fix(telegram): 修正 EA 通知格式與 Agent 名稱問題

- 禁止 Gemini 音譯 Agent 名稱（赫瑪斯→Hermes, 內莫特朗→NemoTron）
- _AGENT_ZH 改為 _AGENT_LABEL，保留英文原名
- orchestrator system/user prompt 強制 reasoning 必須含具體數字
- _notify_telegram_executed 改為直接組裝訊息，顯示效益/依據/步驟
- _escalate_to_human 使用 _AGENT_LABEL 替換 _AGENT_ZH

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

services/elephant_alpha_autonomous_engine.py

@@ -80,20 +80,27 @@ _TRIGGER_ZH = {
     "weekly_insight": "全景電商洞察分析",
 }
 
-_AGENT_ZH = {
-    "hermes": "Hermes 分析師",
-    "nemotron": "NemoTron 監控",
-    "openclaw": "OpenClaw 策略師",
-    "scheduler": "排程器",
-    "?": "未知模組",
+# Agent 名稱保留英文，僅補上角色說明（禁止音譯）
+_AGENT_LABEL = {
+    "hermes":    "Hermes",
+    "nemotron":  "NemoTron",
+    "openclaw":  "OpenClaw",
+    "elephant_alpha": "Elephant Alpha",
+    "scheduler": "Scheduler",
 }
 
 _ACTION_ZH = {
-    "analyze_price_competition": "競品價格分析",
-    "dispatch_alert": "派送告警通知",
-    "generate_strategic_analysis": "產出策略分析報告",
-    "adjust_price": "調整定價",
-    "send_alert": "發送告警",
+    "analyze_price_competition":   "競品價格分析",
+    "dispatch_alert":              "派送告警通知",
+    "dispatch_price_updates":      "派送定價更新",
+    "dispatch_price_update":       "派送定價更新",
+    "generate_strategic_analysis": "產出策略分析",
+    "generate_weekly_strategy":    "產出全景週報",
+    "generate_market_analysis":    "市場分析",
+    "generate_pricing_strategy":   "定價策略建議",
+    "generate_meta_analysis":      "AI 系統自我審視",
+    "adjust_price":                "調整定價",
+    "send_alert":                  "發送告警",
 }
 
 
@@ -102,9 +109,13 @@ def _zh_trigger(trigger_type: str) -> str:
 
 
 def _zh_step(step: dict) -> str:
-    agent = _AGENT_ZH.get(step.get("agent", "?"), step.get("agent", "?"))
-    action = _ACTION_ZH.get(step.get("action", ""), step.get("action", "?"))
-    return f"[{agent}] {action}"
+    agent_key = step.get("agent", "?").lower()
+    agent = _AGENT_LABEL.get(agent_key, step.get("agent", "?"))
+    action = _ACTION_ZH.get(step.get("action", ""), step.get("action", ""))
+    desc = step.get("description", "")
+    # 優先用 description（Gemini 生成的繁中說明），其次用 action 中文對照
+    detail = desc if desc else action
+    return f"[{agent}] {detail}"
 
