feat(onboarding): Observation Log — append-only JSONL calibration store (Task 59b/c v1)

Per the user's decision (2026-06-28): observations are CALIBRATION data for the knowledge base, NOT
business data and NOT product-DB data. So they live with the other versioned knowledge artifacts as an
append-only JSONL log under knowledge/observations/ — NO migration, NO DB. (A real persistence layer is
only warranted once thousands of onboardings exist; not before.)

  - ObservationRecord = Observation + log metadata (observation_id, timestamp [caller-stamped, no hidden
    clock], customer_archetype [anonymised — NEVER a real name], evidence, provenance, knowledge_version).
  - append_observation() writes one JSON line; append-only, lines are never rewritten. A later review is a
    NEW line with the same observation_id; load_observations(reconcile=True) keeps the latest per id.
  - load_observations() reads a single .jsonl or a directory of monthly .jsonl files.
  - aggregate_by_hypothesis() (59c) -> per-hypothesis distribution + confidence, COMPUTED from the log
    (computed-not-stored); the review gate (reviewed-only) is enforced in empirical_distribution/confidence.
  - review_queue() -> the unreviewed worklist. Observation -> Review -> Accepted -> recompute, never
    Observation -> confidence++. Nothing is ever written back to a hypothesis.

You can `rm` the log and recompute, `git diff` it over months, or rebuild confidence under a new policy —
fully consistent with computed-not-stored and the product/knowledge data separation.

Non-runtime (module + tests only, no endpoint) -> origin/main, NO dev deploy. 5 new tests (append-only,
review supersession, review-gate statistics, queue, monthly-file load); 27 onboarding tests pass, mypy
--strict clean (9 modules), check-loc 0. 59d (surface computed confidence at runtime) stays a later step.

backend-compliance/compliance/onboarding/__init__.py

@@ -21,6 +21,14 @@ from .observations import (
     empirical_distribution,
     reviewed,
 )
+from .observation_log import (
+    HypothesisStats,
+    ObservationRecord,
+    aggregate_by_hypothesis,
+    append_observation,
+    load_observations,
+    review_queue,
+)
 from .signals import (
     ProducedSignal,
     SignalVocabularyEntry,
@@ -69,4 +77,10 @@ __all__ = [
     "ProducedSignal",
     "SignalVocabularyEntry",
     "normalize_signals",
+    "ObservationRecord",
+    "HypothesisStats",
+    "append_observation",
+    "load_observations",
+    "aggregate_by_hypothesis",
+    "review_queue",
 ]