feat(anomalies): a metric out of its normal is an anomaly to review (#5740)

#5740 "A metric that departs from its normal is recorded as an anomaly to
review, and is findable after it ends", milestone 513.

- Every minute each learned key's last 15 minutes is judged against its
  hour's normal: above the usual hold x1.25 and by the metric's floor opens
  an occurrence, which ends when the level comes back.
- metric_anomalies holds one row per item to review; a repeat while it
  still waits joins it as an occurrence (metric_anomaly_spans).
- An ended row nobody reviews within anomalies.review_days (14, Settings ->
  Thresholds) lapses; an ongoing one never does.
- Learning skips every hour an occurrence covers unless its anomaly was
  marked normal: no normal is learned without review.
- Alerts -> Anomalies lists those to review first, then the reviewed and
  lapsed under day dividers; marking is operator/admin and audited.
- The Overview widget and get_overview warn of open ones and count those
  to review; the host page shows its own; the weekly report lists the
  week's and the lapsed; MCP list_anomalies and review_anomaly.
- Reviewed and lapsed rows are pruned at data.retention_days, and kept
  in backups like alert history.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

steward/core/settings.py

@@ -114,6 +114,9 @@ DEFAULTS: dict[str, Any] = {
     # Seconds a host's clock may be from Steward's before it is called out
     # (milestone 490), either way.
     "thresholds.clock_warn_s": 30, "thresholds.clock_crit_s": 300,
+    # Days an ended metric anomaly waits to be reviewed before it lapses:
+    # it leaves the to-review list and is kept as not reviewed (#5740).
+    "anomalies.review_days": 14,
     # Docker time-series retention (rule 25 — tunable, no restart). Raw 30s
     # samples are heavy, so keep a short raw window then roll up to hourly
     # averages kept much longer; lifecycle events are light, keep a month.