fix(prod): 修復四個生產致命 bug — outcome 寫入 / OpenClaw / Telegram 通知 / LLM 規則顯示

1. decision_manager: 移除 UPDATE incidents 中的 verification_result 欄位
   （incidents 表無此欄位 → 所有 outcome 寫入失敗 outcome_write_failed）

2. failure_watcher: get_openclaw_service → get_openclaw
   （函數名錯誤 → OpenClaw 分析全部 ImportError 崩潰）

3. failure_watcher: tg.send_message → tg.send_notification
   （TelegramGateway 無 send_message 方法 → 修復通知無法送出）

4. decision_manager: expert_analyze 補齊 initial_diagnosis / diagnosis_description key
   （openclaw.py 讀這兩個 key，但 expert_analyze 只有 matched_rule / description
    → LLM 永遠看到 Matched Rule=unknown，無法正確分析）

2026-04-15 ogt + Claude Sonnet 4.6（亞太）: 生產緊急修復

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

apps/api/src/services/decision_manager.py

@@ -842,15 +842,11 @@ async def _push_auto_repair_result(
             from src.db.models import IncidentRecord
             from sqlalchemy import update as _upd_outcome
             _outcome = "auto_repaired" if success else "auto_repair_failed"
-            _verification = (
-                f"自動修復{'成功' if success else '失敗'}：{action[:120] if action else '未知'}"
-                + (f" | 錯誤：{error[:80]}" if error else "")
-            )
             async with get_db_context() as _odb:
                 await _odb.execute(
                     _upd_outcome(IncidentRecord)
                     .where(IncidentRecord.incident_id == inc_id)
-                    .values(outcome=_outcome, verification_result=_verification)
+                    .values(outcome=_outcome)
                 )
                 await _odb.commit()
             logger.info("outcome_written", incident_id=inc_id, outcome=_outcome)
@@ -1018,13 +1014,15 @@ def expert_analyze(incident: Incident) -> dict[str, Any]:
         "source": "expert_system",
         "action": rule["action"].format(target=target),
         "description": rule["description"],
+        "diagnosis_description": rule["description"],   # openclaw.py reads this key
         "risk_level": rule["risk_level"],
         "reasoning": f"[規則匹配] {rule['reasoning']}",  # 明確標示來源
         "confidence": 0.0,  # 🔴 規則匹配不是 AI 仲裁，信心度設 0
         "kubectl_command": rule["action"].format(target=target),
         "matched_rule": matched_rule,
+        "initial_diagnosis": matched_rule,   # openclaw.py reads this key
         "from_cache": False,
-        "is_rule_based": True,  # 新增標記
+        "is_rule_based": True,
     }