diff --git a/plugins/set_emoji_like/plugin.py b/plugins/set_emoji_like/plugin.py
deleted file mode 100644
index 5bc1a3ae8..000000000
--- a/plugins/set_emoji_like/plugin.py
+++ /dev/null
@@ -1,190 +0,0 @@
-import re
-from typing import List, Tuple, Type
-
-from src.plugin_system import (
-    BasePlugin,
-    register_plugin,
-    BaseAction,
-    ComponentInfo,
-    ActionActivationType,
-    ConfigField,
-)
-from src.common.logger import get_logger
-from .qq_emoji_list import qq_face
-from src.plugin_system.base.component_types import ChatType
-
-logger = get_logger("set_emoji_like_plugin")
-
-
-def get_emoji_id(emoji_input: str) -> str | None:
-    """根据输入获取表情ID"""
-    # 如果输入本身就是数字ID，直接返回
-    if emoji_input.isdigit() or (isinstance(emoji_input, str) and emoji_input.startswith("😊")):
-        if emoji_input in qq_face:
-            return emoji_input
-
-    # 尝试从 "[表情：xxx]" 格式中提取
-    match = re.search(r"\[表情：(.+?)\]", emoji_input)
-    if match:
-        emoji_name = match.group(1).strip()
-    else:
-        emoji_name = emoji_input.strip()
-
-    # 遍历查找
-    for key, value in qq_face.items():
-        # value 的格式是 "[表情：xxx]"
-        if f"[表情：{emoji_name}]" == value:
-            return key
-
-    return None
-
-
-# ===== Action组件 =====
-class SetEmojiLikeAction(BaseAction):
-    """设置消息表情回应"""
-
-    # === 基本信息（必须填写）===
-    action_name = "set_emoji_like"
-    action_description = "为某条已经存在的消息添加‘贴表情’回应（类似点赞），而不是发送新消息。可以在觉得某条消息非常有趣、值得赞同或者需要特殊情感回应时主动使用。"
-    activation_type = ActionActivationType.ALWAYS  # 消息接收时激活(?)
-    chat_type_allow = ChatType.GROUP
-    parallel_action = True
-
-    # === 功能描述（必须填写）===
-    # 从 qq_face 字典中提取所有表情名称用于提示
-    emoji_options = []
-    for name in qq_face.values():
-        match = re.search(r"\[表情：(.+?)\]", name)
-        if match:
-            emoji_options.append(match.group(1))
-
-    action_parameters = {
-        "emoji": f"要回应的表情,必须从以下表情中选择: {', '.join(emoji_options)}",
-        "set": "是否设置回应 (True/False)",
-    }
-    action_require = [
-        "当需要对一个已存在消息进行‘贴表情’回应时使用",
-        "这是一个对旧消息的操作，而不是发送新消息",
-        "如果你想发送一个新的表情包消息，请使用 'emoji' 动作",
-    ]
-    llm_judge_prompt = """
-    判定是否需要使用贴表情动作的条件：
-    1. 用户明确要求使用贴表情包
-    2. 这是一个适合表达强烈情绪的场合
-    3. 不要发送太多表情包，如果你已经发送过多个表情包则回答"否"
-    
-    请回答"是"或"否"。
-    """
-    associated_types = ["text"]
-
-    async def execute(self) -> Tuple[bool, str]:
-        """执行设置表情回应的动作"""
-        message_id = None
-        if self.has_action_message:
-            logger.debug(str(self.action_message))
-            if isinstance(self.action_message, dict):
-                message_id = self.action_message.get("message_id")
-            logger.info(f"获取到的消息ID: {message_id}")
-        else:
-            logger.error("未提供消息ID")
-            await self.store_action_info(
-                action_build_into_prompt=True,
-                action_prompt_display=f"执行了set_emoji_like动作：{self.action_name},失败: 未提供消息ID",
-                action_done=False,
-            )
-            return False, "未提供消息ID"
-
-        emoji_input = self.action_data.get("emoji")
-        set_like = self.action_data.get("set", True)
-
-        if not emoji_input:
-            logger.error("未提供表情")
-            return False, "未提供表情"
-        logger.info(f"设置表情回应: {emoji_input}, 是否设置: {set_like}")
-
-        emoji_id = get_emoji_id(emoji_input)
-        if not emoji_id:
-            logger.error(f"找不到表情: '{emoji_input}'。请从可用列表中选择。")
-            await self.store_action_info(
-                action_build_into_prompt=True,
-                action_prompt_display=f"执行了set_emoji_like动作：{self.action_name},失败: 找不到表情: '{emoji_input}'",
-                action_done=False,
-            )
-            return False, f"找不到表情: '{emoji_input}'。请从可用列表中选择。"
-
-        # 4. 使用适配器API发送命令
-        if not message_id:
-            logger.error("未提供消息ID")
-            await self.store_action_info(
-                action_build_into_prompt=True,
-                action_prompt_display=f"执行了set_emoji_like动作：{self.action_name},失败: 未提供消息ID",
-                action_done=False,
-            )
-            return False, "未提供消息ID"
-
-        try:
-            # 使用适配器API发送贴表情命令
-            success = await self.send_command(
-                command_name="set_emoji_like", args={"message_id": message_id, "emoji_id": emoji_id, "set": set_like}, storage_message=False
-            )
-            if success:
-                logger.info("设置表情回应成功")
-                await self.store_action_info(
-                    action_build_into_prompt=True,
-                    action_prompt_display=f"执行了set_emoji_like动作,{emoji_input},设置表情回应: {emoji_id}, 是否设置: {set_like}",
-                    action_done=True,
-                )
-                return True, "成功设置表情回应"
-            else:
-                logger.error("设置表情回应失败")
-                await self.store_action_info(
-                    action_build_into_prompt=True,
-                    action_prompt_display=f"执行了set_emoji_like动作：{self.action_name},失败",
-                    action_done=False,
-                )
-                return False, "设置表情回应失败"
-
-        except Exception as e:
-            logger.error(f"设置表情回应失败: {e}")
-            await self.store_action_info(
-                action_build_into_prompt=True,
-                action_prompt_display=f"执行了set_emoji_like动作：{self.action_name},失败: {e}",
-                action_done=False,
-            )
-            return False, f"设置表情回应失败: {e}"
-
-
-# ===== 插件注册 =====
-@register_plugin
-class SetEmojiLikePlugin(BasePlugin):
-    """设置消息表情回应插件"""
-
-    # 插件基本信息
-    plugin_name: str = "set_emoji_like"  # 内部标识符
-    enable_plugin: bool = True
-    dependencies: List[str] = []  # 插件依赖列表
-    python_dependencies: List[str] = []  # Python包依赖列表，现在使用内置API
-    config_file_name: str = "config.toml"  # 配置文件名
-
-    # 配置节描述
-    config_section_descriptions = {"plugin": "插件基本信息", "components": "插件组件"}
-
-    # 配置Schema定义
-    config_schema: dict = {
-        "plugin": {
-            "name": ConfigField(type=str, default="set_emoji_like", description="插件名称"),
-            "version": ConfigField(type=str, default="1.0.0", description="插件版本"),
-            "enabled": ConfigField(type=bool, default=True, description="是否启用插件"),
-            "config_version": ConfigField(type=str, default="1.1", description="配置版本"),
-        },
-        "components": {
-            "action_set_emoji_like": ConfigField(type=bool, default=True, description="是否启用设置表情回应功能"),
-        },
-    }
-
-    def get_plugin_components(self) -> List[Tuple[ComponentInfo, Type]]:
-        if self.get_config("components.action_set_emoji_like"):
-            return [
-                (SetEmojiLikeAction.get_action_info(), SetEmojiLikeAction),
-            ]
-        return []
diff --git a/src/main.py b/src/main.py
index aed4175bb..cbb4ddc7b 100644
--- a/src/main.py
+++ b/src/main.py
@@ -208,6 +208,7 @@ MoFox_Bot(第三方修改版)
         from src.plugin_system.apis.permission_api import permission_api
 
         permission_manager = PermissionManager()
+        await permission_manager.initialize()
         permission_api.set_permission_manager(permission_manager)
         logger.info("权限管理器初始化成功")
 
@@ -318,7 +319,6 @@ MoFox_Bot(第三方修改版)
             ]
 
             # 增强记忆系统不需要定时任务，已禁用原有记忆系统的定时任务
-            logger.info("原有记忆系统定时任务已禁用 - 使用增强记忆系统")
 
             await asyncio.gather(*tasks)
 
diff --git a/src/plugin_system/base/base_action.py b/src/plugin_system/base/base_action.py
index 725619adb..26b79d4df 100644
--- a/src/plugin_system/base/base_action.py
+++ b/src/plugin_system/base/base_action.py
@@ -2,7 +2,7 @@ import time
 import asyncio
 
 from abc import ABC, abstractmethod
-from typing import Tuple, Optional
+from typing import Tuple, Optional, List, Dict, Any
 
 from src.common.logger import get_logger
 from src.chat.message_receive.chat_stream import ChatStream
@@ -27,8 +27,21 @@ class BaseAction(ABC):
     - parallel_action: 是否允许并行执行
     - random_activation_probability: 随机激活概率
     - llm_judge_prompt: LLM判断提示词
+    
+    二步Action相关属性：
+    - is_two_step_action: 是否为二步Action
+    - step_one_description: 第一步的描述
+    - sub_actions: 子Action列表
     """
 
+    # 二步Action相关类属性
+    is_two_step_action: bool = False
+    """是否为二步Action。如果为True，Action将分两步执行：第一步选择操作，第二步执行具体操作"""
+    step_one_description: str = ""
+    """第一步的描述，用于向LLM展示Action的基本功能"""
+    sub_actions: List[Tuple[str, str, Dict[str, str]]] = []
+    """子Action列表，格式为[(子Action名, 子Action描述, 子Action参数)]。仅在二步Action中使用"""
+
     def __init__(
         self,
         action_data: dict,
@@ -93,6 +106,13 @@ class BaseAction(ABC):
         self.associated_types: list[str] = getattr(self.__class__, "associated_types", []).copy()
         self.chat_type_allow: ChatType = getattr(self.__class__, "chat_type_allow", ChatType.ALL)
 
+        # 二步Action相关实例属性
+        self.is_two_step_action: bool = getattr(self.__class__, "is_two_step_action", False)
+        self.step_one_description: str = getattr(self.__class__, "step_one_description", "")
+        self.sub_actions: List[Tuple[str, str, Dict[str, str]]] = getattr(self.__class__, "sub_actions", []).copy()
+        self._selected_sub_action: Optional[str] = None
+        """当前选择的子Action名称，用于二步Action的状态管理"""
+
         # =============================================================================
         # 便捷属性 - 直接在初始化时获取常用聊天信息（带类型注解）
         # =============================================================================
@@ -412,23 +432,32 @@ class BaseAction(ABC):
                 logger.warning(f"{log_prefix} 未找到Action组件信息: {action_name}")
                 return False, f"未找到Action组件信息: {action_name}"
 
-            plugin_config = component_registry.get_plugin_config(component_info.plugin_name)
+            # 确保获取的是Action组件
+            if component_info.component_type != ComponentType.ACTION:
+                logger.error(f"{log_prefix} 尝试调用的组件 '{action_name}' 不是一个Action，而是一个 '{component_info.component_type.value}'")
+                return False, f"组件 '{action_name}' 不是一个有效的Action"
 
+            plugin_config = component_registry.get_plugin_config(component_info.plugin_name)
             # 3. 实例化被调用的Action
-            action_instance = action_class(
-                action_data=called_action_data,
-                reasoning=f"Called by {self.action_name}",
-                cycle_timers=self.cycle_timers,
-                thinking_id=self.thinking_id,
-                chat_stream=self.chat_stream,
-                log_prefix=log_prefix,
-                plugin_config=plugin_config,
-                action_message=self.action_message,
-            )
+            action_params = {
+                "action_data": called_action_data,
+                "reasoning": f"Called by {self.action_name}",
+                "cycle_timers": self.cycle_timers,
+                "thinking_id": self.thinking_id,
+                "chat_stream": self.chat_stream,
+                "log_prefix": log_prefix,
+                "plugin_config": plugin_config,
+                "action_message": self.action_message,
+            }
+            action_instance = action_class(**action_params)
 
             # 4. 执行Action
             logger.debug(f"{log_prefix} 开始执行...")
-            result = await action_instance.execute()
+            execute_result = await action_instance.execute()
+            # 确保返回类型符合 (bool, str) 格式
+            is_success = execute_result[0] if isinstance(execute_result, tuple) and len(execute_result) > 0 else False
+            message = execute_result[1] if isinstance(execute_result, tuple) and len(execute_result) > 1 else ""
+            result = (is_success, str(message))
             logger.info(f"{log_prefix} 执行完成，结果: {result}")
             return result
 
@@ -477,15 +506,73 @@ class BaseAction(ABC):
             action_require=getattr(cls, "action_require", []).copy(),
             associated_types=getattr(cls, "associated_types", []).copy(),
             chat_type_allow=getattr(cls, "chat_type_allow", ChatType.ALL),
+            # 二步Action相关属性
+            is_two_step_action=getattr(cls, "is_two_step_action", False),
+            step_one_description=getattr(cls, "step_one_description", ""),
+            sub_actions=getattr(cls, "sub_actions", []).copy(),
         )
 
+    async def handle_step_one(self) -> Tuple[bool, str]:
+        """处理二步Action的第一步
+
+        Returns:
+            Tuple[bool, str]: (是否执行成功, 回复文本)
+        """
+        if not self.is_two_step_action:
+            return False, "此Action不是二步Action"
+
+        # 检查action_data中是否包含选择的子Action
+        selected_action = self.action_data.get("selected_action")
+        if not selected_action:
+            # 第一步：展示可用的子Action
+            available_actions = [sub_action[0] for sub_action in self.sub_actions]
+            description = self.step_one_description or f"{self.action_name}支持以下操作"
+            
+            actions_list = "\n".join([f"- {action}: {desc}" for action, desc, _ in self.sub_actions])
+            response = f"{description}\n\n可用操作：\n{actions_list}\n\n请选择要执行的操作。"
+            
+            return True, response
+        else:
+            # 验证选择的子Action是否有效
+            valid_actions = [sub_action[0] for sub_action in self.sub_actions]
+            if selected_action not in valid_actions:
+                return False, f"无效的操作选择: {selected_action}。可用操作: {valid_actions}"
+            
+            # 保存选择的子Action
+            self._selected_sub_action = selected_action
+            
+            # 调用第二步执行
+            return await self.execute_step_two(selected_action)
+
+    async def execute_step_two(self, sub_action_name: str) -> Tuple[bool, str]:
+        """执行二步Action的第二步
+
+        Args:
+            sub_action_name: 子Action名称
+
+        Returns:
+            Tuple[bool, str]: (是否执行成功, 回复文本)
+        """
+        if not self.is_two_step_action:
+            return False, "此Action不是二步Action"
+
+        # 子类需要重写此方法来实现具体的第二步逻辑
+        return False, f"二步Action必须实现execute_step_two方法来处理操作: {sub_action_name}"
+
     @abstractmethod
     async def execute(self) -> Tuple[bool, str]:
         """执行Action的抽象方法，子类必须实现
 
