db
+      .select()
+      .from(integrationTokens)
+      .where(and(eq(integrationTokens.userId, userId), eq(integrationTokens.provider, 'google-health')))
+      .limit(1);
+
+    if (!row) return [];
+
+    let token = row.accessToken;
+    const isExpired = row.expiresAt && new Date(row.expiresAt).getTime() - Date.now() < 5 * 60_000;
+
+    if (isExpired && row.refreshToken) {
+      const refreshed = await refreshGoogleToken(userId, row.refreshToken);
+      if (!refreshed) {
+        logger.warn({ userId }, 'google-health: refresh failed');
+        await db
+          .update(integrationTokens)
+          .set({ tokenStatus: 'needs_reconnect' })
+          .where(and(eq(integrationTokens.userId, userId), eq(integrationTokens.provider, 'google-health')));
+        bus.publish('signals.integration.token_expired', {
+          userId,
+          provider: 'google-health',
+          detectedAt: new Date().toISOString(),
+        });
+        return entry?.signals ?? [];
+      }
+      token = refreshed;
+    }
+
+    try {
+      const startMs = todayMidnightMs();
+      const endMs = Date.now();
+
+      const [aggData, sleepData] = await Promise.all([
+        fetchAggregates(token, startMs, endMs),
+        fetchSleepSessions(token),
+      ]);
+
+      const bucket = aggData.bucket?.[0];
+      const signals: Signal[] = [];
+      const now = new Date().toISOString();
+
+      if (bucket) {
+        // Steps
+        const steps = extractAnyMetric(bucket, 'step_count', 'intVal');
+        const stepGoalPct = Math.round((steps / STEP_DAILY_GOAL) * 100);
+        signals.push({
+          id: `google-health:steps`,
+          source: 'google-health',
+          kind: 'health',
+          content: `${steps.toLocaleString()} steps today (${stepGoalPct}% of ${STEP_DAILY_GOAL.toLocaleString()} goal)`,
+          metadata: { dataType: 'steps' },
+          features: {
+            step_count: steps,
+            step_goal_pct: stepGoalPct,
+            step_goal: STEP_DAILY_GOAL,
+            below_step_goal: steps < STEP_DAILY_GOAL,
+          },
+          timestamp: now,
+        });
+
+        // Calories + active minutes
+        const calories = Math.round(extractAnyMetric(bucket, 'calories', 'fpVal'));
+        const activeMinutes = extractAnyMetric(bucket, 'active_minutes', 'intVal');
+        signals.push({
+          id: `google-health:activity`,
+          source: 'google-health',
+          kind: 'health',
+          content: `${activeMinutes} active minutes, ${calories} calories burned today`,
+          metadata: { dataType: 'activity' },
+          features: {
+            active_minutes: activeMinutes,
+            calories_burned: calories,
+            sedentary: activeMinutes < 20,
+          },
+          timestamp: now,
+        });
+
+        // Heart rate
+        const bpm = Math.round(extractAnyMetric(bucket, 'heart_rate', 'fpVal'));
+        if (bpm > 0) {
+          signals.push({
+            id: `google-health:heart_rate`,
+            source: 'google-health',
+            kind: 'health',
+            content: `Resting heart rate: ${bpm} bpm`,
+            metadata: { dataType: 'heart_rate' },
+            features: { resting_bpm: bpm, elevated_hr: bpm > 90 },
+            timestamp: now,
+          });
+        }
+      }
+
+      // Sleep — find the most recent sleep session
+      if (sleepData.session?.length) {
+        const sorted = [...sleepData.session].sort(
+          (a, b) => Number(b.endTimeMillis) - Number(a.endTimeMillis),
+        );
+        const last = sorted[0]!;
+        const durationMs = Number(last.endTimeMillis) - Number(last.startTimeMillis);
+        const sleepHours = Math.round((durationMs / 3_600_000) * 10) / 10;
+        const belowGoal = sleepHours < SLEEP_GOAL_HOURS;
+        signals.push({
+          id: `google-health:sleep`,
+          source: 'google-health',
+          kind: 'health',
+          content: `${sleepHours}h sleep last night (${belowGoal ? 'below' : 'meets'} ${SLEEP_GOAL_HOURS}h goal)`,
+          metadata: { dataType: 'sleep', sessionName: last.name },
+          features: {
+            sleep_hours: sleepHours,
+            sleep_goal_hours: SLEEP_GOAL_HOURS,
+            sleep_deficit_hours: Math.max(0, SLEEP_GOAL_HOURS - sleepHours),
+            below_sleep_goal: belowGoal,
+          },
+          timestamp: now,
+        });
+      }
+
+      this.cache.set(userId, { signals, fetchedAt: Date.now() });
