chore(lint): auto-fix 253 of 283 ruff issues (F401, I001, E402, RUF100, UP037, SIM105)

Mass ruff auto-fix:
  - ruff check --fix: 109 issues fixed (F401 unused imports,
    I001 unsorted imports, UP037 quoted annotations, SIM105
    suppressible exception, RUF100 unused-noqa)
  - ruff check --fix --unsafe-fixes: 22 additional issues
  - ruff format: 70 files reformatted
  - Manual pass: fix 16 misplaced import httpx lines
  - Manual pass: fix remaining E402 (import-after-docstring)

Result: 283 errors -> 30 errors.

The remaining 30 are real issues that need manual review:
  5 F401 unused-import (likely auto-generated stubs)
  5 F821 undefined-name (real bugs in code that references
    redis/pydantic/LLMRegistry without imports)
  3 BLE001 (the compliance LLM fallback is intentional; the
    other two are real)
  3 RUF012 mutable-class-default
  3 SIM105, 3 SIM117, 2 E722, 2 E741
  1 B007, 1 B025, 1 E402, 1 RUF200 (pyproject.toml issue)

Tests: 436/437 pass (1 pre-existing SSE sandbox failure).
format check + import sort: now clean.
make ci: still gated on the 30 remaining real issues.
Follow-up: triage the 30 issues file-by-file.

anomaly.py

@@ -82,7 +82,9 @@ class AnomalyDetector:
             if isinstance(v, (int, float)) and not isinstance(v, bool):
                 common.add(k)
         for r in records[1:]:
-            rkeys = {k for k, v in r.items() if isinstance(v, (int, float)) and not isinstance(v, bool)}
+            rkeys = {
+                k for k, v in r.items() if isinstance(v, (int, float)) and not isinstance(v, bool)
+            }
             common &= rkeys
         return list(common)
 
@@ -139,16 +141,14 @@ class AnomalyDetector:
         if current_dow in dow_values and len(dow_values[current_dow]) >= 2:
             dow_mean = statistics.mean(dow_values[current_dow])
             dow_stdev = (
-                statistics.stdev(dow_values[current_dow])
-                if len(dow_values[current_dow]) > 1
-                else 0
+                statistics.stdev(dow_values[current_dow]) if len(dow_values[current_dow]) > 1 else 0
             )
             if dow_stdev > 0 and abs((current - dow_mean) / dow_stdev) < 1.5:
                 return {
                     "seasonal_anomaly": False,
                     "seasonal_explanation": (
                         f"Value fits "
-                        f"{['Mon','Tue','Wed','Thu','Fri','Sat','Sun'][current_dow]} pattern"
+                        f"{['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'][current_dow]} pattern"
                     ),
                 }
         if context.get("is_promotional"):