feat(tier2): finish the finish line — Block H follow-ups, Block G geolocation, cross-cutting

Three threads of work land here together to close out Tier 2.

### Block H follow-ups — day-of check-in
- Scanner is now an "open on your phone" magic-link flow. Hosts on
  desktop mint a scoped JWT via POST /events/{id}/scanner-ticket and
  render its URL into a QR; phone scans it and lands on /scanner with
  the ticket as bearer. The ticket carries Audience=scanner so it can
  never substitute for a session token.
- Plus-one confirmation at the door: scan → POST /check-in/preview to
  fetch guest + expected party size → confirm buttons ("Just them",
  "Party of N", custom) → POST /check-in. No more silent arrival_count=1.
- Offline scan queue: failed POSTs go into an IndexedDB store and drain
  on the 'online' event with poison-message protection.
- Day-of arrivals headline widget on the event overview, gated to the
  host's local calendar date so it doesn't dominate the page weeks out.
- Tab nav restyled with inline heroicons + scrollable segmented control;
  Check-in moves to the rightmost slot.
- PWA: manifest + service worker scoped to /scanner, generated 192/512
  icons (Go scripted renderer in scripts/gen-scanner-icons.go).
- Confirmation email QR was rendering broken because html/template
  rewrites data: URLs to #ZgotmplZ; mark the value as template.URL.
- Email "open your invitation" link 404'd because we had no token to
  put after /rsvp/. Threaded AccessLink through the RSVPConfirmed NATS
  event from the API at submit time.

### Block G remainder — geolocation + threshold preview
- Pluggable GeoResolver in the fraud engine (NullResolver, IPApiResolver
  for the free ip-api.com fallback, MaxMindResolver behind GG_GEOIP_DB_PATH).
  Wrapped in a Redis cache (30d TTL). Geo flows through both gRPC and
  NATS scoring paths.
- geo_jump scoring feature: >500km in <1h flags ("accessed from Lagos
  and Paris within 12 minutes"); >500km in <6h is a softer signal. The
  existing single-signal cap keeps a lone geo_jump in MEDIUM.
- FraudScored event carries geo_country/city/lat/lon; ApplyScore uses
  COALESCE so a later re-score without geo doesn't wipe earlier data.
- Threshold-slider live preview: GET /events/{id}/security/thresholds/preview
  returns band counts the host's existing access events would have
  fallen into under the proposed thresholds. Debounced (250ms) widget
  under the Advanced sliders so the host gets concrete feedback instead
  of guessing.

### Cross-cutting — audit, tier-gating, feature flags
- audit_log table + internal/audit.Recorder (async fire-and-forget on
  detached context so an audit blip never fails the real action). Wired
  into branding update, thresholds update, allowlist add/remove,
  collaborator invite/role-change/remove, message create/send-now/cancel.
- Tier-gating: extended billing.Limits with MaxCollaborators,
  CustomBranding, Scanner, Broadcasts. Free = none; Pro = 5 + all;
  Business = unlimited. Gates the scanner-ticket, message create,
  branding put, and collaborator invite endpoints with 402 +
  structured upgrade payload. Auto-reminders, fraud detection, and
  analytics deliberately stay on every tier — those are safety + visibility
  features, not upsell levers.
- Feature flags: feature_flags table + internal/flags.Store with 30s
  in-memory refresh, stable sha256(key + user_id) percent bucketing,
  unknown-key-defaults-on. Six Tier 2 flags pre-seeded. Three handlers
  (branding, broadcasts, scanner) check the kill switch ahead of the
  tier gate so ops can pull a feature back without a redeploy.

### Verified
- go test ./... + fraud-engine pytest (12/12 incl. 3 new geo_jump tests + 5
  new flags tests).
- docker compose build + up across api, fraud-engine, notifier, frontend.
- /health endpoints 200; migrations 0014 + 0015 applied; 6 flags
  seeded; audit_log table + partial indexes confirmed.
- Fraud-engine logs confirm geo resolver kind=CachedGeoResolver provider=auto.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

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

fraud-engine/app/scoring.py

@@ -9,10 +9,13 @@ baseline established by the first one.
 from __future__ import annotations
 
 import hashlib
+import math
 from dataclasses import dataclass, field
+from datetime import datetime
 from typing import Any
 from uuid import UUID
 
