diff --git a/backend/python/app/platform_knowledge.py b/backend/python/app/platform_knowledge.py
index 3ec588b..25df381 100644
--- a/backend/python/app/platform_knowledge.py
+++ b/backend/python/app/platform_knowledge.py
@@ -38,7 +38,7 @@ PLATFORM_KNOWLEDGE = """# 七虎AI平台能力全览
 
 ## 5. IM Bot（微信集成）
 - **个人微信Bot**: 通过iLink官方接口接入，扫码绑定，微信内直接AI对话
-- **企业微信/飞书/QQ/悟空IM**: Webhook框架已就绪，支持自定义机器人
+- **企业微信/飞书/QQ/悟空IM**: Webhook框架已绪，支持自定义机器人
 - **统一会话管理**: 微信消息并入平台AI聊天，统一历史管理
 
 ## 6. 资源中心
@@ -97,25 +97,62 @@ def get_platform_knowledge(db=None, user_id=None) -> str:
         apps = (
             db.query(MarketApp)
             .filter(MarketApp.source == "official", MarketApp.status == "published")
-            .order_by(MarketApp.capability, MarketApp.name)
+            .order_by(MarketApp.capability, MarketApp.type, MarketApp.name)
             .all()
         )
         if not apps:
             return PLATFORM_KNOWLEDGE
 
-        lines = [
-            "",
-            "## 平台已上线技能（官方，优先推荐）",
-            "以下为平台当前已发布上线的技能/应用，回答用户相关问题时**优先推荐使用**这些平台能力：",
-        ]
+        # 按类型分组统计
+        type_count = {}
+        for a in apps:
+            t = getattr(a, "type", None) or "skill"
+            type_count[t] = type_count.get(t, 0) + 1
+        type_brief = ", ".join([f"{({'skill':'技能','agent':'Agent','mcp':'MCP应用','mcp_tool':'MCP工具'}.get(k,k))} {v}" for k, v in sorted(type_count.items())])
+
+        # 按能力(capability)分组
+        from collections import OrderedDict
+        by_cap = OrderedDict()
         for a in apps:
-            name = getattr(a, "name", None) or "未命名"
             cap = getattr(a, "capability", None) or getattr(a, "category", None) or "通用"
-            desc = (getattr(a, "description", "") or "").strip().replace("\n", " ")
-            if len(desc) > 60:
-                desc = desc[:60] + "…"
-            lines.append(f"- **{name}**（{cap}）: {desc}")
-        return PLATFORM_KNOWLEDGE + "\n" + "\n".join(lines)
+            by_cap.setdefault(cap, []).append(a)
+
+        # 强制完整性说明（关键变更）
+        lines = [
+            "# 📦 平台已上线能力清单（实时同步自数据库，可放心信任此清单）",
+            "",
+            f"**总数：{len(apps)} 个已上线能力**（{type_brief}）",
+            "",
+            "## ⚠️ 强制完整性指令",
+            "当用户询问以下任何一类问题时，**必须输出下方全部内容**：",
+            '- "平台有哪些技能/应用/MCP"',
+            '- "全部技能" / "列一下技能" / "技能清单"',
+            '- "有什么 Skills" / "支持哪些 Skills"',
+            '- "列举所有" / "每个都列出来"',
+            "",
+            "**绝对不允许**精简、抽样、挑代表性的几个、或改写为综述类叙述。**逐条输出下方每一项**，可以按类别分组展示，但不可省略任何一条。",
+            "",
+        ]
+
+        # 按能力分组渲染
+        type_emoji = {"skill": "🛠", "agent": "🤖", "mcp": "🔌", "mcp_tool": "🔧"}
+        for cap in by_cap.keys():
+            cap_apps = by_cap[cap]
+            lines.append(f"## 【{cap}】- {len(cap_apps)} 个")
+            for a in cap_apps:
+                name = getattr(a, "name", None) or "未命名"
+                desc = (getattr(a, "description", "") or "").strip().replace("\n", " ")
+                # 描述长度提升到 100 字，给出更可识别的简介
+                if len(desc) > 100:
+                    desc = desc[:100] + "…"
+                emoji = type_emoji.get(getattr(a, "type", "skill"), "📦")
+                lines.append(f"- {emoji} **{name}**: {desc}")
+            lines.append("")
+
+        dynamic_section = "\n".join(lines)
+
+        # 关键：动态能力清单放在最前面，让 AI 优先处理
+        return dynamic_section + "\n\n---\n\n# 平台静态文档\n\n" + PLATFORM_KNOWLEDGE
     except Exception as e:
         logger.warning(f"[platform_knowledge] 动态构建失败，回退静态知识库: {e}")
         return PLATFORM_KNOWLEDGE