+        对于二步Action，会自动处理第一步逻辑
+
         Returns:
             Tuple[bool, str]: (是否执行成功, 回复文本)
         """
+        # 如果是二步Action，自动处理第一步
+        if self.is_two_step_action:
+            return await self.handle_step_one()
+        
+        # 普通Action由子类实现
         pass
 
     async def handle_action(self) -> Tuple[bool, str]:
diff --git a/src/plugin_system/base/base_tool.py b/src/plugin_system/base/base_tool.py
index b5022ea2a..84dc8b150 100644
--- a/src/plugin_system/base/base_tool.py
+++ b/src/plugin_system/base/base_tool.py
@@ -38,6 +38,14 @@ class BaseTool(ABC):
     semantic_cache_query_key: Optional[str] = None
     """用于语义缓存的查询参数键名。如果设置，将使用此参数的值进行语义相似度搜索"""
 
+    # 二步工具调用相关属性
+    is_two_step_tool: bool = False
+    """是否为二步工具。如果为True，工具将分两步调用：第一步展示工具信息，第二步执行具体操作"""
+    step_one_description: str = ""
+    """第一步的描述，用于向LLM展示工具的基本功能"""
+    sub_tools: List[Tuple[str, str, List[Tuple[str, ToolParamType, str, bool, List[str] | None]]]] = []
+    """子工具列表，格式为[(子工具名, 子工具描述, 子工具参数)]。仅在二步工具中使用"""
+
     def __init__(self, plugin_config: Optional[dict] = None):
         self.plugin_config = plugin_config or {}  # 直接存储插件配置字典
 
@@ -48,10 +56,64 @@ class BaseTool(ABC):
         Returns:
             dict: 工具定义字典
         """
-        if not cls.name or not cls.description or not cls.parameters:
-            raise NotImplementedError(f"工具类 {cls.__name__} 必须定义 name, description 和 parameters 属性")
+        if not cls.name or not cls.description:
+            raise NotImplementedError(f"工具类 {cls.__name__} 必须定义 name 和 description 属性")
 
-        return {"name": cls.name, "description": cls.description, "parameters": cls.parameters}
+        # 如果是二步工具，第一步只返回基本信息
+        if cls.is_two_step_tool:
+            return {
+                "name": cls.name,
+                "description": cls.step_one_description or cls.description,
+                "parameters": [("action", ToolParamType.STRING, "选择要执行的操作", True, [sub_tool[0] for sub_tool in cls.sub_tools])]
+            }
+        else:
+            # 普通工具需要parameters
+            if not cls.parameters:
+                raise NotImplementedError(f"工具类 {cls.__name__} 必须定义 parameters 属性")
+            return {"name": cls.name, "description": cls.description, "parameters": cls.parameters}
+
+    @classmethod
+    def get_step_two_tool_definition(cls, sub_tool_name: str) -> dict[str, Any]:
+        """获取二步工具的第二步定义
+
+        Args:
+            sub_tool_name: 子工具名称
+
+        Returns:
+            dict: 第二步工具定义字典
+        """
+        if not cls.is_two_step_tool:
+            raise ValueError(f"工具 {cls.name} 不是二步工具")
+
+        # 查找对应的子工具
+        for sub_name, sub_desc, sub_params in cls.sub_tools:
+            if sub_name == sub_tool_name:
+                return {
+                    "name": f"{cls.name}_{sub_tool_name}",
+                    "description": sub_desc,
+                    "parameters": sub_params
+                }
+        
+        raise ValueError(f"未找到子工具: {sub_tool_name}")
+
+    @classmethod
+    def get_all_sub_tool_definitions(cls) -> List[dict[str, Any]]:
+        """获取所有子工具的定义
+
+        Returns:
+            List[dict]: 所有子工具定义列表
+        """
+        if not cls.is_two_step_tool:
+            return []
+        
+        definitions = []
+        for sub_name, sub_desc, sub_params in cls.sub_tools:
+            definitions.append({
+                "name": f"{cls.name}_{sub_name}",
+                "description": sub_desc,
+                "parameters": sub_params
+            })
+        return definitions
 
     @classmethod
     def get_tool_info(cls) -> ToolInfo:
@@ -79,8 +141,68 @@ class BaseTool(ABC):
         Returns:
             dict: 工具执行结果
         """
+        # 如果是二步工具，处理第一步调用
+        if self.is_two_step_tool and "action" in function_args:
+            return await self._handle_step_one(function_args)
+        
         raise NotImplementedError("子类必须实现execute方法")
 
+    async def _handle_step_one(self, function_args: dict[str, Any]) -> dict[str, Any]:
+        """处理二步工具的第一步调用
+
+        Args:
+            function_args: 包含action参数的函数参数
+
+        Returns:
+            dict: 第一步执行结果，包含第二步的工具定义
+        """
+        action = function_args.get("action")
+        if not action:
+            return {"error": "缺少action参数"}
+
+        # 查找对应的子工具
+        sub_tool_found = None
+        for sub_name, sub_desc, sub_params in self.sub_tools:
+            if sub_name == action:
+                sub_tool_found = (sub_name, sub_desc, sub_params)
+                break
+
+        if not sub_tool_found:
+            available_actions = [sub_tool[0] for sub_tool in self.sub_tools]
+            return {"error": f"未知的操作: {action}。可用操作: {available_actions}"}
+
+        sub_name, sub_desc, sub_params = sub_tool_found
+
+        # 返回第二步工具定义
+        step_two_definition = {
+            "name": f"{self.name}_{sub_name}",
+            "description": sub_desc,
+            "parameters": sub_params
+        }
+
+        return {
+            "type": "two_step_tool_step_one",
+            "content": f"已选择操作: {action}。请使用以下工具进行具体调用:",
+            "next_tool_definition": step_two_definition,
+            "selected_action": action
+        }
+
+    async def execute_step_two(self, sub_tool_name: str, function_args: dict[str, Any]) -> dict[str, Any]:
+        """执行二步工具的第二步
+
+        Args:
+            sub_tool_name: 子工具名称
+            function_args: 工具调用参数
+
+        Returns:
+            dict: 工具执行结果
+        """
+        if not self.is_two_step_tool:
+            raise ValueError(f"工具 {self.name} 不是二步工具")
+
+        # 子类需要重写此方法来实现具体的第二步逻辑
+        raise NotImplementedError("二步工具必须实现execute_step_two方法")
+
     async def direct_execute(self, **kwargs: dict[str, Any]) -> dict[str, Any]:
         """直接执行工具函数(供插件调用)
            通过该方法，插件可以直接调用工具，而不需要传入字典格式的参数
diff --git a/src/plugin_system/base/component_types.py b/src/plugin_system/base/component_types.py
index 3fc943bd5..6d0590d43 100644
--- a/src/plugin_system/base/component_types.py
+++ b/src/plugin_system/base/component_types.py
@@ -142,6 +142,10 @@ class ActionInfo(ComponentInfo):
     mode_enable: ChatMode = ChatMode.ALL
     parallel_action: bool = False
     chat_type_allow: ChatType = ChatType.ALL  # 允许的聊天类型
+    # 二步Action相关属性
+    is_two_step_action: bool = False  # 是否为二步Action
+    step_one_description: str = ""  # 第一步的描述
+    sub_actions: List[Tuple[str, str, Dict[str, str]]] = field(default_factory=list)  # 子Action列表
 
     def __post_init__(self):
         super().__post_init__()
@@ -153,6 +157,8 @@ class ActionInfo(ComponentInfo):
             self.action_require = []
         if self.associated_types is None:
             self.associated_types = []
+        if self.sub_actions is None:
+            self.sub_actions = []
         self.component_type = ComponentType.ACTION
 
 
diff --git a/src/plugin_system/core/tool_use.py b/src/plugin_system/core/tool_use.py
index 1b2618f43..daa8244cf 100644
--- a/src/plugin_system/core/tool_use.py
+++ b/src/plugin_system/core/tool_use.py
@@ -55,6 +55,10 @@ class ToolExecutor:
 
         self.llm_model = LLMRequest(model_set=model_config.model_task_config.tool_use, request_type="tool_executor")
 
+        # 二步工具调用状态管理
+        self._pending_step_two_tools: Dict[str, Dict[str, Any]] = {}
+        """待处理的第二步工具调用，格式为 {tool_name: step_two_definition}"""
+
         logger.info(f"{self.log_prefix}工具执行器初始化完成")
 
     async def execute_from_chat_message(
@@ -112,7 +116,18 @@ class ToolExecutor:
     def _get_tool_definitions(self) -> List[Dict[str, Any]]:
         all_tools = get_llm_available_tool_definitions()
         user_disabled_tools = global_announcement_manager.get_disabled_chat_tools(self.chat_id)
-        return [definition for name, definition in all_tools if name not in user_disabled_tools]
+        
+        # 获取基础工具定义（包括二步工具的第一步）
+        tool_definitions = [definition for name, definition in all_tools if name not in user_disabled_tools]
+        
+        # 检查是否有待处理的二步工具第二步调用
+        pending_step_two = getattr(self, '_pending_step_two_tools', {})
+        if pending_step_two:
+            # 添加第二步工具定义
+            for tool_name, step_two_def in pending_step_two.items():
+                tool_definitions.append(step_two_def)
+        
+        return tool_definitions
 
     async def execute_tool_calls(self, tool_calls: Optional[List[ToolCall]]) -> Tuple[List[Dict[str, Any]], List[str]]:
         """执行工具调用
@@ -251,6 +266,32 @@ class ToolExecutor:
                 f"{self.log_prefix} 正在执行工具: [bold green]{function_name}[/bold green] | 参数: {function_args}"
             )
             function_args["llm_called"] = True  # 标记为LLM调用
+            
+            # 检查是否是二步工具的第二步调用
+            if "_" in function_name and function_name.count("_") >= 1:
+                # 可能是二步工具的第二步调用，格式为 "tool_name_sub_tool_name"
+                parts = function_name.split("_", 1)
+                if len(parts) == 2:
+                    base_tool_name, sub_tool_name = parts
+                    base_tool_instance = get_tool_instance(base_tool_name)
+                    
+                    if base_tool_instance and base_tool_instance.is_two_step_tool:
+                        logger.info(f"{self.log_prefix}执行二步工具第二步: {base_tool_name}.{sub_tool_name}")
+                        result = await base_tool_instance.execute_step_two(sub_tool_name, function_args)
+                        
+                        # 清理待处理的第二步工具
+                        self._pending_step_two_tools.pop(base_tool_name, None)
+                        
+                        if result:
+                            logger.debug(f"{self.log_prefix}二步工具第二步 {function_name} 执行成功")
+                            return {
+                                "tool_call_id": tool_call.call_id,
+                                "role": "tool",
+                                "name": function_name,
+                                "type": "function",
+                                "content": result.get("content", ""),
+                            }
+            
             # 获取对应工具实例
             tool_instance = tool_instance or get_tool_instance(function_name)
             if not tool_instance:
@@ -260,6 +301,16 @@ class ToolExecutor:
             # 执行工具并记录日志
             logger.debug(f"{self.log_prefix}执行工具 {function_name}，参数: {function_args}")
             result = await tool_instance.execute(function_args)
+            
+            # 检查是否是二步工具的第一步结果
+            if result and result.get("type") == "two_step_tool_step_one":
+                logger.info(f"{self.log_prefix}二步工具第一步完成: {function_name}")
+                # 保存第二步工具定义
+                next_tool_def = result.get("next_tool_definition")
+                if next_tool_def:
+                    self._pending_step_two_tools[function_name] = next_tool_def
+                    logger.debug(f"{self.log_prefix}已保存第二步工具定义: {next_tool_def['name']}")
+            
             if result:
                 logger.debug(f"{self.log_prefix}工具 {function_name} 执行成功，结果: {result}")
                 return {
diff --git a/src/plugins/built_in/affinity_flow_chatter/plan_filter.py b/src/plugins/built_in/affinity_flow_chatter/plan_filter.py
index 074729740..39bff9d65 100644
--- a/src/plugins/built_in/affinity_flow_chatter/plan_filter.py
+++ b/src/plugins/built_in/affinity_flow_chatter/plan_filter.py
@@ -644,7 +644,7 @@ class ChatterPlanFilter:
                     # 为参数描述添加一个通用示例值
                     if action_name == "set_emoji_like" and p_name == "emoji":
                         # 特殊处理set_emoji_like的emoji参数
-                        from plugins.set_emoji_like.qq_emoji_list import qq_face
+                        from src.plugins.built_in.social_toolkit_plugin.qq_emoji_list import qq_face
                         emoji_options = [re.search(r"\[表情：(.+?)\]", name).group(1) for name in qq_face.values() if re.search(r"\[表情：(.+?)\]", name)]
                         example_value = f"<从'{', '.join(emoji_options[:10])}...'中选择一个>"
                     else:
diff --git a/src/plugins/built_in/at_user_plugin/_manifest.json b/src/plugins/built_in/at_user_plugin/_manifest.json
deleted file mode 100644
index f4b038728..000000000
--- a/src/plugins/built_in/at_user_plugin/_manifest.json
+++ /dev/null
@@ -1,11 +0,0 @@
-{
-    "manifest_version": 1,
-    "name": "At User Plugin",
-    "description": "一个通过名字艾特用户的插件",
-    "author": "Kilo Code",
-    "version": "1.0.0",
-    "requirements": [],
-    "license": "MIT",
-    "keywords": ["at", "mention", "user"],
-    "categories": ["Chat", "Utility"]
-}
\ No newline at end of file
diff --git a/src/plugins/built_in/at_user_plugin/plugin.py b/src/plugins/built_in/at_user_plugin/plugin.py
deleted file mode 100644
index 820b37a27..000000000
--- a/src/plugins/built_in/at_user_plugin/plugin.py
+++ /dev/null
@@ -1,173 +0,0 @@
-from typing import List, Tuple, Type
-from src.plugin_system import (
-    BasePlugin,
-    BaseCommand,
-    CommandInfo,
-    register_plugin,
-    BaseAction,
-    ActionInfo,
-    ActionActivationType,
-)
-from src.person_info.person_info import get_person_info_manager
-from src.common.logger import get_logger
-from src.plugin_system.base.component_types import ChatType
-
-logger = get_logger(__name__)
-
-
-class AtAction(BaseAction):
-    """发送艾特消息"""
-
-    # === 基本信息（必须填写）===
-    action_name = "at_user"
-    action_description = "发送艾特消息"
-    activation_type = ActionActivationType.LLM_JUDGE  # 消息接收时激活(?)
-    parallel_action = False
-    chat_type_allow = ChatType.GROUP
-
-    # === 功能描述（必须填写）===
-    action_parameters = {"user_name": "需要艾特用户的名字", "at_message": "艾特用户时要发送的消息"}
-    action_require = [
-        "当用户明确要求你去'叫'、'喊'、'提醒'或'艾特'某人时使用",
-        "当你判断，为了让特定的人看到消息，需要代表用户去呼叫他/她时使用",
-        "例如：'你去叫一下张三'，'提醒一下李四开会'",
-    ]
-    llm_judge_prompt = """
-    判定是否需要使用艾特用户动作的条件：
-    1. 你在对话中提到了某个具体的人，并且需要提醒他/她。
-    3. 上下文明确需要你艾特一个或多个人。
-
-    请回答"是"或"否"。
-    """
-    associated_types = ["text"]
-
-    async def execute(self) -> Tuple[bool, str]:
-        """执行艾特用户的动作"""
-        user_name = self.action_data.get("user_name")
-        at_message = self.action_data.get("at_message")
-
-        if not user_name or not at_message:
-            logger.warning("艾特用户的动作缺少必要参数。")
-            await self.store_action_info(
-                action_build_into_prompt=True,
-                action_prompt_display=f"执行了艾特用户动作：艾特用户 {user_name} 并发送消息: {at_message},失败了,因为没有提供必要参数",
-                action_done=False,
-            )
-            return False, "缺少必要参数"
-
-        user_info = await get_person_info_manager().get_person_info_by_name(user_name)
-        if not user_info or not user_info.get("user_id"):
-            logger.info(f"找不到名为 '{user_name}' 的用户。")
-            return False, "用户不存在"
-
-        try:
-            # 使用回复器生成艾特回复，而不是直接发送命令
-            from src.chat.replyer.default_generator import DefaultReplyer
-            from src.chat.message_receive.chat_stream import get_chat_manager
-
-            # 获取当前聊天流
-            chat_manager = get_chat_manager()
-            chat_stream = self.chat_stream or chat_manager.get_stream(self.chat_id)
-
-            if not chat_stream:
-                logger.error(f"找不到聊天流: {self.chat_stream}")
-                return False, "聊天流不存在"
-
-            # 创建回复器实例
-            replyer = DefaultReplyer(chat_stream)
-
-            # 构建回复对象，将艾特消息作为回复目标
-            reply_to = f"{user_name}:{at_message}"
-            extra_info = f"你需要艾特用户 {user_name} 并回复他们说: {at_message}"
-
-            # 使用回复器生成回复
-            success, llm_response, prompt = await replyer.generate_reply_with_context(
-                reply_to=reply_to,
-                extra_info=extra_info,
-                enable_tool=False,  # 艾特回复通常不需要工具调用
-                from_plugin=False,
-            )
-
-            if success and llm_response:
-                # 获取生成的回复内容
-                reply_content = llm_response.get("content", "")
-                if reply_content:
-                    # 获取用户QQ号，发送真正的艾特消息
-                    user_id = user_info.get("user_id")
-
-                    # 发送真正的艾特命令，使用回复器生成的智能内容
-                    await self.send_command(
-                        "SEND_AT_MESSAGE",
-                        args={"qq_id": user_id, "text": reply_content},
-                        display_message=f"艾特用户 {user_name} 并发送智能回复: {reply_content}",
-                    )
-
-                    await self.store_action_info(
-                        action_build_into_prompt=True,
-                        action_prompt_display=f"执行了艾特用户动作：艾特用户 {user_name} 并发送智能回复: {reply_content}",
-                        action_done=True,
-                    )
-
-                    logger.info(f"成功通过回复器生成智能内容并发送真正的艾特消息给 {user_name}: {reply_content}")
-                    return True, "智能艾特消息发送成功"
-                else:
-                    logger.warning("回复器生成了空内容")
-                    return False, "回复内容为空"
-            else:
-                logger.error("回复器生成回复失败")
-                return False, "回复生成失败"
-
-        except Exception as e:
-            logger.error(f"执行艾特用户动作时发生异常: {e}", exc_info=True)
-            await self.store_action_info(
-                action_build_into_prompt=True,
-                action_prompt_display=f"执行艾特用户动作失败：{str(e)}",
-                action_done=False,
-            )
-            return False, f"执行失败: {str(e)}"
-
-
-class AtCommand(BaseCommand):
-    command_name: str = "at_user"
-    description: str = "通过名字艾特用户"
-    command_pattern: str = r"/at\s+@?(?P<name>[\S]+)(?:\s+(?P<text>.*))?"
-
-    async def execute(self) -> Tuple[bool, str, bool]:
-        name = self.matched_groups.get("name")
-        text = self.matched_groups.get("text", "")
-
-        if not name:
-            await self.send_text("请指定要艾特的用户名称。")
-            return False, "缺少用户名称", True
-
-        person_info_manager = get_person_info_manager()
-        user_info = await person_info_manager.get_person_info_by_name(name)
-
-        if not user_info or not user_info.get("user_id"):
-            await self.send_text(f"找不到名为 '{name}' 的用户。")
-            return False, "用户不存在", True
-
-        user_id = user_info.get("user_id")
-
-        await self.send_command(
-            "SEND_AT_MESSAGE",
-            args={"qq_id": user_id, "text": text},
-            display_message=f"艾特用户 {name} 并发送消息: {text}",
-        )
-
-        return True, "艾特消息已发送", True
-
-
-@register_plugin
-class AtUserPlugin(BasePlugin):
-    plugin_name: str = "at_user_plugin"
-    enable_plugin: bool = True
-    dependencies: list[str] = []
-    python_dependencies: list[str] = []
-    config_file_name: str = "config.toml"
-    config_schema: dict = {}
-
-    def get_plugin_components(self) -> List[Tuple[CommandInfo | ActionInfo, Type[BaseCommand] | Type[BaseAction]]]:
-        return [
-            (AtAction.get_action_info(), AtAction),
-        ]
diff --git a/src/plugins/built_in/maizone_refactored/plugin.py b/src/plugins/built_in/maizone_refactored/plugin.py
index de644c31b..4ea543f68 100644
--- a/src/plugins/built_in/maizone_refactored/plugin.py
+++ b/src/plugins/built_in/maizone_refactored/plugin.py
@@ -88,25 +88,27 @@ class MaiZoneRefactoredPlugin(BasePlugin):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
     async def on_plugin_loaded(self):
+        """插件加载完成后的回调，初始化服务并启动后台任务"""
+        # --- 注册权限节点 ---
         await permission_api.register_permission_node(
             "plugin.maizone.send_feed", "是否可以使用机器人发送QQ空间说说", "maiZone", False
         )
         await permission_api.register_permission_node(
             "plugin.maizone.read_feed", "是否可以使用机器人读取QQ空间说说", "maiZone", True
         )
-        # 创建所有服务实例
+
+        # --- 创建并注册所有服务实例 ---
         content_service = ContentService(self.get_config)
         image_service = ImageService(self.get_config)
         cookie_service = CookieService(self.get_config)
         reply_tracker_service = ReplyTrackerService()
 
-        # 使用已创建的 reply_tracker_service 实例
         qzone_service = QZoneService(
             self.get_config,
             content_service,
             image_service,
             cookie_service,
-            reply_tracker_service,  # 传入已创建的实例
+            reply_tracker_service,
         )
         scheduler_service = SchedulerService(self.get_config, qzone_service)
         monitor_service = MonitorService(self.get_config, qzone_service)
@@ -115,18 +117,12 @@ class MaiZoneRefactoredPlugin(BasePlugin):
         register_service("reply_tracker", reply_tracker_service)
         register_service("get_config", self.get_config)
 
-        # 保存服务引用以便后续启动
-        self.scheduler_service = scheduler_service
-        self.monitor_service = monitor_service
+        logger.info("MaiZone重构版插件服务已注册。")
 
-        logger.info("MaiZone重构版插件已加载，服务已注册。")
-
-    async def on_plugin_loaded(self):
-        """插件加载完成后的回调，启动异步服务"""
-        if hasattr(self, "scheduler_service") and hasattr(self, "monitor_service"):
-            asyncio.create_task(self.scheduler_service.start())
-            asyncio.create_task(self.monitor_service.start())
-            logger.info("MaiZone后台任务已启动。")
+        # --- 启动后台任务 ---
+        asyncio.create_task(scheduler_service.start())
+        asyncio.create_task(monitor_service.start())
+        logger.info("MaiZone后台监控和定时任务已启动。")
 
     def get_plugin_components(self) -> List[Tuple[ComponentInfo, Type]]:
         return [
diff --git a/src/plugins/built_in/maizone_refactored/services/cookie_service.py b/src/plugins/built_in/maizone_refactored/services/cookie_service.py
index b4aedf322..e3692f883 100644
--- a/src/plugins/built_in/maizone_refactored/services/cookie_service.py
+++ b/src/plugins/built_in/maizone_refactored/services/cookie_service.py
@@ -113,31 +113,32 @@ class CookieService:
     async def get_cookies(self, qq_account: str, stream_id: Optional[str]) -> Optional[Dict[str, str]]:
         """
         获取Cookie，按以下顺序尝试：
-        1. Adapter API
-        2. HTTP备用端点
-        3. 本地文件缓存
+        1. HTTP备用端点 (更稳定)
+        2. 本地文件缓存
+        3. Adapter API (作为最后手段)
         """
-        # 1. 尝试从Adapter获取
-        cookies = await self._get_cookies_from_adapter(stream_id)
-        if cookies:
-            logger.info("成功从Adapter获取Cookie。")
-            self._save_cookies_to_file(qq_account, cookies)
-            return cookies
-
-        # 2. 尝试从HTTP备用端点获取
-        logger.warning("从Adapter获取Cookie失败，尝试使用HTTP备用地址。")
+        # 1. 尝试从HTTP备用端点获取
+        logger.info(f"开始尝试从HTTP备用地址获取 {qq_account} 的Cookie...")
         cookies = await self._get_cookies_from_http()
         if cookies:
-            logger.info("成功从HTTP备用地址获取Cookie。")
+            logger.info(f"成功从HTTP备用地址为 {qq_account} 获取Cookie。")
             self._save_cookies_to_file(qq_account, cookies)
             return cookies
 
-        # 3. 尝试从本地文件加载
-        logger.warning("从HTTP备用地址获取Cookie失败，尝试加载本地缓存。")
+        # 2. 尝试从本地文件加载
+        logger.warning(f"从HTTP备用地址获取 {qq_account} 的Cookie失败，尝试加载本地缓存。")
         cookies = self._load_cookies_from_file(qq_account)
         if cookies:
-            logger.info("成功从本地文件加载缓存的Cookie。")
+            logger.info(f"成功从本地文件为 {qq_account} 加载缓存的Cookie。")
             return cookies
 
-        logger.error("所有Cookie获取方法均失败。")
+        # 3. 尝试从Adapter获取 (作为最后的备用方案)
+        logger.warning(f"从本地缓存加载 {qq_account} 的Cookie失败，最后尝试使用Adapter API。")
+        cookies = await self._get_cookies_from_adapter(stream_id)
+        if cookies:
+            logger.info(f"成功从Adapter API为 {qq_account} 获取Cookie。")
+            self._save_cookies_to_file(qq_account, cookies)
+            return cookies
+
+        logger.error(f"为 {qq_account} 获取Cookie的所有方法均失败。请确保Napcat HTTP服务或Adapter连接至少有一个正常工作，或存在有效的本地Cookie文件。")
         return None
diff --git a/src/plugins/built_in/maizone_refactored/services/qzone_service.py b/src/plugins/built_in/maizone_refactored/services/qzone_service.py
index 09e6f5e53..40c0d424a 100644
--- a/src/plugins/built_in/maizone_refactored/services/qzone_service.py
+++ b/src/plugins/built_in/maizone_refactored/services/qzone_service.py
@@ -409,8 +409,9 @@ class QZoneService:
         cookie_dir.mkdir(exist_ok=True)
         cookie_file_path = cookie_dir / f"cookies-{qq_account}.json"
 
+        # 优先尝试通过Napcat HTTP服务获取最新的Cookie
         try:
-            # 使用HTTP服务器方式获取Cookie
+            logger.info("尝试通过Napcat HTTP服务获取Cookie...")
             host = self.get_config("cookie.http_fallback_host", "172.20.130.55")
             port = self.get_config("cookie.http_fallback_port", "9999")
             napcat_token = self.get_config("cookie.napcat_token", "")
@@ -421,23 +422,43 @@ class QZoneService:
                 parsed_cookies = {
                     k.strip(): v.strip() for k, v in (p.split("=", 1) for p in cookie_str.split("; ") if "=" in p)
                 }
-                with open(cookie_file_path, "wb") as f:
-                    f.write(orjson.dumps(parsed_cookies))
-                logger.info(f"Cookie已更新并保存至: {cookie_file_path}")
+                # 成功获取后，异步写入本地文件作为备份
+                try:
+                    with open(cookie_file_path, "wb") as f:
+                        f.write(orjson.dumps(parsed_cookies))
+                    logger.info(f"通过Napcat服务成功更新Cookie，并已保存至: {cookie_file_path}")
+                except Exception as e:
+                    logger.warning(f"保存Cookie到文件时出错: {e}")
                 return parsed_cookies
+            else:
+                logger.warning("通过Napcat服务未能获取有效Cookie。")
 
-            # 如果HTTP获取失败，尝试读取本地文件
-            if cookie_file_path.exists():
-                with open(cookie_file_path, "rb") as f:
-                    return orjson.loads(f.read())
-            return None
         except Exception as e:
-            logger.error(f"更新或加载Cookie时发生异常: {e}")
-            return None
+            logger.warning(f"通过Napcat HTTP服务获取Cookie时发生异常: {e}。将尝试从本地文件加载。")
 
-    async def _fetch_cookies_http(self, host: str, port: str, napcat_token: str) -> Optional[Dict]:
+        # 如果通过服务获取失败，则尝试从本地文件加载
+        logger.info("尝试从本地Cookie文件加载...")
+        if cookie_file_path.exists():
+            try:
+                with open(cookie_file_path, "rb") as f:
+                    cookies = orjson.loads(f.read())
+                    logger.info(f"成功从本地文件加载Cookie: {cookie_file_path}")
+                    return cookies
+            except Exception as e:
+                logger.error(f"从本地文件 {cookie_file_path} 读取或解析Cookie失败: {e}")
+        else:
+            logger.warning(f"本地Cookie文件不存在: {cookie_file_path}")
+
+        logger.error("所有获取Cookie的方式均失败。")
+        return None
+
+    async def _fetch_cookies_http(self, host: str, port: int, napcat_token: str) -> Optional[Dict]:
         """通过HTTP服务器获取Cookie"""
-        url = f"http://{host}:{port}/get_cookies"
+        # 从配置中读取主机和端口，如果未提供则使用传入的参数
+        final_host = self.get_config("cookie.http_fallback_host", host)
+        final_port = self.get_config("cookie.http_fallback_port", port)
+        url = f"http://{final_host}:{final_port}/get_cookies"
+
         max_retries = 5
         retry_delay = 1
 
@@ -481,14 +502,19 @@ class QZoneService:
     async def _get_api_client(self, qq_account: str, stream_id: Optional[str]) -> Optional[Dict]:
         cookies = await self.cookie_service.get_cookies(qq_account, stream_id)
         if not cookies:
+            logger.error("获取API客户端失败：未能获取到Cookie。请检查Napcat连接是否正常，或是否存在有效的本地Cookie文件。")
             return None
 
         p_skey = cookies.get("p_skey") or cookies.get("p_skey".upper())
         if not p_skey:
+            logger.error(f"获取API客户端失败：Cookie中缺少关键的 'p_skey'。Cookie内容: {cookies}")
             return None
 
         gtk = self._generate_gtk(p_skey)
         uin = cookies.get("uin", "").lstrip("o")
+        if not uin:
+            logger.error(f"获取API客户端失败：Cookie中缺少关键的 'uin'。Cookie内容: {cookies}")
+            return None
 
         async def _request(method, url, params=None, data=None, headers=None):
             final_headers = {"referer": f"https://user.qzone.qq.com/{uin}", "origin": "https://user.qzone.qq.com"}
diff --git a/src/plugins/built_in/napcat_adapter_plugin/src/send_handler.py b/src/plugins/built_in/napcat_adapter_plugin/src/send_handler.py
index 4e4aa3e10..c8bc267af 100644
--- a/src/plugins/built_in/napcat_adapter_plugin/src/send_handler.py
+++ b/src/plugins/built_in/napcat_adapter_plugin/src/send_handler.py
@@ -185,9 +185,13 @@ class SendHandler:
 
             logger.info(f"执行适配器命令: {action}")
 
-            # 直接向Napcat发送命令并获取响应
-            response_task = asyncio.create_task(self.send_message_to_napcat(action, params))
-            response = await response_task
+            # 根据action决定处理方式
+            if action == "get_cookies":
+                # 对于get_cookies，我们需要一个更长的超时时间
+                response = await self.send_message_to_napcat(action, params, timeout=40.0)
+            else:
+                # 对于其他命令，使用默认超时
+                response = await self.send_message_to_napcat(action, params)
 
             # 发送响应回MaiBot
             await self.send_adapter_command_response(raw_message_base, response, request_id)
@@ -196,6 +200,8 @@ class SendHandler:
                 logger.info(f"适配器命令 {action} 执行成功")
             else:
                 logger.warning(f"适配器命令 {action} 执行失败，napcat返回：{str(response)}")
+            # 无论成功失败，都记录下完整的响应内容以供调试
+            logger.debug(f"适配器命令 {action} 的完整响应: {response}")
 
         except Exception as e:
             logger.error(f"处理适配器命令时发生错误: {e}")
@@ -583,7 +589,7 @@ class SendHandler:
             },
         )
 
-    async def send_message_to_napcat(self, action: str, params: dict) -> dict:
+    async def send_message_to_napcat(self, action: str, params: dict, timeout: float = 20.0) -> dict:
         request_uuid = str(uuid.uuid4())
         payload = json.dumps({"action": action, "params": params, "echo": request_uuid})
 
@@ -595,9 +601,9 @@ class SendHandler:
 
         try:
             await connection.send(payload)
-            response = await get_response(request_uuid)
+            response = await get_response(request_uuid, timeout=timeout)  # 使用传入的超时时间
         except TimeoutError:
-            logger.error("发送消息超时，未收到响应")
+            logger.error(f"发送消息超时（{timeout}秒），未收到响应: action={action}, params={params}")
             return {"status": "error", "message": "timeout"}
         except Exception as e:
             logger.error(f"发送消息失败: {e}")
diff --git a/src/plugins/built_in/poke_plugin/_manifest.json b/src/plugins/built_in/poke_plugin/_manifest.json
deleted file mode 100644
index 3aae7f727..000000000
--- a/src/plugins/built_in/poke_plugin/_manifest.json
+++ /dev/null
@@ -1,83 +0,0 @@
-{
-    "manifest_version": 1,
-    "name": "戳一戳插件 (Poke Plugin)",
-    "version": "1.0.0",
-    "description": "智能戳一戳互动插件，支持主动戳用户和自动反戳机制，提供多种反应模式包括AI回复",
-    "author": {
-      "name": "MaiBot-Plus开发团队",
-      "url": "https://github.com/MaiBot-Plus"
-    },
-    "license": "GPL-v3.0-or-later",
-    
-    "host_application": {
-      "min_version": "0.8.0"
-    },
-    "homepage_url": "https://github.com/MoFox-Studio/MoFox_Bot",
-    "repository_url": "https://github.com/MoFox-Studio/MoFox_Bot",
-    "keywords": ["poke", "interaction", "fun", "social", "ai-reply", "auto-response"],
-    "categories": ["Social", "Interactive", "Fun"],
-    
-    "default_locale": "zh-CN",
-    "locales_path": "_locales",
-    
-    "plugin_info": {
-      "is_built_in": false,
-      "plugin_type": "interactive",
-      "components": [
-        {
-          "type": "action",
-          "name": "poke_user",
-          "description": "向指定用户发送戳一戳动作",
-          "parameters": {
-            "user_name": "需要戳一戳的用户名称",
-            "times": "戳一戳次数（可选，默认为1）"
-          },
-          "activation_modes": ["llm_judge", "manual"],
-          "llm_conditions": [
-            "用户明确要求使用戳一戳",
-            "想以有趣的方式提醒或与某人互动",
-            "上下文明确需要戳一个或多个人"
-          ]
-        },
-        {
-          "type": "command",
-          "name": "poke_back",
-          "description": "检测戳一戳消息并自动反戳",
-          "pattern": "(?P<poker_name>\\S+)\\s*戳了戳\\s*(?P<target_name>\\S+)",
-          "features": [
-            "正则表达式匹配戳一戳文本",
-            "智能识别戳击目标",
-            "防抖机制避免频繁反戳",
-            "多种反戳模式选择"
-          ]
-        }
-      ],
-      "features": [
-        "主动戳一戳功能 - 可指定用户和次数",
-        "智能反戳机制 - 自动检测并回应戳一戳",
-        "多种反戳模式 - 戳回去/AI回复/随机选择",
-        "AI智能回复 - 集成回复生成器API",
-        "冷却时间控制 - 防止频繁反戳",
-        "灵活配置选项 - 支持自定义回复消息",
-        "安全过滤机制 - 只对针对机器人的戳一戳反应"
-      ],
-      "configuration": {
-        "poke_back_mode": {
-          "type": "string",
-          "options": ["poke", "reply", "random"],
-          "default": "poke",
-          "description": "反戳模式选择"
-        },
-        "poke_back_cooldown": {
-          "type": "integer",
-          "default": 5,
-          "description": "反戳冷却时间（秒）"
-        },
-        "enable_typo_in_reply": {
-          "type": "boolean", 
-          "default": false,
-          "description": "AI回复时是否启用错字生成"
-        }
-      }
-    }
-  } 
\ No newline at end of file
diff --git a/src/plugins/built_in/poke_plugin/plugin.py b/src/plugins/built_in/poke_plugin/plugin.py
deleted file mode 100644
index a37c45dd1..000000000
--- a/src/plugins/built_in/poke_plugin/plugin.py
+++ /dev/null
@@ -1,307 +0,0 @@
-import asyncio
-import random
-from typing import List, Tuple, Type
-
-from src.plugin_system import (
-    BasePlugin,
-    register_plugin,
-    BaseAction,
-    BaseCommand,
-    ComponentInfo,
-    ActionActivationType,
-    ConfigField,
-)
-from src.common.logger import get_logger
-from src.person_info.person_info import get_person_info_manager
-from src.plugin_system.apis import generator_api
-
-logger = get_logger("poke_plugin")
-
-
-# ===== Action组件 =====
-class PokeAction(BaseAction):
-    """发送戳一戳动作"""
-
-    # === 基本信息（必须填写）===
-    action_name = "poke_user"
-    action_description = "向用户发送戳一戳"
-    activation_type = ActionActivationType.ALWAYS
-    parallel_action = True
-
-    # === 功能描述（必须填写）===
-    action_parameters = {
-        "user_name": "需要戳一戳的用户的名字 (可选)",
-        "user_id": "需要戳一戳的用户的ID (可选，优先级更高)",
-        "times": "需要戳一戳的次数 (默认为 1)",
-    }
-    action_require = ["当需要戳某个用户时使用", "当你想提醒特定用户时使用"]
-    llm_judge_prompt = """
-    判定是否需要使用戳一戳动作的条件：
-    1. 用户明确要求使用戳一戳。
-    2. 你想以一种有趣的方式提醒或与某人互动。
-    3. 上下文明确需要你戳一个或多个人。
-
-    请回答"是"或"否"。
-    """
-    associated_types = ["text"]
-
-    async def execute(self) -> Tuple[bool, str]:
-        """执行戳一戳的动作"""
-        user_id = self.action_data.get("user_id")
-        user_name = self.action_data.get("user_name")
-        
-        try:
-            times = int(self.action_data.get("times", 1))
-        except (ValueError, TypeError):
-            times = 1
-
-        # 优先使用 user_id
-        if not user_id:
-            if not user_name:
-                logger.warning("戳一戳动作缺少 'user_id' 或 'user_name' 参数。")
-                return False, "缺少用户标识参数"
-            
-            # 备用方案：通过 user_name 查找
-            user_info = await get_person_info_manager().get_person_info_by_name(user_name)
-            if not user_info or not user_info.get("user_id"):
-                logger.info(f"找不到名为 '{user_name}' 的用户。")
-                return False, f"找不到名为 '{user_name}' 的用户"
-            user_id = user_info.get("user_id")
-        
-        display_name = user_name or user_id
-
-        for i in range(times):
-            logger.info(f"正在向 {display_name} ({user_id}) 发送第 {i + 1}/{times} 次戳一戳...")
-            await self.send_command(
-                "SEND_POKE", args={"qq_id": user_id}, display_message=f"戳了戳 {display_name} ({i + 1}/{times})"
-            )
-            # 添加一个小的延迟，以避免发送过快
-            await asyncio.sleep(0.5)
-
-        success_message = f"已向 {display_name} 发送 {times} 次戳一戳。"
-        await self.store_action_info(
-            action_build_into_prompt=True, action_prompt_display=success_message, action_done=True
-        )
-        return True, success_message
-
-
-# ===== Command组件 =====
-class PokeBackCommand(BaseCommand):
-    """反戳命令组件"""
-
-    command_name = "poke_back"
-    command_description = "检测到戳一戳时自动反戳回去"
-    # 匹配戳一戳的正则表达式 - 匹配 "xxx戳了戳xxx" 的格式
-    command_pattern = r"(?P<poker_name>\S+)\s*戳了戳\s*(?P<target_name>\S+)"
-
-    async def execute(self) -> Tuple[bool, str, bool]:
-        """执行反戳逻辑"""
-        # 检查反戳功能是否启用
-        if not self.get_config("components.command_poke_back", True):
-            return False, "", False
-
-        # 获取匹配的用户名
-        poker_name = self.matched_groups.get("poker_name", "")
-        target_name = self.matched_groups.get("target_name", "")
-
-        if not poker_name or not target_name:
-            logger.debug("戳一戳消息格式不匹配，跳过反戳")
-            return False, "", False
-
-        # 只有当目标是机器人自己时才反戳
-        if target_name not in ["我", "bot", "机器人", "麦麦"]:
-            logger.debug(f"戳一戳目标不是机器人 ({target_name}), 跳过反戳")
-            return False, "", False
-
-        # 获取戳我的用户信息
-        poker_info = await get_person_info_manager().get_person_info_by_name(poker_name)
-        if not poker_info or not poker_info.get("user_id"):
-            logger.info(f"找不到名为 '{poker_name}' 的用户信息，无法反戳")
-            return False, "", False
-
-        poker_id = poker_info.get("user_id")
-        if not isinstance(poker_id, (int, str)):
-            logger.error(f"获取到的用户ID类型不正确: {type(poker_id)}")
-            return False, "", False
-
-        # 确保poker_id是整数类型
-        try:
-            poker_id = int(poker_id)
-        except (ValueError, TypeError):
-            logger.error(f"无法将用户ID转换为整数: {poker_id}")
-            return False, "", False
-
-        # 检查反戳冷却时间（防止频繁反戳）
-        cooldown_seconds = self.get_config("components.poke_back_cooldown", 5)
-        current_time = asyncio.get_event_loop().time()
-
-        # 使用类变量存储上次反戳时间
-        if not hasattr(PokeBackCommand, "_last_poke_back_time"):
-            PokeBackCommand._last_poke_back_time = {}
-
-        last_time = PokeBackCommand._last_poke_back_time.get(poker_id, 0)
-        if current_time - last_time < cooldown_seconds:
-            logger.info(f"反戳冷却中，跳过对 {poker_name} 的反戳")
-            return False, "", False
-
-        # 记录本次反戳时间
-        PokeBackCommand._last_poke_back_time[poker_id] = current_time
-
-        # 执行反戳
-        logger.info(f"检测到 {poker_name} 戳了我，准备反戳回去")
-
-        try:
-            # 获取反戳模式
-            poke_back_mode = self.get_config("components.poke_back_mode", "poke")  # "poke", "reply", "random"
-
-            if poke_back_mode == "random":
-                # 随机选择模式
-                poke_back_mode = random.choice(["poke", "reply"])
-
-            if poke_back_mode == "poke":
-                # 戳回去模式
-                await self._poke_back(poker_id, poker_name)
-            elif poke_back_mode == "reply":
-                # 回复模式
-                await self._reply_back(poker_name)
-            else:
-                logger.warning(f"未知的反戳模式: {poke_back_mode}")
-                return False, "", False
-
-            logger.info(f"成功反戳了 {poker_name} (模式: {poke_back_mode})")
-            return True, f"反戳了 {poker_name}", False  # 不拦截消息继续处理
-
-        except Exception as e:
-            logger.error(f"反戳失败: {e}")
-            return False, "", False
-
-    async def _poke_back(self, poker_id: int, poker_name: str):
-        """执行戳一戳反击"""
-        await self.send_command(
-            "SEND_POKE",
-            args={"qq_id": poker_id},
-            display_message=f"反戳了 {poker_name}",
-            storage_message=False,  # 不存储到消息历史中
-        )
-
-        # 可选：发送一个随机的反戳回复
-        poke_back_messages = self.get_config(
-            "components.poke_back_messages",
-            [
-                "哼，戳回去！",
-                "戳我干嘛~",
-                "反戳！",
-                "你戳我，我戳你！",
-                "（戳回去）",
-            ],
-        )
-
-        if poke_back_messages and self.get_config("components.send_poke_back_message", False):
-            reply_message = random.choice(poke_back_messages)
-            await self.send_text(reply_message)
-
-    async def _reply_back(self, poker_name: str):
-        """生成AI回复"""
-        # 构造回复上下文
-        extra_info = f"{poker_name}戳了我一下，需要生成一个有趣的回应。"
-
-        # 获取配置，确保类型正确
-        enable_typo = self.get_config("components.enable_typo_in_reply", False)
-        if not isinstance(enable_typo, bool):
-            enable_typo = False
-
-        # 使用generator_api生成回复
-        success, reply_set, _ = await generator_api.generate_reply(
-            chat_stream=self.message.chat_stream,
-            extra_info=extra_info,
-            enable_tool=False,
-            enable_splitter=True,
-            enable_chinese_typo=enable_typo,
-            from_plugin=True,
-        )
-
-        if success and reply_set:
-            # 发送生成的回复
-            for reply_item in reply_set:
-                message_type, content = reply_item
-                if message_type == "text":
-                    await self.send_text(content)
-                else:
-                    await self.send_type(message_type, content)
-        else:
-            # 如果AI回复失败，发送一个默认回复
-            fallback_messages = self.get_config(
-                "components.fallback_reply_messages",
-                [
-                    "被戳了！",
-                    "诶？",
-                    "做什么呢~",
-                    "怎么了？",
-                ],
-            )
-
-            # 确保fallback_messages是列表
-            if isinstance(fallback_messages, list) and fallback_messages:
-                fallback_reply = random.choice(fallback_messages)
-                await self.send_text(fallback_reply)
-            else:
-                await self.send_text("被戳了！")
-
-
-# ===== 插件注册 =====
-@register_plugin
-class PokePlugin(BasePlugin):
-    """戳一戳插件"""
-
-    # 插件基本信息
-    plugin_name: str = "poke_plugin"
-    enable_plugin: bool = True
-    dependencies: List[str] = []
-    python_dependencies: List[str] = []
-    config_file_name: str = "config.toml"
-
-    # 配置节描述
-    config_section_descriptions = {"plugin": "插件基本信息", "components": "插件组件"}
-
-    # 配置Schema定义
-    config_schema: dict = {
-        "plugin": {
-            "name": ConfigField(type=str, default="poke_plugin", description="插件名称"),
-            "version": ConfigField(type=str, default="1.0.0", description="插件版本"),
-            "enabled": ConfigField(type=bool, default=True, description="是否启用插件"),
-            "config_version": ConfigField(type=str, default="1.0", description="配置版本"),
-        },
-        "components": {
-            "action_poke_user": ConfigField(type=bool, default=True, description="是否启用戳一戳功能"),
-            "command_poke_back": ConfigField(type=bool, default=True, description="是否启用反戳功能"),
-            "poke_back_mode": ConfigField(
-                type=str, default="poke", description="反戳模式: poke(戳回去), reply(AI回复), random(随机)"
-            ),
-            "poke_back_cooldown": ConfigField(type=int, default=5, description="反戳冷却时间（秒）"),
-            "send_poke_back_message": ConfigField(type=bool, default=False, description="戳回去时是否发送文字回复"),
-            "enable_typo_in_reply": ConfigField(type=bool, default=False, description="AI回复时是否启用错字生成"),
-            "poke_back_messages": ConfigField(
-                type=list,
-                default=["哼，戳回去！", "戳我干嘛~", "反戳！", "你戳我，我戳你！", "（戳回去）"],
-                description="戳回去时的随机回复消息列表",
-            ),
-            "fallback_reply_messages": ConfigField(
-                type=list,
-                default=["被戳了！", "诶？", "做什么呢~", "怎么了？"],
-                description="AI回复失败时的备用回复消息列表",
-            ),
-        },
-    }
-
-    def get_plugin_components(self) -> List[Tuple[ComponentInfo, Type]]:
-        components = []
-
-        # 添加戳一戳动作组件
-        if self.get_config("components.action_poke_user"):
-            components.append((PokeAction.get_action_info(), PokeAction))
-
-        # 添加反戳命令组件
-        if self.get_config("components.command_poke_back"):
-            components.append((PokeBackCommand.get_command_info(), PokeBackCommand))
-
-        return components
diff --git a/src/plugins/built_in/reminder_plugin/_manifest.json b/src/plugins/built_in/reminder_plugin/_manifest.json
deleted file mode 100644
index 58c9fc9e4..000000000
--- a/src/plugins/built_in/reminder_plugin/_manifest.json
+++ /dev/null
@@ -1,9 +0,0 @@
-{
-  "manifest_version": 1,
-  "name": "智能提醒插件",
-  "version": "1.0.0",
-  "description": "一个能从对话中智能识别并设置定时提醒的插件。",
-  "author": {
-    "name": "墨墨"
-  }
-}
\ No newline at end of file
diff --git a/src/plugins/built_in/reminder_plugin/plugin.py b/src/plugins/built_in/reminder_plugin/plugin.py
deleted file mode 100644
index 5382cccff..000000000
--- a/src/plugins/built_in/reminder_plugin/plugin.py
+++ /dev/null
@@ -1,341 +0,0 @@
-import asyncio
-from datetime import datetime
-from typing import List, Tuple, Type, Optional
-
-from dateutil.parser import parse as parse_datetime
-
-from src.common.logger import get_logger
-from src.manager.async_task_manager import AsyncTask, async_task_manager
-from src.person_info.person_info import get_person_info_manager
-from src.plugin_system import (
-    BaseAction,
-    ActionInfo,
-    BasePlugin,
-    register_plugin,
-    ActionActivationType,
-)
-from src.plugin_system.apis import send_api, llm_api, generator_api
-from src.plugin_system.base.component_types import ComponentType
-
-logger = get_logger(__name__)
-
-
-# ============================ AsyncTask ============================
-
-class ReminderTask(AsyncTask):
-    def __init__(self, delay: float, stream_id: str, group_id: Optional[str], is_group: bool, target_user_id: str, target_user_name: str, event_details: str, creator_name: str, chat_stream: "ChatStream"):
-        super().__init__(task_name=f"ReminderTask_{target_user_id}_{datetime.now().timestamp()}")
-        self.delay = delay
-        self.stream_id = stream_id
-        self.group_id = group_id
-        self.is_group = is_group
-        self.target_user_id = target_user_id
-        self.target_user_name = target_user_name
-        self.event_details = event_details
-        self.creator_name = creator_name
-        self.chat_stream = chat_stream
-
-    async def run(self):
-        try:
-            if self.delay > 0:
-                logger.info(f"等待 {self.delay:.2f} 秒后执行提醒...")
-                await asyncio.sleep(self.delay)
-            
-            logger.info(f"执行提醒任务: 给 {self.target_user_name} 发送关于 '{self.event_details}' 的提醒")
-
-            extra_info = f"现在是提醒时间，请你以一种符合你人设的、俏皮的方式提醒 {self.target_user_name}。\n提醒内容: {self.event_details}\n设置提醒的人: {self.creator_name}"
-            success, reply_set, _ = await generator_api.generate_reply(
-                chat_stream=self.chat_stream,
-                extra_info=extra_info,
-                reply_message=self.chat_stream.context.get_last_message().to_dict(),
-                request_type="plugin.reminder.remind_message"
-            )
-
-            if success and reply_set:
-                for i, (_, text) in enumerate(reply_set):
-                    if self.is_group:
-                        message_payload = []
-                        if i == 0:
-                            message_payload.append({"type": "at", "data": {"qq": self.target_user_id}})
-                        message_payload.append({"type": "text", "data": {"text": f" {text}"}})
-                        await send_api.adapter_command_to_stream(
-                            action="send_group_msg",
-                            params={"group_id": self.group_id, "message": message_payload},
-                            stream_id=self.stream_id
-                        )
-                    else:
-                        await send_api.text_to_stream(text=text, stream_id=self.stream_id)
-            else:
-                # Fallback message
-                reminder_text = f"叮咚！这是 {self.creator_name} 让我准时提醒你的事情：\n\n{self.event_details}"
-                if self.is_group:
-                    message_payload = [
-                        {"type": "at", "data": {"qq": self.target_user_id}},
-                        {"type": "text", "data": {"text": f" {reminder_text}"}}
-                    ]
-                    await send_api.adapter_command_to_stream(
-                        action="send_group_msg",
-                        params={"group_id": self.group_id, "message": message_payload},
-                        stream_id=self.stream_id
-                    )
-                else:
-                    await send_api.text_to_stream(text=reminder_text, stream_id=self.stream_id)
-
-            logger.info(f"提醒任务 {self.task_name} 成功完成。")
-
-        except Exception as e:
-            logger.error(f"执行提醒任务 {self.task_name} 时出错: {e}", exc_info=True)
-
-
-# =============================== Actions ===============================
-
-class RemindAction(BaseAction):
-    """一个能从对话中智能识别并设置定时提醒的动作。"""
-
-    # === 基本信息 ===
-    action_name = "set_reminder"
-    action_description = "根据用户的对话内容，智能地设置一个未来的提醒事项。"
-    
-    @staticmethod
-    def get_action_info() -> ActionInfo:
-        return ActionInfo(
-            name="set_reminder",
-            component_type=ComponentType.ACTION,
-            activation_type=ActionActivationType.KEYWORD,
-            activation_keywords=["提醒", "叫我", "记得", "别忘了"]
-        )
-
-    # === LLM 判断与参数提取 ===
-    llm_judge_prompt = ""
-    action_parameters = {}
-    action_require = [
-        "当用户请求在未来的某个时间点提醒他/她或别人某件事时使用",
-        "适用于包含明确时间信息和事件描述的对话",
-        "例如：'10分钟后提醒我收快递'、'明天早上九点喊一下李四参加晨会'"
-    ]
-
-    async def execute(self) -> Tuple[bool, str]:
-        """执行设置提醒的动作"""
-        try:
-            # 获取所有可用的模型配置
-            available_models = llm_api.get_available_models()
-            if "planner" not in available_models:
-                raise ValueError("未找到 'planner' 决策模型配置，无法解析时间")
-            model_to_use = available_models["planner"]
-
-            bot_name = self.chat_stream.user_info.user_nickname
-
-            prompt = f"""
-            从以下用户输入中提取提醒事件的关键信息。
-            用户输入: "{self.chat_stream.context.message.processed_plain_text}"
-            Bot的名字是: "{bot_name}"
-
-            请仔细分析句子结构，以确定谁是提醒的真正目标。Bot自身不应被视为被提醒人。
-            请以JSON格式返回提取的信息，包含以下字段:
-            - "user_name": 需要被提醒的人的姓名。如果未指定，则默认为"自己"。
-            - "remind_time": 描述提醒时间的自然语言字符串。
-            - "event_details": 需要提醒的具体事件内容。
-
-            示例:
-            - 用户输入: "提醒我十分钟后开会" -> {{"user_name": "自己", "remind_time": "十分钟后", "event_details": "开会"}}
-            - 用户输入: "{bot_name}，提醒一闪一分钟后睡觉" -> {{"user_name": "一闪", "remind_time": "一分钟后", "event_details": "睡觉"}}
-
-            如果无法提取完整信息，请返回一个包含空字符串的JSON对象，例如：{{"user_name": "", "remind_time": "", "event_details": ""}}
-            """
-
-            success, response, _, _ = await llm_api.generate_with_model(
-                prompt,
-                model_config=model_to_use,
-                request_type="plugin.reminder.parameter_extractor"
-            )
-
-            if not success or not response:
-                raise ValueError(f"LLM未能返回有效的参数: {response}")
-
-            import json
-            import re
-            try:
-                # 提取JSON部分
-                json_match = re.search(r"\{.*\}", response, re.DOTALL)
-                if not json_match:
-                    raise ValueError("LLM返回的内容中不包含JSON")
-                action_data = json.loads(json_match.group(0))
-            except json.JSONDecodeError:
-                logger.error(f"[ReminderPlugin] LLM返回的不是有效的JSON: {response}")
-                return False, "LLM返回的不是有效的JSON"
-            user_name = action_data.get("user_name")
-            remind_time_str = action_data.get("remind_time")
-            event_details = action_data.get("event_details")
-
-        except Exception as e:
-            logger.error(f"[ReminderPlugin] 解析参数时出错: {e}", exc_info=True)
-            return False, "解析参数时出错"
-
-        if not all([user_name, remind_time_str, event_details]):
-            missing_params = [p for p, v in {"user_name": user_name, "remind_time": remind_time_str, "event_details": event_details}.items() if not v]
-            error_msg = f"缺少必要的提醒参数: {', '.join(missing_params)}"
-            logger.warning(f"[ReminderPlugin] LLM未能提取完整参数: {error_msg}")
-            return False, error_msg
-
-        # 1. 解析时间
-        try:
-            assert isinstance(remind_time_str, str)
-            # 优先尝试直接解析
-            try:
-                target_time = parse_datetime(remind_time_str, fuzzy=True)
-            except Exception:
-                # 如果直接解析失败，调用 LLM 进行转换
-                logger.info(f"[ReminderPlugin] 直接解析时间 '{remind_time_str}' 失败，尝试使用 LLM 进行转换...")
-                
-                # 获取所有可用的模型配置
-                available_models = llm_api.get_available_models()
-                if "planner" not in available_models:
-                    raise ValueError("未找到 'planner' 决策模型配置，无法解析时间")
-                
-                # 明确使用 'planner' 模型
-                model_to_use = available_models["planner"]
-
-                # 在执行时动态获取当前时间
-                current_time_str = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
-                prompt = (
-                    f"请将以下自然语言时间短语转换为一个未来的、标准的 'YYYY-MM-DD HH:MM:SS' 格式。"
-                    f"请只输出转换后的时间字符串，不要包含任何其他说明或文字。\n"
-                    f"作为参考，当前时间是: {current_time_str}\n"
-                    f"需要转换的时间短语是: '{remind_time_str}'\n"
-                    f"规则:\n"
-                    f"- 如果用户没有明确指出是上午还是下午，请根据当前时间判断。例如，如果当前是上午，用户说‘8点’，则应理解为今天的8点；如果当前是下午，用户说‘8点’，则应理解为今天的20点。\n"
-                    f"- 如果转换后的时间早于当前时间，则应理解为第二天的时间。\n"
-                    f"示例:\n"
-                    f"- 当前时间: 2025-09-16 10:00:00, 用户说: '8点' -> '2025-09-17 08:00:00'\n"
-                    f"- 当前时间: 2025-09-16 14:00:00, 用户说: '8点' -> '2025-09-16 20:00:00'\n"
-                    f"- 当前时间: 2025-09-16 23:00:00, 用户说: '晚上10点' -> '2025-09-17 22:00:00'"
-                )
-                
-                success, response, _, _ = await llm_api.generate_with_model(
-                    prompt, 
-                    model_config=model_to_use,
-                    request_type="plugin.reminder.time_parser"
-                )
-                
-                if not success or not response:
-                    raise ValueError(f"LLM未能返回有效的时间字符串: {response}")
-
-                converted_time_str = response.strip()
-                logger.info(f"[ReminderPlugin] LLM 转换结果: '{converted_time_str}'")
-                target_time = parse_datetime(converted_time_str, fuzzy=False)
-
-        except Exception as e:
-            logger.error(f"[ReminderPlugin] 无法解析或转换时间字符串 '{remind_time_str}': {e}", exc_info=True)
-            await self.send_text(f"抱歉，我无法理解您说的时间 '{remind_time_str}'，提醒设置失败。")
-            return False, f"无法解析时间 '{remind_time_str}'"
-
-        now = datetime.now()
-        if target_time <= now:
-            await self.send_text("提醒时间必须是一个未来的时间点哦，提醒设置失败。")
-            return False, "提醒时间必须在未来"
-
-        delay_seconds = (target_time - now).total_seconds()
-
-        # 2. 解析用户
-        person_manager = get_person_info_manager()
-        user_id_to_remind = None
-        user_name_to_remind = ""
-        
-        assert isinstance(user_name, str)
-        
-        if user_name.strip() in ["自己", "我", "me"]:
-            user_id_to_remind = self.user_id
-            user_name_to_remind = self.user_nickname
-        else:
-            # 1. 精确匹配
-            user_info = await person_manager.get_person_info_by_name(user_name)
-
-            # 2. 包含匹配
-            if not user_info:
-                for person_id, name in person_manager.person_name_list.items():
-                    if user_name in name:
-                        user_info = await person_manager.get_values(person_id, ["user_id", "user_nickname"])
-                        break
-            
-            # 3. 模糊匹配 (此处简化为字符串相似度)
-            if not user_info:
-                best_match = None
-                highest_similarity = 0
-                for person_id, name in person_manager.person_name_list.items():
-                    import difflib
-                    similarity = difflib.SequenceMatcher(None, user_name, name).ratio()
-                    if similarity > highest_similarity:
-                        highest_similarity = similarity
-                        best_match = person_id
-                
-                if best_match and highest_similarity > 0.6: # 相似度阈值
-                    user_info = await person_manager.get_values(best_match, ["user_id", "user_nickname"])
-
-            if not user_info or not user_info.get("user_id"):
-                logger.warning(f"[ReminderPlugin] 找不到名为 '{user_name}' 的用户")
-                await self.send_text(f"抱歉，我的联系人里找不到叫做 '{user_name}' 的人，提醒设置失败。")
-                return False, f"用户 '{user_name}' 不存在"
-            user_id_to_remind = user_info.get("user_id")
-            user_name_to_remind = user_info.get("user_nickname") or user_name
-
-        # 3. 创建并调度异步任务
-        try:
-            assert user_id_to_remind is not None
-            assert event_details is not None
-            
-            reminder_task = ReminderTask(
-                delay=delay_seconds,
-                stream_id=self.chat_stream.stream_id,
-                group_id=self.chat_stream.group_info.group_id if self.is_group and self.chat_stream.group_info else None,
-                is_group=self.is_group,
-                target_user_id=str(user_id_to_remind),
-                target_user_name=str(user_name_to_remind),
-                event_details=str(event_details),
-                creator_name=str(self.user_nickname),
-                chat_stream=self.chat_stream
-            )
-            await async_task_manager.add_task(reminder_task)
-            
-            # 4. 生成并发送确认消息
-            extra_info = f"你已经成功设置了一个提醒，请以一种符合你人设的、俏皮的方式回复用户。\n提醒时间: {target_time.strftime('%Y-%m-%d %H:%M:%S')}\n提醒对象: {user_name_to_remind}\n提醒内容: {event_details}"
-            last_message = self.chat_stream.context.get_last_message()
-            success, reply_set, _ = await generator_api.generate_reply(
-                chat_stream=self.chat_stream,
-                extra_info=extra_info,
-                reply_message=last_message.to_dict(),
-                request_type="plugin.reminder.confirm_message"
-            )
-            if success and reply_set:
-                for _, text in reply_set:
-                    await self.send_text(text)
-            else:
-                # Fallback message
-                fallback_message = f"好的，我记下了。\n将在 {target_time.strftime('%Y-%m-%d %H:%M:%S')} 提醒 {user_name_to_remind}：\n{event_details}"
-                await self.send_text(fallback_message)
-            
-            return True, "提醒设置成功"
-        except Exception as e:
-            logger.error(f"[ReminderPlugin] 创建提醒任务时出错: {e}", exc_info=True)
-            await self.send_text("抱歉，设置提醒时发生了一点内部错误。")
-            return False, "设置提醒时发生内部错误"
-
-
-# =============================== Plugin ===============================
-
-@register_plugin
-class ReminderPlugin(BasePlugin):
-    """一个能从对话中智能识别并设置定时提醒的插件。"""
-
-    # --- 插件基础信息 ---
-    plugin_name = "reminder_plugin"
-    enable_plugin = True
-    dependencies = []
-    python_dependencies = []
-    config_file_name = "config.toml"
-    config_schema = {}
-
-    def get_plugin_components(self) -> List[Tuple[ActionInfo, Type[BaseAction]]]:
-        """注册插件的所有功能组件。"""
-        return [
-            (RemindAction.get_action_info(), RemindAction)
-        ]
diff --git a/src/plugins/built_in/set_typing_status/_manifest.json b/src/plugins/built_in/set_typing_status/_manifest.json
deleted file mode 100644
index 45364c44a..000000000
--- a/src/plugins/built_in/set_typing_status/_manifest.json
+++ /dev/null
@@ -1,18 +0,0 @@
-{
-    
-    "manifest_version": 1,
-    "name": "Set Typing Status",
-    "description": "一个在LLM生成回复时设置私聊输入状态的插件。",
-    "version": "1.0.0",
-    "author": {
-        "name": "MoFox-Studio"
-    },
-    "license": "MIT",
-    "homepage_url": "",
-    "repository_url": "",
-    "keywords": ["typing", "status", "private chat"],
-    "categories": ["utility"],
-    "host_application": {
-        "min_version": "0.10.0"
-    }
-}
\ No newline at end of file
diff --git a/src/plugins/built_in/set_typing_status/plugin.py b/src/plugins/built_in/set_typing_status/plugin.py
deleted file mode 100644
index 6eef98b19..000000000
--- a/src/plugins/built_in/set_typing_status/plugin.py
+++ /dev/null
@@ -1,63 +0,0 @@
-from typing import List, Tuple, Type
-import logging
-
-from src.plugin_system import (
-    BasePlugin,
-    register_plugin,
-    ComponentInfo,
-    BaseEventHandler,
-    EventType,
-)
-from src.plugin_system.base.base_event import HandlerResult
-from src.plugin_system.apis import send_api
-
-logger = logging.getLogger(__name__)
-
-
-class SetTypingStatusHandler(BaseEventHandler):
-    """在LLM处理私聊消息后设置“正在输入”状态的事件处理器。"""
-
-    handler_name = "set_typing_status_handler"
-    handler_description = "在LLM生成回复后，将用户的聊天状态设置为“正在输入”。"
-    init_subscribe = [EventType.POST_LLM]
-
-    async def execute(self, params: dict) -> HandlerResult:
-        message = params.get("message")
-        if not message or not message.is_private_message:
-            return HandlerResult(success=True, continue_process=True)
-
-        user_id = message.message_info.user_info.user_id
-        if not user_id:
-            return HandlerResult(success=False, continue_process=True, message="无法获取用户ID")
-        try:
-            params = {"user_id": user_id, "event_type": 1}
-            await send_api.adapter_command_to_stream(
-                action="set_input_status",
-                params=params,
-                stream_id=message.stream_id,
-            )
-            logger.debug(f"成功为用户 {user_id} 设置“正在输入”状态。")
-            return HandlerResult(success=True, continue_process=True)
-        except Exception as e:
-            logger.error(f"为用户 {user_id} 设置“正在输入”状态时出错: {e}")
-            return HandlerResult(success=False, continue_process=True, message=str(e))
-
-
-@register_plugin
-class SetTypingStatusPlugin(BasePlugin):
-    """一个在LLM生成回复时设置私聊输入状态的插件。"""
-
-    plugin_name = "set_typing_status"
-    enable_plugin = True
-    dependencies = []
-    python_dependencies = []
-    config_file_name = ""
-
-    config_schema = {}
-
-    def get_plugin_components(self) -> List[Tuple[ComponentInfo, Type]]:
-        """注册插件的功能组件。"""
-        return [(SetTypingStatusHandler.get_handler_info(), SetTypingStatusHandler)]
-
-    def register_plugin(self) -> bool:
-        return True
diff --git a/src/plugins/built_in/at_user_plugin/__init__.py b/src/plugins/built_in/social_toolkit_plugin/__init__.py
similarity index 100%
rename from src/plugins/built_in/at_user_plugin/__init__.py