+from app.geo import GeoLocation
 from app.schemas import AccessAttempted
 
 LOW = "low"
@@ -36,12 +39,20 @@ class GuestBaseline:
     fingerprint_digest: str | None = None
     ip_prefix: str | None = None
     accesses: int = 0
+    # Tier 2 Block G — geo_jump. Stash the most recent coordinates + the
+    # timestamp of the access they came from so the next access can be
+    # compared against them.
+    last_lat: float | None = None
+    last_lon: float | None = None
+    last_geo_at: datetime | None = None
+    last_country: str | None = None
 
 
 @dataclass
 class ScoringResult:
     score: int
     reasons: list[str]
+    geo: GeoLocation | None = None
 
 
 @dataclass
@@ -53,11 +64,17 @@ class HeuristicScorer:
             "missing_signals": 0.10,
             "repeated_access": 0.10,
             "no_user_agent": 0.15,
+            # Tier 2 Block G — geo_jump. Implausibly fast travel between
+            # two accesses (>500 km in <1h) carries the heaviest weight
+            # alongside fingerprint mismatch. Note the weights are not
+            # required to sum to 1; the final score is clamped to
+            # [0, 100] so the relative magnitudes are what matters.
+            "geo_jump": 0.40,
         }
     )
     baselines: dict[UUID, GuestBaseline] = field(default_factory=dict)
 
-    def score(self, evt: AccessAttempted) -> ScoringResult:
+    def score(self, evt: AccessAttempted, geo: GeoLocation | None = None) -> ScoringResult:
         reasons: list[str] = []
         sub: dict[str, int] = {}
 
@@ -97,7 +114,37 @@ class HeuristicScorer:
         else:
             sub["no_user_agent"] = 0
 
-        weighted = sum(sub[k] * self.weights[k] for k in self.weights)
+        # geo_jump — implausibly fast travel between this access and the
+        # previous one. 500 km in under an hour means either a private
+        # jet or, far more likely, a stolen invitation being opened by
+        # someone in a different country. Spec threshold from
+        # docs/TIER2_PLAN.md Block G.
+        sub["geo_jump"] = 0
+        if (
+            geo is not None
+            and geo.lat is not None
+            and geo.lon is not None
+            and baseline.last_lat is not None
+            and baseline.last_lon is not None
+            and baseline.last_geo_at is not None
+        ):
+            km = _haversine_km(
+                baseline.last_lat, baseline.last_lon, geo.lat, geo.lon
+            )
+            dt = (evt.occurred_at - baseline.last_geo_at).total_seconds()
+            if km > 500 and 0 < dt < 3600:
+                sub["geo_jump"] = 100
+                where_now = geo.city or geo.country or "elsewhere"
+                where_before = baseline.last_country or "another location"
+                mins = max(int(dt / 60), 1)
+                reasons.append(
+                    f"accessed from {where_before} and {where_now} within {mins} minutes"
+                )
+            elif km > 500 and dt < 21600:  # within 6h is still suspicious-but-possible
+                sub["geo_jump"] = 50
+                reasons.append(f"large geographic jump ({int(km)} km)")
+
+        weighted = sum(sub[k] * self.weights.get(k, 0) for k in sub)
         final = int(round(min(max(weighted, 0), 100)))
 
         # Tier 2 Block G — tighten the consecutive-fingerprint false
@@ -124,10 +171,24 @@ class HeuristicScorer:
             baseline.fingerprint_digest = current_digest
         if baseline.ip_prefix is None:
             baseline.ip_prefix = current_prefix
+        if geo is not None and geo.lat is not None and geo.lon is not None:
+            baseline.last_lat = geo.lat
+            baseline.last_lon = geo.lon
+            baseline.last_geo_at = evt.occurred_at
+            baseline.last_country = geo.city or geo.country
         baseline.accesses += 1
         self.baselines[evt.guest_id] = baseline
 
-        return ScoringResult(score=final, reasons=reasons)
+        return ScoringResult(score=final, reasons=reasons, geo=geo)
+
+
+def _haversine_km(lat1: float, lon1: float, lat2: float, lon2: float) -> float:
+    """Great-circle distance in kilometres. Earth radius 6371 km."""
+    rlat1, rlat2 = math.radians(lat1), math.radians(lat2)
+    dlat = math.radians(lat2 - lat1)
+    dlon = math.radians(lon2 - lon1)
+    a = math.sin(dlat / 2) ** 2 + math.cos(rlat1) * math.cos(rlat2) * math.sin(dlon / 2) ** 2
+    return 2 * 6371.0 * math.asin(math.sqrt(a))
 
 
 def _fingerprint_digest(fp: dict[str, Any] | None) -> str | None: