feat(realism): operator-tunable planner weights via realism_config

New realism_config table (uuid PK + unique key) + two repo methods
(get/set) backs an admin-only GET/PUT /api/v1/realism/config surface.

The planner now exposes apply_payload(payload) / current_payload() /
reset_to_defaults() and reads its weights through mutable module
globals; pick() resolves the live values each call. Validation
catches negative weights, zero totals, out-of-range canary_probability,
unknown content_class names, and silently drops cross-list entries
(canary class on the user list, etc).

The orchestrator worker calls _refresh_realism_config(repo) on
startup and every 5 ticks (~5min at 60s interval). Operator changes
land within one refresh window with no bus signal — the simpler path
for a knob whose latency tolerance is minutes.

tests/orchestrator/test_realism_config_refresh.py

@@ -0,0 +1,74 @@
+"""The orchestrator pulls operator-tuned weights from realism_config.
+
+§3c contract: the planner reads in-memory module globals, but the
+operator's tuning lives in the DB (admin PUT /api/v1/realism/config).
+The orchestrator worker bridges the two by calling
+``_refresh_realism_config(repo)`` at startup and every Nth tick.
+"""
+from __future__ import annotations
+
+import json
+from unittest.mock import AsyncMock
+
+import pytest
+
+from decnet.orchestrator.worker import _refresh_realism_config
+from decnet.realism import planner
+
+
+@pytest.fixture(autouse=True)
+def _reset_planner():
+    yield
+    planner.reset_to_defaults()
+
+
+@pytest.mark.asyncio
+async def test_refresh_no_row_keeps_defaults():
+    repo = AsyncMock()
+    repo.get_realism_config = AsyncMock(return_value=None)
+    await _refresh_realism_config(repo)
+    assert planner.current_payload()["canary_probability"] == pytest.approx(0.03)
+
+
+@pytest.mark.asyncio
+async def test_refresh_applies_stored_payload():
+    repo = AsyncMock()
+    repo.get_realism_config = AsyncMock(return_value={
+        "key": "weights",
+        "value": json.dumps({"canary_probability": 0.12}),
+    })
+    await _refresh_realism_config(repo)
+    assert planner.current_payload()["canary_probability"] == pytest.approx(0.12)
+
+
+@pytest.mark.asyncio
+async def test_refresh_swallows_db_error():
+    """A wedged DB must not bring down the orchestrator's tick loop."""
+    repo = AsyncMock()
+    repo.get_realism_config = AsyncMock(side_effect=RuntimeError("boom"))
+    await _refresh_realism_config(repo)  # does not raise
+    # planner unchanged
+    assert planner.current_payload()["canary_probability"] == pytest.approx(0.03)
+
+
+@pytest.mark.asyncio
+async def test_refresh_swallows_malformed_json():
+    repo = AsyncMock()
+    repo.get_realism_config = AsyncMock(return_value={
+        "key": "weights",
+        "value": "not-json",
+    })
+    await _refresh_realism_config(repo)
+    assert planner.current_payload()["canary_probability"] == pytest.approx(0.03)
+
+
+@pytest.mark.asyncio
+async def test_refresh_swallows_invalid_payload():
+    repo = AsyncMock()
+    repo.get_realism_config = AsyncMock(return_value={
+        "key": "weights",
+        "value": json.dumps({"canary_probability": 9.0}),
+    })
+    await _refresh_realism_config(repo)
+    # Planner config not corrupted by a bad refresh.
+    assert planner.current_payload()["canary_probability"] == pytest.approx(0.03)