diff --git a/api.py b/api.py
index 4791da5..a292d04 100644
--- a/api.py
+++ b/api.py
@@ -2410,6 +2410,10 @@ def dashboard_today(authorization: str | None = Header(default=None)):
         conn.close()
 
 
+# 场景中文映射(用于摘要)
+SCENE_LABELS_MAP = {"legal": "法务", "decision": "决策", "life": "生活", "emotion": "情绪"}
+
+
 @app.get("/dashboard/summary")
 def dashboard_summary(authorization: str | None = Header(default=None)):
     """老板台执行摘要:AI 用一句话总结当前状态和建议行动。
@@ -2443,20 +2447,31 @@ def dashboard_summary(authorization: str | None = Header(default=None)):
 
     red_count = sum(1 for m in open_matters if m["risk_level"] == "red")
     orange_count = sum(1 for m in open_matters if m["risk_level"] == "orange")
+    open_count = len(open_matters)
 
     # 规则摘要(兜底)
     parts = []
     if red_count > 0:
-        parts.append(f"有 {red_count} 项高风险事项需立即处理")
+        parts.append(f"{red_count} 项高风险需立即处理")
     if orange_count > 0:
-        parts.append(f"{orange_count} 项中风险事项需关注")
+        parts.append(f"{orange_count} 项中风险需关注")
     if pending_count > 0:
-        parts.append(f"{pending_count} 个事项待你确认")
+        parts.append(f"{pending_count} 个待确认")
     if reply_count > 0:
         parts.append(f"{reply_count} 条专家回复待查看")
+    # 有进行中的案卡但无风险标记
+    ongoing = open_count - red_count - orange_count
+    if ongoing > 0 and not parts:
+        # 按场景归类
+        scene_counts: dict[str, int] = {}
+        for m in open_matters:
+            sc = SCENE_LABELS_MAP.get(m["scene"], m["scene"])
+            scene_counts[sc] = scene_counts.get(sc, 0) + 1
+        scene_desc = "、".join(f"{k}{v}项" for k, v in scene_counts.items())
+        parts.append(f"{ongoing} 项{scene_desc}进行中")
     if not parts:
-        rule_summary = "当前无待办事项,经营状况良好。可主动发起咨询或查看经营健康度。"
-        rule_action = "浏览智囊或查看事项看板"
+        rule_summary = "当前无待办事项,经营状况良好。"
+        rule_action = "可主动发起咨询或浏览智囊"
     else:
         rule_summary = "、".join(parts) + "。"
         if red_count > 0:
@@ -2465,6 +2480,10 @@ def dashboard_summary(authorization: str | None = Header(default=None)):
             rule_action = "建议先确认待办事项"
         elif reply_count > 0:
             rule_action = "建议查看专家回复"
+        elif ongoing > 0:
+            # 取最近的案卡标题作为建议
+            latest = open_matters[0]["title"] if open_matters else ""
+            rule_action = f"继续跟进「{latest[:15]}」" if latest else "建议跟进进行中的事项"
         else:
             rule_action = "建议关注中风险事项"
 
@@ -2472,13 +2491,16 @@ def dashboard_summary(authorization: str | None = Header(default=None)):
     try:
         if kimi and open_matters:
             matter_desc = "\n".join(
-                f"- {m['title']}(风险:{m['risk_level']},场景:{m['scene']},状态:{m['status']})"
+                f"- {m['title']}(场景:{SCENE_LABELS_MAP.get(m['scene'], m['scene'])},状态:{m['status']})"
                 for m in open_matters[:5]
             )
             prompt = (
-                f"你是中小企业老板的AI助手。请根据以下数据,用一句话(不超过50字)总结当前经营状态,"
-                f"并给出一个最优先的行动建议(不超过20字)。\n\n"
-                f"待办事项:\n{matter_desc}\n"
+                f"你是中小企业老板的AI助手。请根据以下待办事项数据,生成今日要点摘要。\n"
+                f"要求:\n"
+                f"1. summary:一句话总结,必须包含具体数字和事项主题,不超过60字\n"
+                f"2. action:最优先的行动建议,必须引用具体事项标题,不超过25字\n"
+                f"3. 不要用'经营状态稳定'等空泛表述,要基于实际数据\n\n"
+                f"待办事项(共{open_count}项):\n{matter_desc}\n"
                 f"待确认:{pending_count}个,专家回复:{reply_count}条\n\n"
                 f"返回 JSON 格式:{{\"summary\": \"...\", \"action\": \"...\"}}"
             )