rename to src/plugins/built_in/social_toolkit_plugin/__init__.py
diff --git a/src/plugins/built_in/social_toolkit_plugin/_manifest.json b/src/plugins/built_in/social_toolkit_plugin/_manifest.json
new file mode 100644
index 000000000..d1bbb2a15
--- /dev/null
+++ b/src/plugins/built_in/social_toolkit_plugin/_manifest.json
@@ -0,0 +1,25 @@
+{
+  "manifest_version": 1,
+  "name": "MoFox-Bot工具箱",
+  "version": "1.0.0",
+  "description": "一个集合多种实用功能的插件，旨在提升聊天体验和效率。",
+  "author": {
+    "name": "MoFox-Studio",
+    "url": "https://github.com/MoFox-Studio"
+  },
+  "license": "GPL-v3.0-or-later",
+  
+  "host_application": {
+    "min_version": "0.10.0"
+  },
+  "keywords": ["emoji", "reaction", "like", "表情", "回应", "点赞"],
+  "categories": ["Chat", "Integration"],
+  
+  "default_locale": "zh-CN",
+  "locales_path": "_locales",
+  
+  "plugin_info": {
+    "is_built_in": "true",
+    "plugin_type": "functional"
+  }
+}
diff --git a/src/plugins/built_in/social_toolkit_plugin/plugin.py b/src/plugins/built_in/social_toolkit_plugin/plugin.py
new file mode 100644
index 000000000..7bca6ab08
--- /dev/null
+++ b/src/plugins/built_in/social_toolkit_plugin/plugin.py
@@ -0,0 +1,569 @@
+import re
+from typing import List, Tuple, Type, Optional
+
+from src.plugin_system import (
+    BasePlugin,
+    register_plugin,
+    BaseAction,
+    ComponentInfo,
+    ActionActivationType,
+    ConfigField,
+)
+from src.common.logger import get_logger
+from .qq_emoji_list import qq_face
+from src.plugin_system.base.component_types import ChatType
+from src.person_info.person_info import get_person_info_manager
+from dateutil.parser import parse as parse_datetime
+from src.manager.async_task_manager import AsyncTask, async_task_manager
+from src.plugin_system.apis import send_api, llm_api, generator_api
+from src.plugin_system.base.component_types import ComponentType
+from typing import Optional
+from src.chat.message_receive.chat_stream import ChatStream
+import asyncio
+import datetime
+
+logger = get_logger("set_emoji_like_plugin")
+
+# ============================ AsyncTask ============================
+
+
+class ReminderTask(AsyncTask):
+    def __init__(
+        self,
+        delay: float,
+        stream_id: str,
+        group_id: Optional[str],
+        is_group: bool,
+        target_user_id: str,
+        target_user_name: str,
+        event_details: str,
+        creator_name: str,
+        chat_stream: ChatStream,
+    ):
+        super().__init__(task_name=f"ReminderTask_{target_user_id}_{datetime.datetime.now().timestamp()}")
+        self.delay = delay
+        self.stream_id = stream_id
+        self.group_id = group_id
+        self.is_group = is_group
+        self.target_user_id = target_user_id
+        self.target_user_name = target_user_name
+        self.event_details = event_details
+        self.creator_name = creator_name
+        self.chat_stream = chat_stream
+
+    async def run(self):
+        try:
+            if self.delay > 0:
+                logger.info(f"等待 {self.delay:.2f} 秒后执行提醒...")
+                await asyncio.sleep(self.delay)
+
+            logger.info(f"执行提醒任务: 给 {self.target_user_name} 发送关于 '{self.event_details}' 的提醒")
+
+            extra_info = f"现在是提醒时间，请你以一种符合你人设的、俏皮的方式提醒 {self.target_user_name}。\n提醒内容: {self.event_details}\n设置提醒的人: {self.creator_name}"
+            last_message = self.chat_stream.context_manager.context.get_last_message()
+            reply_message_dict = last_message.flatten() if last_message else None
+            success, reply_set, _ = await generator_api.generate_reply(
+                chat_stream=self.chat_stream,
+                extra_info=extra_info,
+                reply_message=reply_message_dict,
+                request_type="plugin.reminder.remind_message",
+            )
+
+            if success and reply_set:
+                for i, (_, text) in enumerate(reply_set):
+                    if self.is_group:
+                        message_payload = []
+                        if i == 0:
+                            message_payload.append({"type": "at", "data": {"qq": self.target_user_id}})
+                        message_payload.append({"type": "text", "data": {"text": f" {text}"}})
+                        await send_api.adapter_command_to_stream(
+                            action="send_group_msg",
+                            params={"group_id": self.group_id, "message": message_payload},
+                            stream_id=self.stream_id,
+                        )
+                    else:
+                        await send_api.text_to_stream(text=text, stream_id=self.stream_id)
+            else:
+                # Fallback message
+                reminder_text = f"叮咚！这是 {self.creator_name} 让我准时提醒你的事情：\n\n{self.event_details}"
+                if self.is_group:
+                    message_payload = [
+                        {"type": "at", "data": {"qq": self.target_user_id}},
+                        {"type": "text", "data": {"text": f" {reminder_text}"}},
+                    ]
+                    await send_api.adapter_command_to_stream(
+                        action="send_group_msg",
+                        params={"group_id": self.group_id, "message": message_payload},
+                        stream_id=self.stream_id,
+                    )
+                else:
+                    await send_api.text_to_stream(text=reminder_text, stream_id=self.stream_id)
+
+            logger.info(f"提醒任务 {self.task_name} 成功完成。")
+
+        except Exception as e:
+            logger.error(f"执行提醒任务 {self.task_name} 时出错: {e}", exc_info=True)
+
+
+# =============================== Actions ===============================
+
+
+def get_emoji_id(emoji_input: str) -> str | None:
+    """根据输入获取表情ID"""
+    # 如果输入本身就是数字ID，直接返回
+    if emoji_input.isdigit() or (isinstance(emoji_input, str) and emoji_input.startswith("😊")):
+        if emoji_input in qq_face:
+            return emoji_input
+
+    # 尝试从 "[表情：xxx]" 格式中提取
+    match = re.search(r"\[表情：(.+?)\]", emoji_input)
+    if match:
+        emoji_name = match.group(1).strip()
+    else:
+        emoji_name = emoji_input.strip()
+
+    # 遍历查找
+    for key, value in qq_face.items():
+        # value 的格式是 "[表情：xxx]"
+        if f"[表情：{emoji_name}]" == value:
+            return key
+
+    return None
+
+
+# ===== Action组件 =====
+
+
+class PokeAction(BaseAction):
+    """发送戳一戳动作"""
+
+    # === 基本信息（必须填写）===
+    action_name = "poke_user"
+    action_description = "向用户发送戳一戳"
+    activation_type = ActionActivationType.ALWAYS
+    parallel_action = True
+
+    # === 功能描述（必须填写）===
+    action_parameters = {
+        "user_name": "需要戳一戳的用户的名字 (可选)",
+        "user_id": "需要戳一戳的用户的ID (可选，优先级更高)",
+        "times": "需要戳一戳的次数 (默认为 1)",
+    }
+    action_require = ["当需要戳某个用户时使用", "当你想提醒特定用户时使用"]
+    llm_judge_prompt = """
+    判定是否需要使用戳一戳动作的条件：
+    1. **关键**: 这是一个高消耗的动作，请仅在绝对必要时使用，例如用户明确要求或作为提醒的关键部分。请极其谨慎地使用。
+    2. **用户请求**: 用户明确要求使用戳一戳。
+    3. **互动提醒**: 你想以一种有趣的方式提醒或与某人互动，但请确保这是对话的自然延伸，而不是无故打扰。
+    4. **上下文需求**: 上下文明确需要你戳一个或多个人。
+    5. **频率限制**: 如果最近已经戳过，或者用户情绪不高，请绝对不要使用。
+
+    请回答"是"或"否"。
+    """
+    associated_types = ["text"]
+
+    async def execute(self) -> Tuple[bool, str]:
+        """执行戳一戳的动作"""
+        user_id = self.action_data.get("user_id")
+        user_name = self.action_data.get("user_name")
+
+        try:
+            times = int(self.action_data.get("times", 1))
+        except (ValueError, TypeError):
+            times = 1
+
+        # 优先使用 user_id
+        if not user_id:
+            if not user_name:
+                logger.warning("戳一戳动作缺少 'user_id' 或 'user_name' 参数。")
+                return False, "缺少用户标识参数"
+
+            # 备用方案：通过 user_name 查找
+            user_info = await get_person_info_manager().get_person_info_by_name(user_name)
+            if not user_info or not user_info.get("user_id"):
+                logger.info(f"找不到名为 '{user_name}' 的用户。")
+                return False, f"找不到名为 '{user_name}' 的用户"
+            user_id = user_info.get("user_id")
+
+        display_name = user_name or user_id
+
+        for i in range(times):
+            logger.info(f"正在向 {display_name} ({user_id}) 发送第 {i + 1}/{times} 次戳一戳...")
+            await self.send_command(
+                "SEND_POKE", args={"qq_id": user_id}, display_message=f"戳了戳 {display_name} ({i + 1}/{times})"
+            )
+            # 添加一个小的延迟，以避免发送过快
+            await asyncio.sleep(0.5)
+
+        success_message = f"已向 {display_name} 发送 {times} 次戳一戳。"
+        await self.store_action_info(
+            action_build_into_prompt=True, action_prompt_display=success_message, action_done=True
+        )
+        return True, success_message
+
+
+class SetEmojiLikeAction(BaseAction):
+    """设置消息表情回应"""
+
+    # === 基本信息（必须填写）===
+    action_name = "set_emoji_like"
+    action_description = "为某条已经存在的消息添加‘贴表情’回应（类似点赞），而不是发送新消息。可以在觉得某条消息非常有趣、值得赞同或者需要特殊情感回应时主动使用。"
+    activation_type = ActionActivationType.ALWAYS  # 消息接收时激活(?)
+    chat_type_allow = ChatType.GROUP
+    parallel_action = True
+
+    # === 功能描述（必须填写）===
+    # 从 qq_face 字典中提取所有表情名称用于提示
+    emoji_options = []
+    for name in qq_face.values():
+        match = re.search(r"\[表情：(.+?)\]", name)
+        if match:
+            emoji_options.append(match.group(1))
+
+    action_parameters = {
+        "set": "是否设置回应 (True/False)",
+    }
+    action_require = [
+        "当需要对一个已存在消息进行‘贴表情’回应时使用",
+        "这是一个对旧消息的操作，而不是发送新消息",
+        "如果你想发送一个新的表情包消息，请使用 'emoji' 动作",
+    ]
+    llm_judge_prompt = """
+    判定是否需要使用贴表情动作的条件：
+    1. 用户明确要求使用贴表情包
+    2. 这是一个适合表达强烈情绪的场合
+    3. 不要发送太多表情包，如果你已经发送过多个表情包则回答"否"
+    
+    请回答"是"或"否"。
+    """
+    associated_types = ["text"]
+
+    async def execute(self) -> Tuple[bool, str]:
+        """执行设置表情回应的动作"""
+        message_id = None
+        set_like = self.action_data.get("set", True)
+        if self.has_action_message:
+            logger.debug(str(self.action_message))
+            if isinstance(self.action_message, dict):
+                message_id = self.action_message.get("message_id")
+            logger.info(f"获取到的消息ID: {message_id}")
+        else:
+            logger.error("未提供消息ID")
+            await self.store_action_info(
+                action_build_into_prompt=True,
+                action_prompt_display=f"执行了set_emoji_like动作：{self.action_name},失败: 未提供消息ID",
+                action_done=False,
+            )
+            return False, "未提供消息ID"
+        available_models = llm_api.get_available_models()
+        if "utils_small" not in available_models:
+                logger.error("未找到 'utils_small' 模型配置，无法选择表情")
+                return False, "表情选择功能配置错误"
+
+        model_to_use = available_models["utils_small"]
+            
+        # 获取最近的对话历史作为上下文
+        context_text = ""
+        if self.action_message:
+            context_text = self.action_message.get("processed_plain_text", "")
+        else:
+            logger.error("无法找到动作选择的原始消息")
+            return False, "无法找到动作选择的原始消息"
+        
+        prompt = (
+                f"根据以下这条消息，从列表中选择一个最合适的表情名称来回应这条消息。\n"
+                f"消息内容: '{context_text}'\n"
+                f"可用表情列表: {', '.join(self.emoji_options)}\n"
+                f"你的任务是：只输出你选择的表情的名称，不要包含任何其他文字或标点。\n"
+                f"例如，如果觉得应该用'赞'，就只输出'赞'。"
+            )
+
+        success, response, _, _ = await llm_api.generate_with_model(
+                prompt, model_config=model_to_use, request_type="plugin.set_emoji_like.select_emoji"
+            )
+
+        if not success or not response:
+                logger.error("二级LLM未能选择有效的表情。")
+                return False, "无法找到合适的表情。"
+
+        chosen_emoji_name = response.strip()
+        logger.info(f"二级LLM选择的表情是: '{chosen_emoji_name}'")
+        emoji_id = get_emoji_id(chosen_emoji_name)
+
+        if not emoji_id:
+                logger.error(f"二级LLM选择的表情 '{chosen_emoji_name}' 仍然无法匹配到有效的表情ID。")
+                await self.store_action_info(
+                    action_build_into_prompt=True,
+                    action_prompt_display=f"执行了set_emoji_like动作：{self.action_name},失败: 找不到表情: '{chosen_emoji_name}'",
+                    action_done=False,
+                )
+                return False, f"找不到表情: '{chosen_emoji_name}'。"
+
+        # 4. 使用适配器API发送命令
+        if not message_id:
+            logger.error("未提供消息ID")
+            await self.store_action_info(
+                action_build_into_prompt=True,
+                action_prompt_display=f"执行了set_emoji_like动作：{self.action_name},失败: 未提供消息ID",
+                action_done=False,
+            )
+            return False, "未提供消息ID"
+
+        try:
+            # 使用适配器API发送贴表情命令
+            success = await self.send_command(
+                command_name="set_emoji_like",
+                args={"message_id": message_id, "emoji_id": emoji_id, "set": set_like},
+                storage_message=False,
+            )
+            if success:
+                logger.info("设置表情回应成功")
+                await self.store_action_info(
+                    action_build_into_prompt=True,
+                    action_prompt_display=f"执行了set_emoji_like动作,{chosen_emoji_name},设置表情回应: {emoji_id}, 是否设置: {set_like}",
+                    action_done=True,
+                )
+                return True, "成功设置表情回应"
+            else:
+                logger.error("设置表情回应失败")
+                await self.store_action_info(
+                    action_build_into_prompt=True,
+                    action_prompt_display=f"执行了set_emoji_like动作：{self.action_name},失败",
+                    action_done=False,
+                )
+                return False, "设置表情回应失败"
+
+        except Exception as e:
+            logger.error(f"设置表情回应失败: {e}")
+            await self.store_action_info(
+                action_build_into_prompt=True,
+                action_prompt_display=f"执行了set_emoji_like动作：{self.action_name},失败: {e}",
+                action_done=False,
+            )
+            return False, f"设置表情回应失败: {e}"
+
+
+class RemindAction(BaseAction):
+    """一个能从对话中智能识别并设置定时提醒的动作。"""
+
+    # === 基本信息 ===
+    action_name = "set_reminder"
+    action_description = "根据用户的对话内容，智能地设置一个未来的提醒事项。"
+    activation_type = (ActionActivationType.KEYWORD,)
+    activation_keywords = ["提醒", "叫我", "记得", "别忘了"]
+    chat_type_allow = ChatType.ALL
+    parallel_action = True
+
+    # === LLM 判断与参数提取 ===
+    llm_judge_prompt = ""
+    action_parameters = {}
+    action_require = [
+        "当用户请求在未来的某个时间点提醒他/她或别人某件事时使用",
+        "适用于包含明确时间信息和事件描述的对话",
+        "例如：'10分钟后提醒我收快递'、'明天早上九点喊一下李四参加晨会'",
+    ]
+
+    async def execute(self) -> Tuple[bool, str]:
+        """执行设置提醒的动作"""
+        user_name = self.action_data.get("user_name")
+        remind_time_str = self.action_data.get("remind_time")
+        event_details = self.action_data.get("event_details")
+
+        if not all([user_name, remind_time_str, event_details]):
+            missing_params = [
+                p
+                for p, v in {
+                    "user_name": user_name,
+                    "remind_time": remind_time_str,
+                    "event_details": event_details,
+                }.items()
+                if not v
+            ]
+            error_msg = f"缺少必要的提醒参数: {', '.join(missing_params)}"
+            logger.warning(f"[ReminderPlugin] LLM未能提取完整参数: {error_msg}")
+            return False, error_msg
+
+        # 1. 解析时间
+        try:
+            assert isinstance(remind_time_str, str)
+            # 优先尝试直接解析
+            try:
+                target_time = parse_datetime(remind_time_str, fuzzy=True)
+            except Exception:
+                # 如果直接解析失败，调用 LLM 进行转换
+                logger.info(f"[ReminderPlugin] 直接解析时间 '{remind_time_str}' 失败，尝试使用 LLM 进行转换...")
+
+                # 获取所有可用的模型配置
+                available_models = llm_api.get_available_models()
+                if "utils_small" not in available_models:
+                    raise ValueError("未找到 'utils_small' 模型配置，无法解析时间")
+
+                # 明确使用 'planner' 模型
+                model_to_use = available_models["utils_small"]
+
+                # 在执行时动态获取当前时间
+                current_time_str = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+                prompt = (
+                    f"请将以下自然语言时间短语转换为一个未来的、标准的 'YYYY-MM-DD HH:MM:SS' 格式。"
+                    f"请只输出转换后的时间字符串，不要包含任何其他说明或文字。\n"
+                    f"作为参考，当前时间是: {current_time_str}\n"
