[Developer] #215 AI frame-suggest vertical slice (mock LlmService)

설계서대로 구현. flutter_gemma 실제 통합은 OQ-1 (모델 URL+SHA) 확정 후.
v1은 LlmService 추상 + ModelLifecycle (다운로드/SHA/purge) + Riverpod
providers + 다이얼로그 + Settings 화면까지. main.dart 가 MockLlmService 를
override 해 모든 경로가 graceful (suggest 결과는 빈 리스트).

추가:
- lib/data/ai/{llm_service,gemma_llm_service,model_lifecycle}.dart
- lib/domain/ai/{frame_candidate,few_shot_builder,parse_response,suggest_frame}.dart
- lib/state/ai_providers.dart (aiSettings + modelAvailability + frameSuggestions)
- lib/ui/screens/settings_screen.dart (opt-in 토글 + 모델 상태 표시)
- lib/ui/widgets/frame_suggestion_dialog.dart (후보 3개 카드 + 다시 시도)
- HabitCreateScreen: "AI 제안" 버튼 (opt-in + ready 일 때만 노출)
- MetaDao.remove(key) 추가 (purge 용)

테스트 31개 신규 추가 (총 62개 통과):
- test/domain/ai/{suggest_frame, few_shot_builder, parse_response}_test.dart
- test/data/ai/model_lifecycle_test.dart (download/SHA/purge/availability)

flutter analyze 0 issue, flutter build apk --debug 통과.

Refs #215

app/lib/domain/ai/parse_response.dart

@@ -0,0 +1,60 @@
+import '../models/habit.dart';
+import 'frame_candidate.dart';
+
+/// Parses function-calling JSON into FrameCandidate list. Throws
+/// [FormatException] on missing/invalid top-level shape; silently skips
+/// individual malformed items.
+List<FrameCandidate> parseFrameCandidates(Map<String, dynamic> json) {
+  final raw = json['candidates'];
+  if (raw == null) {
+    throw const FormatException('candidates missing');
+  }
+  if (raw is! List) {
+    throw const FormatException('candidates not array');
+  }
+
+  final result = <FrameCandidate>[];
+  for (final item in raw) {
+    if (item is! Map) continue;
+    final map = item.map((k, v) => MapEntry(k.toString(), v));
+
+    final levelStr = map['level'];
+    if (levelStr is! String) continue;
+    final level = _parseLevel(levelStr);
+    if (level == null) continue;
+
+    final framedText = map['framed_text'];
+    if (framedText is! String) continue;
+    final trimmed = framedText.trim();
+    if (trimmed.isEmpty || trimmed.length > 120) continue;
+
+    double confidence = 0.5;
+    final c = map['confidence'];
+    if (c is num) {
+      confidence = c.toDouble().clamp(0.0, 1.0);
+    }
+
+    final src = map['source_pattern_id'];
+    result.add(FrameCandidate(
+      level: level,
+      framedText: trimmed,
+      confidence: confidence,
+      sourcePatternId: src is String ? src : null,
+    ));
+  }
+  return result;
+}
+
+FrameLevel? _parseLevel(String s) {
+  switch (s.toUpperCase()) {
+    case 'L0':
+      return FrameLevel.l0;
+    case 'L1':
+      return FrameLevel.l1;
+    case 'L2':
+      return FrameLevel.l2;
+    case 'L3':
+      return FrameLevel.l3;
+  }
+  return null;
+}