[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/alerts_agent/api/schemas.py

@@ -0,0 +1,111 @@
+"""
+Request/Response Schemas fuer Alerts Agent API.
+"""
+
+from datetime import datetime
+from typing import Optional
+from pydantic import BaseModel, Field
+
+
+# ============================================================================
+# Request Models
+# ============================================================================
+
+class AlertIngestRequest(BaseModel):
+    """Request fuer manuelles Alert-Import."""
+    title: str = Field(..., min_length=1, max_length=500)
+    url: str = Field(..., min_length=1)
+    snippet: Optional[str] = Field(default=None, max_length=2000)
+    topic_label: str = Field(default="Manual Import")
+    published_at: Optional[datetime] = None
+
+
+class AlertRunRequest(BaseModel):
+    """Request fuer Scoring-Pipeline."""
+    limit: int = Field(default=50, ge=1, le=200)
+    skip_scored: bool = Field(default=True)
+
+
+class FeedbackRequest(BaseModel):
+    """Request fuer Relevanz-Feedback."""
+    alert_id: str
+    is_relevant: bool
+    reason: Optional[str] = None
+    tags: list[str] = Field(default_factory=list)
+
+
+class ProfilePriorityRequest(BaseModel):
+    """Priority fuer Profile-Update."""
+    label: str
+    weight: float = Field(default=0.5, ge=0.0, le=1.0)
+    keywords: list[str] = Field(default_factory=list)
+    description: Optional[str] = None
+
+
+class ProfileUpdateRequest(BaseModel):
+    """Request fuer Profile-Update."""
+    priorities: Optional[list[ProfilePriorityRequest]] = None
+    exclusions: Optional[list[str]] = None
+    policies: Optional[dict] = None
+
+
+# ============================================================================
+# Response Models
+# ============================================================================
+
+class AlertIngestResponse(BaseModel):
+    """Response fuer Alert-Import."""
+    id: str
+    status: str
+    message: str
+
+
+class AlertRunResponse(BaseModel):
+    """Response fuer Scoring-Pipeline."""
+    processed: int
+    keep: int
+    drop: int
+    review: int
+    errors: int
+    duration_ms: int
+
+
+class InboxItem(BaseModel):
+    """Ein Item in der Inbox."""
+    id: str
+    title: str
+    url: str
+    snippet: Optional[str]
+    topic_label: str
+    published_at: Optional[datetime]
+    relevance_score: Optional[float]
+    relevance_decision: Optional[str]
+    relevance_summary: Optional[str]
+    status: str
+
+
+class InboxResponse(BaseModel):
+    """Response fuer Inbox-Abfrage."""
+    items: list[InboxItem]
+    total: int
+    page: int
+    page_size: int
+
+
+class FeedbackResponse(BaseModel):
+    """Response fuer Feedback."""
+    success: bool
+    message: str
+    profile_updated: bool
+
+
+class ProfileResponse(BaseModel):
+    """Response fuer Profile."""
+    id: str
+    priorities: list[dict]
+    exclusions: list[str]
+    policies: dict
+    total_scored: int
+    total_kept: int
+    total_dropped: int
+    accuracy_estimate: Optional[float]