[split-required] Split final 43 files (500-668 LOC) to complete refactoring

klausur-service (11 files):
- cv_gutter_repair, ocr_pipeline_regression, upload_api
- ocr_pipeline_sessions, smart_spell, nru_worksheet_generator
- ocr_pipeline_overlays, mail/aggregator, zeugnis_api
- cv_syllable_detect, self_rag

backend-lehrer (17 files):
- classroom_engine/suggestions, generators/quiz_generator
- worksheets_api, llm_gateway/comparison, state_engine_api
- classroom/models (→ 4 submodules), services/file_processor
- alerts_agent/api/wizard+digests+routes, content_generators/pdf
- classroom/routes/sessions, llm_gateway/inference
- classroom_engine/analytics, auth/keycloak_auth
- alerts_agent/processing/rule_engine, ai_processor/print_versions

agent-core (5 files):
- brain/memory_store, brain/knowledge_graph, brain/context_manager
- orchestrator/supervisor, sessions/session_manager

admin-lehrer (5 components):
- GridOverlay, StepGridReview, DevOpsPipelineSidebar
- DataFlowDiagram, sbom/wizard/page

website (2 files):
- DependencyMap, lehrer/abitur-archiv

Other: nibis_ingestion, grid_detection_service, export-doclayout-onnx

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

backend-lehrer/generators/quiz_helpers.py

@@ -0,0 +1,70 @@
+"""
+Quiz Helpers - Text-Verarbeitungs-Hilfsfunktionen fuer Quiz-Generierung.
+"""
+
+import re
+from typing import List, Tuple
+
+
+def extract_factual_sentences(text: str) -> List[str]:
+    """Extrahiert Fakten-Sätze aus dem Text."""
+    sentences = re.split(r'[.!?]+', text)
+    factual = []
+
+    for sentence in sentences:
+        sentence = sentence.strip()
+        if len(sentence) > 20 and '?' not in sentence:
+            factual.append(sentence)
+
+    return factual
+
+
+def negate_sentence(sentence: str) -> str:
+    """Negiert eine Aussage einfach."""
+    words = sentence.split()
+    if len(words) > 2:
+        for i, word in enumerate(words):
+            if word.endswith(('t', 'en', 'st')) and i > 0:
+                words.insert(i + 1, 'nicht')
+                break
+    return ' '.join(words)
+
+
+def extract_definitions(text: str) -> List[Tuple[str, str]]:
+    """Extrahiert Begriff-Definition-Paare."""
+    definitions = []
+    patterns = [
+        r'(\w+)\s+ist\s+(.+?)[.]',
+        r'(\w+)\s+bezeichnet\s+(.+?)[.]',
+        r'(\w+)\s+bedeutet\s+(.+?)[.]',
+        r'(\w+):\s+(.+?)[.]',
+    ]
+
+    for pattern in patterns:
+        matches = re.findall(pattern, text)
+        for term, definition in matches:
+            if len(definition) > 10:
+                definitions.append((term, definition.strip()))
+
+    return definitions
+
+
+def extract_sequence(text: str) -> List[str]:
+    """Extrahiert eine Sequenz von Schritten."""
+    steps = []
+    numbered = re.findall(r'\d+[.)]\s*([^.]+)', text)
+    steps.extend(numbered)
+
+    signal_words = ['zuerst', 'dann', 'danach', 'anschließend', 'schließlich']
+    for word in signal_words:
+        pattern = rf'{word}\s+([^.]+)'
+        matches = re.findall(pattern, text, re.IGNORECASE)
+        steps.extend(matches)
+
+    return steps
+
+
+def extract_keywords(text: str) -> List[str]:
+    """Extrahiert Schlüsselwörter."""
+    words = re.findall(r'\b[A-ZÄÖÜ][a-zäöüß]+\b', text)
+    return list(set(words))[:5]