[split-required] Split 500-850 LOC files (batch 2)

backend-lehrer (10 files):
- game/database.py (785 → 5), correction_api.py (683 → 4)
- classroom_engine/antizipation.py (676 → 5)
- llm_gateway schools/edu_search already done in prior batch

klausur-service (12 files):
- orientation_crop_api.py (694 → 5), pdf_export.py (677 → 4)
- zeugnis_crawler.py (676 → 5), grid_editor_api.py (671 → 5)
- eh_templates.py (658 → 5), mail/api.py (651 → 5)
- qdrant_service.py (638 → 5), training_api.py (625 → 4)

website (6 pages):
- middleware (696 → 8), mail (733 → 6), consent (628 → 8)
- compliance/risks (622 → 5), export (502 → 5), brandbook (629 → 7)

studio-v2 (3 components):
- B2BMigrationWizard (848 → 3), CleanupPanel (765 → 2)
- dashboard-experimental (739 → 2)

admin-lehrer (4 files):
- uebersetzungen (769 → 4), manager (670 → 2)
- ChunkBrowserQA (675 → 6), dsfa/page (674 → 5)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

backend-lehrer/classroom_engine/antizipation_models.py

@@ -0,0 +1,93 @@
+"""
+Antizipation Engine - Data models, enums, and signal container.
+"""
+
+from dataclasses import dataclass, field
+from datetime import datetime
+from typing import List, Dict, Any, Optional
+from enum import Enum
+
+
+# ==================== Enums & Types ====================
+
+class SuggestionTone(str, Enum):
+    """Ton/Dringlichkeit eines Vorschlags."""
+    HINT = "hint"  # Sanfter Hinweis
+    SUGGESTION = "suggestion"  # Aktiver Vorschlag
+    REMINDER = "reminder"  # Erinnerung
+    URGENT = "urgent"  # Dringend
+
+
+class ContextType(str, Enum):
+    """Typ eines aktiven Kontexts."""
+    EVENT_WINDOW = "event_window"  # Event steht bevor
+    ROUTINE = "routine"  # Routine heute
+    PHASE = "phase"  # Makro-Phase bedingt
+    TIME = "time"  # Zeitbasiert (Ferien, Wochenende)
+
+
+@dataclass
+class Signal:
+    """Ein einzelnes Signal aus einer Quelle."""
+    name: str
+    value: Any
+    source: str  # "calendar", "usage", "events", "routines"
+
+
+@dataclass
+class ActiveContext:
+    """Ein aktiver Kontext der Vorschlaege beeinflusst."""
+    id: str
+    context_type: ContextType
+    label: str
+    data: Dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass
+class Suggestion:
+    """Ein generierter Vorschlag."""
+    id: str
+    title: str
+    description: str
+    tone: SuggestionTone
+    action_url: Optional[str] = None
+    badge: Optional[str] = None  # z.B. "in 7 Tagen"
+    priority: int = 50  # 0-100, hoeher = wichtiger
+    rule_id: str = ""
+    icon: str = "lightbulb"
+
+
+@dataclass
+class Signals:
+    """Container fuer alle gesammelten Signale."""
+    # Zeit/Kalender
+    current_week: int = 1
+    weeks_since_start: int = 0
+    is_weekend: bool = False
+    is_before_holidays: bool = False
+    days_until_holidays: int = 999
+
+    # Makro-Phase
+    macro_phase: str = "onboarding"
+    onboarding_completed: bool = False
+
+    # Produktnutzung
+    classes_count: int = 0
+    has_classes: bool = False
+    has_schedule: bool = False
+
+    # Events
+    exams_scheduled_count: int = 0
+    exams_in_7_days: List[Dict] = field(default_factory=list)
+    exams_past_ungraded: List[Dict] = field(default_factory=list)
+    upcoming_events: List[Dict] = field(default_factory=list)
+    trips_in_30_days: List[Dict] = field(default_factory=list)
+    parent_evenings_soon: List[Dict] = field(default_factory=list)
+
+    # Routinen
+    routines_today: List[Dict] = field(default_factory=list)
+    has_conference_today: bool = False
+
+    # Statistiken (aus Analytics)
+    corrections_pending: int = 0
+    grades_completion_ratio: float = 0.0