[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/suggest_frame.dart

@@ -0,0 +1,83 @@
+import '../../data/ai/llm_service.dart';
+import '../frame/validate_frame_level.dart';
+import '../models/frame_pattern.dart';
+import 'few_shot_builder.dart';
+import 'frame_candidate.dart';
+import 'parse_response.dart';
+
+/// JSON schema (function-calling parameters) for the model output.
+const Map<String, dynamic> kFrameCandidatesSchema = {
+  'type': 'object',
+  'properties': {
+    'candidates': {
+      'type': 'array',
+      'minItems': 1,
+      'maxItems': 3,
+      'items': {
+        'type': 'object',
+        'properties': {
+          'level': {'type': 'string', 'enum': ['L2', 'L3']},
+          'framed_text': {'type': 'string', 'maxLength': 120},
+          'confidence': {'type': 'number', 'minimum': 0, 'maximum': 1},
+          'source_pattern_id': {'type': 'string'},
+        },
+        'required': ['level', 'framed_text'],
+      },
+    },
+  },
+  'required': ['candidates'],
+};
+
+const Duration _defaultTimeout = Duration(seconds: 10);
+const int _maxRawTextLength = 200;
+
+/// Main domain function (#215 §A). Pure-ish: depends only on injected
+/// [llm] and [framePatterns]. Never throws — failure returns an empty list
+/// so callers (UI) can decide messaging (graceful degradation, AC-9).
+Future<List<FrameCandidate>> suggestFrame(
+  SuggestFrameInput input, {
+  required LlmService llm,
+  required List<FramePatternModel> framePatterns,
+  Duration timeout = _defaultTimeout,
+}) async {
+  final raw = input.rawText.trim();
+  if (raw.isEmpty || raw.length > _maxRawTextLength) {
+    return const [];
+  }
+
+  final prompt = buildFewShotPrompt(input, framePatterns);
+
+  Map<String, dynamic> json;
+  try {
+    json = await llm
+        .generateStructured(prompt, kFrameCandidatesSchema)
+        .timeout(timeout);
+  } catch (_) {
+    // Timeout / StateError / FormatException / anything else: graceful.
+    return const [];
+  }
+
+  List<FrameCandidate> candidates;
+  try {
+    candidates = parseFrameCandidates(json);
+  } on FormatException {
+    return const [];
+  }
+
+  // Drop L0/L1 + avoidance-violating via validateFrameLevel.
+  final validated = <FrameCandidate>[];
+  for (final c in candidates) {
+    final result = validateFrameLevel(
+      FrameInput(
+        level: c.level,
+        framedText: c.framedText,
+        originalText: input.rawText,
+      ),
+      knownPatterns: framePatterns,
+    );
+    if (result.status == FrameValidationStatus.reject) continue;
+    validated.add(c);
+    if (validated.length >= 3) break;
+  }
+  return validated;
+}