keyword cleanup: v2 engine, alias rule layer, LLM semantic suggestions

- build_review_bundle.py: 新增 _compute_percentile/_compute_growth,
  候选池从固定阈值改为百分位排名 + 增速因子 (v2 policy)
- term_cleanup_policy.json: 升级 v2 schema
- generate_term_cleanup_suggestions.py: 新增 _prepare_alias_suggestions,
  规则层输出 alias (大小写/单复数/分词变体)
- generate_term_cleanup_semantic_suggestions.py: 新增 LLM 语义建议脚本
  (DeepSeek API, 产出 semantic alias/stopword/promote)
- SKILL.md: 更新为 5 Phase 工作流程
- 首轮清洗 apply: interest 54, aliases 17组, stopwords 17个
- docs/design/keyword-cleanup-flow-overview.md: 流程文档
- plans/: 引擎设计方案

configs/term_cleanup_policy.json

@@ -1,14 +1,12 @@
 {
-  "schema_version": "v1",
+  "schema_version": "v2",
   "interest_keyword_review": {
-    "min_total_count": 3,
-    "min_days_seen": 2
+    "percentile_max": 0.05,
+    "growth_promotion": 0.5
   },
   "watch_term_review": {
-    "min_total_count": 1,
-    "min_days_seen": 1,
-    "max_total_count": 2,
-    "max_days_seen": 2
+    "percentile_min": 0.05,
+    "percentile_max": 0.20
   },
   "alias_review": {
     "min_total_count": 2,
@@ -19,7 +17,10 @@
     "max_days_seen": 2
   },
   "notes": [
-    "当前阶段采用保守阈值，避免在低样本条件下直接扩充 interest_keywords。",
-    "watch_terms 先用于观察，后续再决定是否升格为 interest_keywords 或进入 alias/stopword 配置。"
+    "v2: interest/watch 使用百分位排名 + 增速因子替代固定阈值",
+    "percentile 越小表示排名越高（top 5% = percentile 0.05）",
+    "growth = recent_count / total_count，衡量近期活跃度",
+    "watch_term_review 的 percentile_min 可理解为兴趣边界下限，低于此值的词归入 interest 候选",
+    "growth_promotion（默认 0.5）用于识别近期集中爆发词，即使排位不高也主动推荐确认"
   ]
-}
+}