feat(profile): /api/profile + eligibility filter + inference framework (ADR-0014 steps 4-6)

Step 4 — /api/profile read-through API:
  GET  /api/profile          → { user, prefs, consents, contexts }
  PATCH /api/profile/prefs/:scope  upsert user_preferences (source='user')
  PATCH /api/profile/consents      grant / revoke consent keys
  PATCH /api/profile/contexts      create / activate / deactivate contexts
  Legacy consentGiven bit folded in as data:core fallback.

Step 5 — registry-driven eligibility filter:
  fetchRegistry() exported from agent-registry.ts.
  profile/eligibility.ts: getEligibleAgentIds(userId) — filters by required
  consents, silenced_in_contexts, and user_preferences[enabled=false].
  fetchOrchestratorTip filters agent_outputs to eligible set before calling
  ml/serving /recommend. Fail-closed: registry unavailable → empty set.

Step 6 — shared context-inference framework (#111) + time-of-day proof (#112):
  ml/agents/inference/: UserHistory, FeedbackEvent, run_inference().
  Framework: cold-start, min_history gating, error fallback, structured logs.
  TimeOfDayAgent v1.1.0: inferred_params=[preferred_hour]; also reads
  quiet_start/quiet_end from agent_prefs. agent_prefs injected by TS caller.
  AgentInput gains agent_prefs field.
  ml/serving: POST /agents/{agent_id}/infer endpoint.
  agent-outputs.ts computeAndStore: loads prefs before compute, calls /infer
  after, persists results (source='inferred'); user overrides never touched.

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

ml/agents/inference/__init__.py

@@ -0,0 +1,9 @@
+"""Shared context-inference framework (ADR-0014 §3, issue #111).
+
+Each agent's manifest declares InferredParams; this package owns the
+scheduling contract, history data model, and write path to user_preferences.
+"""
+from .framework import run_inference
+from .history import FeedbackEvent, UserHistory
+
+__all__ = ["run_inference", "FeedbackEvent", "UserHistory"]

ml/agents/inference/framework.py

@@ -0,0 +1,59 @@
+"""run_inference — core of the context-inference framework (ADR-0014 §3).
+
+Contract:
+    run_inference(manifest, history) → dict[key, value]
+
+Semantics:
+  - For each InferredParam in manifest.inferred_params:
+      - If len(history.events) < param.min_history → emit cold_start_default.
+      - Otherwise → call param.infer(history) and emit the result.
+  - Returns {key: value} ready for the caller to persist to user_preferences
+    with source='inferred'.
+  - User overrides (source='user') are handled by the caller's upsert logic;
+    this function has no DB access.
+"""
+from __future__ import annotations
+
+import logging
+import time
+from typing import Any
+
+from ..manifest import AgentManifest
+from .history import UserHistory
+
+log = logging.getLogger(__name__)
+
+
+def run_inference(manifest: AgentManifest, history: UserHistory) -> dict[str, Any]:
+    """Evaluate all InferredParams for an agent and return {key: inferred_value}."""
+    result: dict[str, Any] = {}
+    n = len(history.events)
+
+    for param in manifest.inferred_params:
+        t0 = time.monotonic()
+        if param.infer is None:
+            result[param.key] = param.cold_start_default
+            continue
+        if n < param.min_history:
+            value = param.cold_start_default
+            source = "cold_start"
+        else:
+            try:
+                value = param.infer(history)
+                source = "inferred"
+            except Exception as exc:
+                log.warning(
+                    "inference_error agent=%s param=%s error=%s — using cold_start_default",
+                    manifest.id, param.key, exc,
+                )
+                value = param.cold_start_default
+                source = "error_fallback"
+
+        latency_ms = round((time.monotonic() - t0) * 1000, 1)
+        log.info(
+            "inference_param agent=%s param=%s source=%s value=%r history_len=%d latency_ms=%s",
+            manifest.id, param.key, source, value, n, latency_ms,
+        )
+        result[param.key] = value
+
+    return result

ml/agents/inference/history.py

@@ -0,0 +1,29 @@
+"""UserHistory — normalised view of a user's feedback events for inference."""
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from datetime import datetime, timezone
+
+
+@dataclass
+class FeedbackEvent:
+    action: str              # 'done' | 'dismiss' | 'snooze' | 'helpful' | 'not_helpful'
+    dwell_ms: int | None
+    created_at: str          # ISO 8601
+
+    @property
+    def hour(self) -> int:
+        """Hour of day (0-23) when the feedback was recorded."""
+        try:
+            dt = datetime.fromisoformat(self.created_at.replace("Z", "+00:00"))
+        except ValueError:
+            return 12
+        if dt.tzinfo is None:
+            dt = dt.replace(tzinfo=timezone.utc)
+        return dt.hour
+
+
+@dataclass
+class UserHistory:
+    user_id: str
+    events: list[FeedbackEvent] = field(default_factory=list)