+      bus.publish('signals.task.synced', {
+        userId,
+        source: 'google-health',
+        count: signals.length,
+        syncedAt: now,
+      });
+
+      return signals;
+    } catch (err: unknown) {
+      const status = (err as { message?: string }).message;
+      if (status?.includes('401')) {
+        logger.warn({ userId }, 'google-health: token expired (401)');
+        if (row.refreshToken) {
+          await refreshGoogleToken(userId, row.refreshToken);
+        } else {
+          await db
+            .update(integrationTokens)
+            .set({ tokenStatus: 'needs_reconnect' })
+            .where(and(eq(integrationTokens.userId, userId), eq(integrationTokens.provider, 'google-health')));
+          bus.publish('signals.integration.token_expired', {
+            userId,
+            provider: 'google-health',
+            detectedAt: new Date().toISOString(),
+          });
+        }
+      } else {
+        logger.error({ userId, err }, 'google-health: fetch failed');
+      }
+      return entry?.signals ?? [];
+    }
+  }
+}
+
+export const googleHealthSource = new GoogleHealthSignalSource();
Author	SHA1	Message	Date
alvis	772bb6e194	feat(consents): auto-grant data:<provider> on connect; remove agent: consents (ADR-0015) - integrations.ts: grant data:<provider> on OAuth callback, revoke on disconnect - Backfill migration: INSERT OR IGNORE data:<provider> for all active tokens - Agent manifests: drop agent:<id> from required_consents (momentum, time-of-day, overdue-task, recent-patterns, health-vitals) — per-agent control is a preference - eligibility.ts: update comment to reflect data:-only consent model - test_manifest.py: assert no agent: consents remain in any manifest - migrations.test.ts: backfill idempotency tests for issue #127 - Dockerfile.api: drop --offline flag (fixes ERR_PNPM_NO_OFFLINE_META) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-05-12 15:09:58 +00:00
alvis	34925310cf	docs: update focus-area manifest description and CLAUDE.md Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-05-12 15:00:06 +00:00
alvis	f66f337779	feat(focus-area): use enriched descriptions in cluster output cluster_tasks now attaches enriched_description to each task dict. focus-area reads enriched_description (falling back to raw content) when building the area summary, so the orchestrator sees the expanded 3-sentence descriptions instead of terse raw titles. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-05-12 14:58:31 +00:00
alvis	f6b89fc849	refactor(focus-area): output all clusters as context; remove scoring and preferred_areas The agent no longer picks a winner — it summarises every cluster so the orchestrator can decide what's relevant. Scoring by overdue count overlapped with the overdue-task agent. preferred_areas (project-ID based, broken label matching) removed entirely. Output format: numbered list of areas with task titles included. Snapshot: {cluster_count, clusters: [{label, task_count, tasks}]}. Version bumped to 3.0.0; inferred_params cleared. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-05-12 14:57:04 +00:00
alvis	12c956b588	fix(clustering): drop TTL check from isUpToDate; task hash is the only signal If tasks haven't changed, the output is valid forever. If they changed, always recompute regardless of age. TTL on focus-area restored to 24h — it only controls recommender eligibility, not recompute frequency. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-05-12 14:46:43 +00:00
alvis	d12f11d29d	feat(clustering): 1h TTL + skip recompute when tasks unchanged focus-area now recomputes at most once per hour, and only if the task list actually changed since the last compute. - focus-area TTL: 43200s → 3600s; version bumped to 2.1.0 - computeAndStore hashes sorted task contents (MD5) and checks the stored _task_hash in the existing snapshot; skips the ml-serving call when the hash matches and the output isn't expired - ml-serving injects _task_hash into the snapshot so the next cycle can compare Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-05-12 14:45:15 +00:00
