refactor(services): one periodic-task shape, one APScheduler job shape, one token hash, one summary rule — the services pass of the shape audit (#2830, milestone 296)

- background.start_periodic(interval, work, label=) replaces the three hand-rolled
  while-True/sleep/try loops in logging, auth and notifications.
- services/scheduler.ScheduledJob replaces the four private BackgroundScheduler
  copies in recurrence/version_pinning/trash/db_maintenance schedulers; public
  start_/stop_/reschedule_ surfaces unchanged.
- api_keys.hash_token is the one sha256 helper; auth.py used to inline it 5x.
- auth.is_registration_open reads via settings.get_admin_setting; notification
  prefs read via settings.get_setting; _fire_share_email uses _get_user_email.
- projects.get_project_summary / milestones.get_project_milestone_summary are
  now the one-id view of their batch siblings instead of a second copy of the
  queries; sharing.best_permission_by (was _deduplicate_by_permission) is the
  one rank-dedup, now also used by list_projects_for_user.
- backup: the row builders for every section both exporters carry are named
  functions, so a column added to one export cannot silently miss the other.
- iso() from models.base replaces the attr.isoformat()-if-attr-else-None idiom
  and db_maintenance._iso across services; backup keeps its explicit shape.
- trash.py hoists the sql_delete/timedelta imports it re-imported per function.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

src/scribe/services/trash.py

@@ -8,15 +8,16 @@ trashed rows via `alive()`.
 from __future__ import annotations
 
 import uuid
-from datetime import datetime, timezone
+from datetime import datetime, timedelta, timezone
 
-from sqlalchemy import or_, select, update
+from sqlalchemy import delete as sql_delete, or_, select, update
 
 from scribe.models import async_session
 from scribe.models.note import Note
 from scribe.models.project import Project
 from scribe.models.milestone import Milestone
 from scribe.models.rulebook import Rulebook, RulebookTopic, Rule
+from scribe.models.base import iso
 
 # entity_type -> Model. Used to resolve which table a trash op targets.
 _MODEL_FOR = {
@@ -87,16 +88,15 @@ async def _cascade(session, user_id: int, etype: str, eid: int, batch: str, now)
         # FK CASCADE would handle a full DELETE on the project row, but the
         # soft-delete path keeps the project row alive; this guarantees the
         # rows are gone whether or not the project ever gets purged.
-        from sqlalchemy import delete as _sql_delete
         from scribe.models.rulebook import (
             project_rule_suppressions, project_topic_suppressions,
         )
         await session.execute(
-            _sql_delete(project_rule_suppressions)
+            sql_delete(project_rule_suppressions)
             .where(project_rule_suppressions.c.project_id == eid)
         )
         await session.execute(
-            _sql_delete(project_topic_suppressions)
+            sql_delete(project_topic_suppressions)
             .where(project_topic_suppressions.c.project_id == eid)
         )
         await _set(session, Project, [Project.user_id == user_id, Project.id == eid], batch, now)
@@ -213,7 +213,6 @@ async def restore_entity(user_id: int, entity_type: str, entity_id: int) -> int
 
 async def purge(user_id: int, batch_id: str) -> int:
     """Hard-delete every row in the batch. Irreversible."""
-    from sqlalchemy import delete as sql_delete
     n = 0
     async with async_session() as session:
         for model in _ALL:
@@ -241,7 +240,7 @@ async def list_trash(user_id: int) -> list[dict]:
                 grp = batches.setdefault(
                     r.deleted_batch_id,
                     {"batch_id": r.deleted_batch_id,
-                     "deleted_at": r.deleted_at.isoformat() if r.deleted_at else None,
+                     "deleted_at": iso(r.deleted_at),
                      "items": []},
                 )
                 grp["items"].append({
@@ -265,8 +264,6 @@ async def purge_expired(user_id: int, retention_days: int) -> int:
     user's short window prematurely destroy another's data.
     retention_days <= 0 disables auto-purge (returns 0 without touching anything).
     """
-    from datetime import timedelta
-    from sqlalchemy import delete as sql_delete
     if retention_days <= 0:
         return 0
     cutoff = datetime.now(timezone.utc) - timedelta(days=retention_days)