feat: build core API, fraud engine, notifier, and frontend

Phase 1 — Core API (Go):
- Events, guests, tokens, RSVPs CRUD on PostgreSQL via pgx/v5
- HMAC-signed per-guest tokens with format validation
- Health endpoint with DB ping, slog JSON logging, graceful shutdown

Phase 2 — NATS + Fraud Engine:
- NATS JetStream pub/sub with explicit-ack consumers
- Python/FastAPI fraud engine with heuristic risk scoring
  (fingerprint mismatch, IP change, missing signals, repeated access)
- gRPC sync scoring with 250ms fail-open timeout
- Per-guest baseline tracking; risk bands low/medium/high/block

Phase 3 — Notifications + Frontend:
- Notification worker scaffolding (Twilio/SES stubs, retry/backoff)
- Nuxt 3 frontend with Tailwind dark theme + brand green
- Live monitor via WebSocket with auto-reconnect
- Activity history endpoint backfills monitor with RSVPs +
  scored access checks (including blocked attempts)

UX polish:
- Marketing-friendly landing page (hero mockup, how-it-works,
  features, use cases, testimonials, FAQ, final CTA)
- Animated layered card mockups on landing + new-event page
- Plus-ones stepper, RSVP status badges, filter buttons
- Friendly access-check labels (Verified/Review/Suspicious/Blocked)
- Dashboard hydration fix via ClientOnly wrapper

Infrastructure:
- docker-compose for full local dev (postgres, nats, api,
  fraud-engine, notifier, frontend)
- Multi-stage Dockerfiles, non-root UID 1000
- Integration tests with testcontainers-go

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

fraud-engine/app/schemas.py

@@ -0,0 +1,30 @@
+from datetime import datetime
+from typing import Any
+from uuid import UUID
+
+from pydantic import BaseModel, ConfigDict, Field
+
+
+class AccessAttempted(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    event_id: UUID
+    guest_id: UUID
+    token_id: UUID
+    access_log_id: UUID
+    fingerprint: dict[str, Any] | None = None
+    ip_address: str | None = None
+    user_agent: str | None = None
+    referrer: str | None = None
+    occurred_at: datetime
+
+
+class FraudScored(BaseModel):
+    event_id: UUID
+    guest_id: UUID
+    token_id: UUID
+    access_log_id: UUID
+    score: int = Field(ge=0, le=100)
+    risk: str
+    reasons: list[str]
+    scored_at: datetime