alvis	9ddeea6cac	feat(clustering): persistent enrichment cache in task_enrichments table Each unique task title is now enriched by LiteLLM once and cached in the DB. Subsequent agent compute cycles (every 12h) fetch the cache before calling ml-serving; only new titles hit the tip-generator. - DB: task_enrichments(content_hash PK, description, model, created_at) - TS: fetchEnrichmentCache / persistEnrichments helpers in agent-outputs.ts; enrichment_cache passed in compute request, new_enrichments persisted from response - Python: AgentComputeRequest.enrichment_cache / AgentComputeResponse.new_enrichments; AgentInput.enrichment_cache; _enrich_batch returns (descriptions, new_entries); cluster_tasks returns (clusters, new_enrichments) - FocusAreaAgent stashes new_enrichments in signals_snapshot under _new_enrichments; compute_agent endpoint pops it before storing the snapshot Closes part of #129 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-05-12 14:39:35 +00:00
alvis	08d08ad7b0	feat(clustering): LLM-enrichment before embedding (port from taskpile #129 ) Ported from taskpile experiments/clustering_eval (prompt v1, qwen2.5:1.5b). The experiment showed ARI 0.22→0.77 and AUROC 0.76→0.91 on synthetic tasks when embedding LLM-expanded descriptions instead of raw titles. - Expand each task title via LiteLLM tip-generator before embedding - Prefix with "clustering: " (nomic-embed-text task instruction prefix) - Cache expansions in-memory by content hash within a compute cycle - Falls back to raw title if enrichment fails; no change to fallback behaviour Fixes #129 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-05-12 14:20:48 +00:00
alvis	1ca2351488	fix(clustering): route embeddings through LiteLLM instead of Ollama directly The old code called Ollama's /api/embeddings one task at a time, which caused silent fallback to project-based grouping when host.docker.internal:11434 was unreachable from the ml-serving container. - Switch to LiteLLM /embeddings (model alias "embedder") as primary path - Batch all task contents in one request instead of N serial calls - Fall back to Ollama /api/embed (updated to current API) when LITELLM_URL is absent - Update tests to mock _embed_batch instead of the removed _embed Fixes #123 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-05-12 13:42:53 +00:00
alvis	4e9210fcef	fix(web): wrap loadTip in arrow fn to satisfy MouseEventHandler type	2026-05-12 13:34:46 +00:00
alvis	59c493323f	fix(recommender): remove Todoist fallback on orchestrator failure; add snooze exclusion When fetchOrchestratorTip returned null (LiteLLM timeout, bad JSON, etc.) the recommender silently fell back to randomPolicy, serving a raw Todoist task with no rationale — explaining both reported symptoms. - Remove randomPolicy/signalToCandidate; return 204 when orchestrator fails so the UI shows "All clear" instead of a confusing Todoist task - Pass recent_tip through the stack (frontend → POST /recommend → fetchOrchestratorTip → ml/serving RecommendRequest → build_orchestrator_messages) so after snooze the LLM is instructed not to repeat the snoozed content Fixes #122 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-05-12 13:28:32 +00:00
alvis	d4b40e2590	docs: document MLflow trace API, span inspection, and no-agent diagnosis Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-05-11 11:23:13 +00:00
alvis	a0a069c525	fix(admin): break redirect loop on /forbidden for non-admin users The middleware was redirecting non-admins to /forbidden but /forbidden wasn't excluded from the matcher, so the middleware ran again on that page, saw a non-admin, and redirected again — infinite loop. Added /forbidden to the pass-through list alongside /login. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-05-11 11:12:16 +00:00
alvis	d1f28666b0	feat(integrations): add Google Health (Fit) integration with full permissions OAuth2 flow with all 11 Google Fitness scopes (activity, body, sleep, heart rate, nutrition, location, blood glucose/pressure/temperature, oxygen saturation, reproductive health). Stores access + refresh tokens; auto-refreshes on expiry. GoogleHealthSignalSource fetches steps, sleep sessions, active minutes, calories, and heart rate from the Fit aggregate + sessions APIs. Signals flow into both the tip orchestrator and the health-vitals pre-compute agent, which generates prompt snippets about step progress, sleep deficit, sedentary time, and elevated heart rate. Signal.kind extended with 'health'; IntegrationProvider extended with 'google-health'. Agent compute signal mapping enriched to include source, kind, and all features so health-vitals can filter its own signals. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-05-11 11:12:11 +00:00