feat(profile): user-profile feature registry + builder (phase A)

Centralizes user-level features (completion_rate_30d, dismiss_rate_30d,
mean_dwell_ms_30d, preferred_hour, tip_volume_30d) in a TS registry that
owns both definition and SQL aggregation, since the data lives in the
TS-owned SQLite tables (tip_views/tip_feedback). Lazy TTL refresh keeps
recommend latency bounded; values persist in user_profile_features (KV).

ml/serving accepts profile_features on /score + /generate but does not
yet consume them — extending the bandit feature vector changes D and
resets every user's learned state, so that's a deliberate phase-B step.

Includes ml/features/profile_schema.py as a contract mirror with a sync
test that diffs name sets against registry.ts.

ADR-0011 records the data-locality reasoning (registry in TS, not Python
as the issue originally suggested).

Phase B (deferred): event-driven incremental updates, bandit consumption
with state migration, admin per-user profile page, staleness alerts.

Refs #81.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

ml/serving/main.py

@@ -152,6 +152,11 @@ class ScoreRequest(BaseModel):
     user_id: str
     candidates: list[Candidate]
     context: Context = Context()
+    # User-level features computed by the API (#81 phase A). Accepted, logged,
+    # but not yet consumed by the bandit — extending the feature vector
+    # changes `D` and resets every user's learned state, which is a deliberate
+    # follow-up (phase B), not a side effect of this PR.
+    profile_features: Optional[dict] = None
 
 
 class ScoreResponse(BaseModel):
@@ -184,6 +189,9 @@ class GenerateRequest(BaseModel):
     context: PromptContext = PromptContext()
     n: int = 3
     prompt_version: Optional[str] = None  # None → server default (env DEFAULT_PROMPT_VERSION)
+    # User-level features (#81 phase A). Accepted by the contract; not yet
+    # injected into the prompt — that's a #84-style prompt-design decision.
+    profile_features: Optional[dict] = None
 
 
 class TipCandidate(BaseModel):