 
 @dataclass
@@ -563,17 +574,23 @@ class ElephantAlphaAutonomousEngine:
         trigger: AutonomousTrigger,
     ) -> None:
         try:
-            from services.telegram_templates import triaged_alert, _send_telegram_raw
-            msg, keyboard = triaged_alert(
-                base_event={
-                    "event_type": trigger.trigger_type,
-                    "title": f"🐘 EA 自主執行完畢 · {_zh_trigger(trigger.trigger_type)}",
-                    "summary": decision.expected_outcome or "EA 完成自主決策",
-                    "id": f"ea_{trigger.trigger_type}_{int(datetime.now().timestamp())}",
-                },
-                tier_label="🐘 Elephant Alpha · 自主執行",
-                ai_summary=(decision.reasoning or "")[:300],
-                ai_executed=[_zh_step(s) for s in decision.execution_plan[:5]] or ["（無具體執行計畫）"],
+            from services.telegram_templates import _send_telegram_raw
+
+            trigger_zh = _zh_trigger(trigger.trigger_type)
+            steps = [_zh_step(s) for s in decision.execution_plan[:5]] or ["（無執行步驟）"]
+            steps_text = "\n".join(f"  • {s}" for s in steps)
+
+            # reasoning 必須含數據；若只是空泛摘要則標記為「待補充」
+            reasoning = (decision.reasoning or "").strip()
+            if len(reasoning) < 30:
+                reasoning = "（AI 推理未提供足夠細節）"
+
+            msg = (
+                f"<b>⚡ 🐘 Elephant Alpha · 自主執行 · {trigger.trigger_type}</b>\n"
+                f"📌 <b>{trigger_zh}</b>\n\n"
+                f"🔍 <b>預期效益：</b>{(decision.expected_outcome or '').strip()}\n\n"
+                f"🧠 <b>決策依據：</b>{reasoning[:400]}\n\n"
+                f"✅ <b>已執行（信心 {decision.confidence:.0%}）：</b>\n{steps_text}"
             )
             await self._run_with_timeout(_send_telegram_raw, msg, timeout=10)
             self._log.info("Telegram audit sent: %s", trigger.trigger_type)
@@ -633,7 +650,7 @@ class ElephantAlphaAutonomousEngine:
                     ai_cause=(
                         f"觸發類型：{_zh_trigger(trigger.trigger_type)} | "
                         f"信心度：{decision.confidence:.2f} | "
-                        f"參與模組：{', '.join(_AGENT_ZH.get(a, a) for a in decision.agents_required)}"
+                        f"參與模組：{', '.join(_AGENT_LABEL.get(a.lower(), a) for a in decision.agents_required)}"
                     ),
                     ai_actions=[
                         f"步驟 {s.get('step', i+1)}：{_zh_step(s)}"

services/elephant_alpha_orchestrator.py

@@ -100,7 +100,11 @@ class ElephantAlphaOrchestrator:
         """Build comprehensive system prompt for Elephant Alpha"""
         return f"""You are Elephant Alpha, the Super Orchestrator for momo-pro-system e-commerce AI platform.
 
-重要語言規定：你的 JSON 回應中所有文字欄位（strategic_assessment、reasoning、expected_outcome、execution_plan 的 description、risk_factors、contingency_plans）必須使用繁體中文（台灣用語）撰寫。嚴禁使用英文或簡體中文。
+重要語言規定：
+1. 所有文字欄位（strategic_assessment、reasoning、expected_outcome、execution_plan 的 description、risk_factors、contingency_plans）必須使用繁體中文（台灣用語）撰寫。
+2. 【禁止翻譯 Agent 名稱】Hermes、NemoTron、OpenClaw、Elephant Alpha 是專有名詞，必須保留英文原名，嚴禁音譯（禁止：赫瑪斯、內莫特朗、開爪等）。
+3. reasoning 欄位必須包含具體數字（如：競品價差 X%、SKU 數量 N 個、業績跌幅 X%），嚴禁使用空泛企業用語（如「提升轉化率」、「擴大利潤邊際」等無數據支撐的說法）。
+4. expected_outcome 必須說明具體預期指標（如：預計 48h 內恢復 N 個 SKU 競爭力、降低平均價差至 X%）。
 
 CURRENT ARCHITECTURE:
 - You coordinate 3 specialized AI agents: Hermes (Analyst), NemoTron (Dispatcher), OpenClaw (Strategist)
@@ -264,7 +268,7 @@ Based on the current business context and system state, determine the optimal st
 
 Provide your strategic decision in the specified JSON format.
 
-重要：所有 JSON 文字欄位必須使用繁體中文（台灣用語）回覆，嚴禁英文或簡體中文。
+重要：所有 JSON 文字欄位必須使用繁體中文（台灣用語）。Hermes、NemoTron、OpenClaw、Elephant Alpha 保留英文原名勿翻譯。reasoning 必須引用上方數據中的具體數字。
 """
         return prompt