[KM-624] Compute Functions: fix non-JSON-safe scalars in mode & top_value

Two latent bugs in the same class as the Decimal crash: numpy/pandas scalars
leaked into tool output and would break JSON serialization (SSE / persistence).

- analyze_descriptive: `mode` of an integer column returned numpy.int64 (not
JSON-serializable); a datetime column returned a pandas.Timestamp. Added a
_clean pass (int64 -> int, Timestamp -> ISO string).
- analyze_profile: `top_value` of a datetime column returned a pandas.Timestamp
that failed json.dumps. Hardened _clean to coerce Timestamp -> ISO string.

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

src/tools/analytics/descriptive.py

@@ -37,6 +37,22 @@ class ColumnNotFoundError(ValueError):
     """A requested column is absent from the DataFrame (maps to error_code COLUMN_NOT_FOUND)."""
 
 
+def _clean(value: object) -> object:
+    """Coerce a scalar to a JSON-clean Python value.
+
+    `mode` can be any dtype: an integer column yields `numpy.int64` (NOT
+    JSON-serializable), a datetime column yields `pandas.Timestamp`. The other
+    metrics are already wrapped in `float(...)`; mode is the one that needs this.
+    """
+    if value is None:
+        return None
+    if isinstance(value, pd.Timestamp):
+        return value.isoformat()
+    if hasattr(value, "item"):
+        return value.item()
+    return value
+
+
 def _describe_one(series: pd.Series, metrics: tuple[str, ...]) -> dict[str, object]:
     """Compute descriptive metrics for a single column.
 
@@ -59,7 +75,7 @@ def _describe_one(series: pd.Series, metrics: tuple[str, ...]) -> dict[str, obje
             out["null_rate"] = float(series.isna().mean()) if total else 0.0
         elif m == "mode":
             modes = non_null.mode()
-            out["mode"] = modes.iloc[0] if not modes.empty else None
+            out["mode"] = _clean(modes.iloc[0]) if not modes.empty else None
         elif not is_numeric:
             out[m] = None
         elif m == "mean":

src/tools/analytics/quality.py

@@ -22,7 +22,13 @@ from src.tools.analytics.descriptive import ColumnNotFoundError
 
 
 def _clean(value: object) -> object:
-    """Convert numpy scalars to plain Python so the output is JSON-clean."""
+    """Convert numpy/pandas scalars to plain Python so the output is JSON-clean.
+
+    `top_value` (most frequent value) can be a `pandas.Timestamp` when profiling
+    a datetime column — neither `Timestamp` nor numpy scalars are JSON-safe.
+    """
+    if isinstance(value, pd.Timestamp):
+        return value.isoformat()
     if hasattr(value, "item"):
         return value.item()
     return value