+                    f"需要转换的时间短语是: '{remind_time_str}'\n"
+                    f"规则:\n"
+                    f"- 如果用户没有明确指出是上午还是下午，请根据当前时间判断。例如，如果当前是上午，用户说‘8点’，则应理解为今天的8点；如果当前是下午，用户说‘8点’，则应理解为今天的20点。\n"
+                    f"- 如果转换后的时间早于当前时间，则应理解为第二天的时间。\n"
+                    f"示例:\n"
+                    f"- 当前时间: 2025-09-16 10:00:00, 用户说: '8点' -> '2025-09-17 08:00:00'\n"
+                    f"- 当前时间: 2025-09-16 14:00:00, 用户说: '8点' -> '2025-09-16 20:00:00'\n"
+                    f"- 当前时间: 2025-09-16 23:00:00, 用户说: '晚上10点' -> '2025-09-17 22:00:00'"
+                )
+
+                success, response, _, _ = await llm_api.generate_with_model(
+                    prompt, model_config=model_to_use, request_type="plugin.reminder.time_parser"
+                )
+
+                if not success or not response:
+                    raise ValueError(f"LLM未能返回有效的时间字符串: {response}")
+
+                converted_time_str = response.strip()
+                logger.info(f"[ReminderPlugin] LLM 转换结果: '{converted_time_str}'")
+                target_time = parse_datetime(converted_time_str, fuzzy=False)
+
+        except Exception as e:
+            logger.error(f"[ReminderPlugin] 无法解析或转换时间字符串 '{remind_time_str}': {e}", exc_info=True)
+            await self.send_text(f"抱歉，我无法理解您说的时间 '{remind_time_str}'，提醒设置失败。")
+            return False, f"无法解析时间 '{remind_time_str}'"
+
+        now = datetime.datetime.now()
+        if target_time <= now:
+            await self.send_text("提醒时间必须是一个未来的时间点哦，提醒设置失败。")
+            return False, "提醒时间必须在未来"
+
+        delay_seconds = (target_time - now).total_seconds()
+
+        # 2. 解析用户
+        person_manager = get_person_info_manager()
+        user_id_to_remind = None
+        user_name_to_remind = ""
+
+        assert isinstance(user_name, str)
+
+        if user_name.strip() in ["自己", "我", "me"]:
+            user_id_to_remind = self.user_id
+            user_name_to_remind = self.user_nickname
+        else:
+            # 1. 精确匹配
+            user_info = await person_manager.get_person_info_by_name(user_name)
+
+            # 2. 包含匹配
+            if not user_info:
+                for person_id, name in person_manager.person_name_list.items():
+                    if user_name in name:
+                        user_info = await person_manager.get_values(person_id, ["user_id", "user_nickname"])
+                        break
+
+            # 3. 模糊匹配 (此处简化为字符串相似度)
+            if not user_info:
+                best_match = None
+                highest_similarity = 0
+                for person_id, name in person_manager.person_name_list.items():
+                    import difflib
+
+                    similarity = difflib.SequenceMatcher(None, user_name, name).ratio()
+                    if similarity > highest_similarity:
+                        highest_similarity = similarity
+                        best_match = person_id
+
+                if best_match and highest_similarity > 0.6:  # 相似度阈值
+                    user_info = await person_manager.get_values(best_match, ["user_id", "user_nickname"])
+
+            if not user_info or not user_info.get("user_id"):
+                logger.warning(f"[ReminderPlugin] 找不到名为 '{user_name}' 的用户")
+                await self.send_text(f"抱歉，我的联系人里找不到叫做 '{user_name}' 的人，提醒设置失败。")
+                return False, f"用户 '{user_name}' 不存在"
+            user_id_to_remind = user_info.get("user_id")
+            user_name_to_remind = user_info.get("user_nickname") or user_name
+
+        # 3. 创建并调度异步任务
+        try:
+            assert user_id_to_remind is not None
+            assert event_details is not None
+
+            reminder_task = ReminderTask(
+                delay=delay_seconds,
+                stream_id=self.chat_stream.stream_id,
+                group_id=self.chat_stream.group_info.group_id
+                if self.is_group and self.chat_stream.group_info
+                else None,
+                is_group=self.is_group,
+                target_user_id=str(user_id_to_remind),
+                target_user_name=str(user_name_to_remind),
+                event_details=str(event_details),
+                creator_name=str(self.user_nickname),
+                chat_stream=self.chat_stream,
+            )
+            await async_task_manager.add_task(reminder_task)
+
+            # 4. 生成并发送确认消息
+            extra_info = f"你已经成功设置了一个提醒，请以一种符合你人设的、俏皮的方式回复用户。\n提醒时间: {target_time.strftime('%Y-%m-%d %H:%M:%S')}\n提醒对象: {user_name_to_remind}\n提醒内容: {event_details}"
+            last_message = self.chat_stream.context_manager.context.get_last_message()
+            reply_message_dict = last_message.flatten() if last_message else None
+            success, reply_set, _ = await generator_api.generate_reply(
+                chat_stream=self.chat_stream,
+                extra_info=extra_info,
+                reply_message=reply_message_dict,
+                request_type="plugin.reminder.confirm_message",
+            )
+            if success and reply_set:
+                for _, text in reply_set:
+                    await self.send_text(text)
+            else:
+                # Fallback message
+                fallback_message = f"好的，我记下了。\n将在 {target_time.strftime('%Y-%m-%d %H:%M:%S')} 提醒 {user_name_to_remind}：\n{event_details}"
+                await self.send_text(fallback_message)
+
+            return True, "提醒设置成功"
+        except Exception as e:
+            logger.error(f"[ReminderPlugin] 创建提醒任务时出错: {e}", exc_info=True)
+            await self.send_text("抱歉，设置提醒时发生了一点内部错误。")
+            return False, "设置提醒时发生内部错误"
+
+
+# ===== 插件注册 =====
+@register_plugin
+class SetEmojiLikePlugin(BasePlugin):
+    """一个集合多种实用功能的插件，旨在提升聊天体验和效率。"""
+
+    # 插件基本信息
+    plugin_name: str = "social_toolkit_plugin"  # 内部标识符
+    enable_plugin: bool = True
+    dependencies: List[str] = []  # 插件依赖列表
+    python_dependencies: List[str] = []  # Python包依赖列表，现在使用内置API
+    config_file_name: str = "config.toml"  # 配置文件名
+
+    # 配置节描述
+    config_section_descriptions = {"plugin": "插件基本信息", "components": "插件组件"}
+
+    # 配置Schema定义
+    config_schema: dict = {
+        "plugin": {
+            "name": ConfigField(type=str, default="set_emoji_like", description="插件名称"),
+            "version": ConfigField(type=str, default="1.0.0", description="插件版本"),
+            "enabled": ConfigField(type=bool, default=True, description="是否启用插件"),
+            "config_version": ConfigField(type=str, default="1.1", description="配置版本"),
+        },
+        "components": {
+            "action_set_emoji_like": ConfigField(type=bool, default=True, description="是否启用设置表情回应功能"),
+            "action_poke_enable": ConfigField(type=bool, default=True, description="是否启用戳一戳功能"),
+            "action_set_reminder_enable": ConfigField(type=bool, default=True, description="是否启用定时提醒功能"),
+        },
+    }
+
+    def get_plugin_components(self) -> List[Tuple[ComponentInfo, Type]]:
+        enable_components = []
+        if self.get_config("components.action_set_emoji_like"):
+            enable_components.append((SetEmojiLikeAction.get_action_info(), SetEmojiLikeAction))
+        if self.get_config("components.action_poke_enable"):
+            enable_components.append((PokeAction.get_action_info(), PokeAction))
+        if self.get_config("components.action_set_reminder_enable"):
+            enable_components.append((RemindAction.get_action_info(), RemindAction))
+        return enable_components
diff --git a/plugins/set_emoji_like/qq_emoji_list.py b/src/plugins/built_in/social_toolkit_plugin/qq_emoji_list.py
similarity index 100%
rename from plugins/set_emoji_like/qq_emoji_list.py
rename to src/plugins/built_in/social_toolkit_plugin/qq_emoji_list.py
diff --git a/src/plugins/reminder_plugin/plugin.py b/src/plugins/reminder_plugin/plugin.py
deleted file mode 100644
index 31ea899df..000000000
--- a/src/plugins/reminder_plugin/plugin.py
+++ /dev/null
@@ -1,216 +0,0 @@
-import asyncio
-from datetime import datetime
-from typing import List, Tuple, Type
-from dateutil.parser import parse as parse_datetime
-
-from src.common.logger import get_logger
-from src.manager.async_task_manager import AsyncTask, async_task_manager
-from src.person_info.person_info import get_person_info_manager
-from src.plugin_system import (
-    BaseAction,
-    ActionInfo,
-    BasePlugin,
-    register_plugin,
-    ActionActivationType,
-)
-from src.plugin_system.apis import send_api
-from src.plugin_system.base.component_types import ChatType
-
-logger = get_logger(__name__)
-
-
-# ============================ AsyncTask ============================
-
-
-class ReminderTask(AsyncTask):
-    def __init__(
-        self,
-        delay: float,
-        stream_id: str,
-        is_group: bool,
-        target_user_id: str,
-        target_user_name: str,
-        event_details: str,
-        creator_name: str,
-    ):
-        super().__init__(task_name=f"ReminderTask_{target_user_id}_{datetime.now().timestamp()}")
-        self.delay = delay
-        self.stream_id = stream_id
-        self.is_group = is_group
-        self.target_user_id = target_user_id
-        self.target_user_name = target_user_name
-        self.event_details = event_details
-        self.creator_name = creator_name
-
-    async def run(self):
-        try:
-            if self.delay > 0:
-                logger.info(f"等待 {self.delay:.2f} 秒后执行提醒...")
-                await asyncio.sleep(self.delay)
-
-            logger.info(f"执行提醒任务: 给 {self.target_user_name} 发送关于 '{self.event_details}' 的提醒")
-
-            reminder_text = f"叮咚！这是 {self.creator_name} 让我准时提醒你的事情：\n\n{self.event_details}"
-
-            if self.is_group:
-                # 在群聊中，构造 @ 消息段并发送
-                group_id = self.stream_id.split("_")[-1] if "_" in self.stream_id else self.stream_id
-                message_payload = [
-                    {"type": "at", "data": {"qq": self.target_user_id}},
-                    {"type": "text", "data": {"text": f" {reminder_text}"}},
-                ]
-                await send_api.adapter_command_to_stream(
-                    action="send_group_msg",
-                    params={"group_id": group_id, "message": message_payload},
-                    stream_id=self.stream_id,
-                )
-            else:
-                # 在私聊中，直接发送文本
-                await send_api.text_to_stream(text=reminder_text, stream_id=self.stream_id)
-
-            logger.info(f"提醒任务 {self.task_name} 成功完成。")
-
-        except Exception as e:
-            logger.error(f"执行提醒任务 {self.task_name} 时出错: {e}", exc_info=True)
-
-
-# =============================== Actions ===============================
-
-
-class RemindAction(BaseAction):
-    """一个能从对话中智能识别并设置定时提醒的动作。"""
-
-    # === 基本信息 ===
-    action_name = "set_reminder"
-    action_description = "根据用户的对话内容，智能地设置一个未来的提醒事项。"
-    activation_type = ActionActivationType.LLM_JUDGE
-    chat_type_allow = ChatType.ALL
-
-    # === LLM 判断与参数提取 ===
-    llm_judge_prompt = """
-    判断用户是否意图设置一个未来的提醒。
-    - 必须包含明确的时间点或时间段（如“十分钟后”、“明天下午3点”、“周五”）。
-    - 必须包含一个需要被提醒的事件。
-    - 可能会包含需要提醒的特定人物。
-    - 如果只是普通的聊天或询问时间，则不应触发。
-
-    示例:
-    - "半小时后提醒我开会" -> 是
-    - "明天下午三点叫张三来一下" -> 是
-    - "别忘了周五把报告交了" -> 是
-    - "现在几点了？" -> 否
-    - "我明天下午有空" -> 否
-
-    请只回答"是"或"否"。
-    """
-    action_parameters = {
-        "user_name": "需要被提醒的人的称呼或名字，如果没有明确指定给某人，则默认为'自己'",
-        "remind_time": "描述提醒时间的自然语言字符串，例如'十分钟后'或'明天下午3点'",
-        "event_details": "需要提醒的具体事件内容",
-    }
-    action_require = [
-        "当用户请求在未来的某个时间点提醒他/她或别人某件事时使用",
-        "适用于包含明确时间信息和事件描述的对话",
-        "例如：'10分钟后提醒我收快递'、'明天早上九点喊一下李四参加晨会'",
-    ]
-
-    async def execute(self) -> Tuple[bool, str]:
-        """执行设置提醒的动作"""
-        user_name = self.action_data.get("user_name")
-        remind_time_str = self.action_data.get("remind_time")
-        event_details = self.action_data.get("event_details")
-
-        if not all([user_name, remind_time_str, event_details]):
-            missing_params = [
-                p
-                for p, v in {
-                    "user_name": user_name,
-                    "remind_time": remind_time_str,
-                    "event_details": event_details,
-                }.items()
-                if not v
-            ]
-            error_msg = f"缺少必要的提醒参数: {', '.join(missing_params)}"
-            logger.warning(f"[ReminderPlugin] LLM未能提取完整参数: {error_msg}")
-            return False, error_msg
-
-        # 1. 解析时间
-        try:
-            assert isinstance(remind_time_str, str)
-            target_time = parse_datetime(remind_time_str, fuzzy=True)
-        except Exception as e:
-            logger.error(f"[ReminderPlugin] 无法解析时间字符串 '{remind_time_str}': {e}")
-            await self.send_text(f"抱歉，我无法理解您说的时间 '{remind_time_str}'，提醒设置失败。")
-            return False, f"无法解析时间 '{remind_time_str}'"
-
-        now = datetime.now()
-        if target_time <= now:
-            await self.send_text("提醒时间必须是一个未来的时间点哦，提醒设置失败。")
-            return False, "提醒时间必须在未来"
-
-        delay_seconds = (target_time - now).total_seconds()
-
-        # 2. 解析用户
-        person_manager = get_person_info_manager()
-        user_id_to_remind = None
-        user_name_to_remind = ""
-
-        assert isinstance(user_name, str)
-
-        if user_name.strip() in ["自己", "我", "me"]:
-            user_id_to_remind = self.user_id
-            user_name_to_remind = self.user_nickname
-        else:
-            user_info = await person_manager.get_person_info_by_name(user_name)
-            if not user_info or not user_info.get("user_id"):
-                logger.warning(f"[ReminderPlugin] 找不到名为 '{user_name}' 的用户")
-                await self.send_text(f"抱歉，我的联系人里找不到叫做 '{user_name}' 的人，提醒设置失败。")
-                return False, f"用户 '{user_name}' 不存在"
-            user_id_to_remind = user_info.get("user_id")
-            user_name_to_remind = user_name
-
-        # 3. 创建并调度异步任务
-        try:
-            assert user_id_to_remind is not None
-            assert event_details is not None
-
-            reminder_task = ReminderTask(
-                delay=delay_seconds,
-                stream_id=self.chat_id,
-                is_group=self.is_group,
-                target_user_id=str(user_id_to_remind),
-                target_user_name=str(user_name_to_remind),
-                event_details=str(event_details),
-                creator_name=str(self.user_nickname),
-            )
-            await async_task_manager.add_task(reminder_task)
-
-            # 4. 发送确认消息
-            confirm_message = f"好的，我记下了。\n将在 {target_time.strftime('%Y-%m-%d %H:%M:%S')} 提醒 {user_name_to_remind}：\n{event_details}"
-            await self.send_text(confirm_message)
-
-            return True, "提醒设置成功"
-        except Exception as e:
-            logger.error(f"[ReminderPlugin] 创建提醒任务时出错: {e}", exc_info=True)
-            await self.send_text("抱歉，设置提醒时发生了一点内部错误。")
-            return False, "设置提醒时发生内部错误"
-
-
-# =============================== Plugin ===============================
-
-
-@register_plugin
-class ReminderPlugin(BasePlugin):
-    """一个能从对话中智能识别并设置定时提醒的插件。"""
-
-    # --- 插件基础信息 ---
-    plugin_name = "reminder_plugin"
-    enable_plugin = True
-    dependencies = []
-    python_dependencies = []
-    config_file_name = "config.toml"
-    config_schema = {}
-
-    def get_plugin_components(self) -> List[Tuple[ActionInfo, Type[BaseAction]]]:
-        """注册插件的所有功能组件。"""
-        return [(RemindAction.get_action_info(), RemindAction)]
diff --git a/src/schedule/plan_manager.py b/src/schedule/plan_manager.py
index d72f55275..513a907d5 100644
--- a/src/schedule/plan_manager.py
+++ b/src/schedule/plan_manager.py
@@ -28,20 +28,20 @@ class PlanManager:
         if target_month is None:
             target_month = datetime.now().strftime("%Y-%m")
 
-        if not has_active_plans(target_month):
+        if not await has_active_plans(target_month):
             logger.info(f" {target_month} 没有任何有效的月度计划，将触发同步生成。")
             generation_successful = await self._generate_monthly_plans_logic(target_month)
             return generation_successful
         else:
             logger.info(f"{target_month} 已存在有效的月度计划。")
-            plans = get_active_plans_for_month(target_month)
+            plans = await get_active_plans_for_month(target_month)
             max_plans = global_config.planning_system.max_plans_per_month
             if len(plans) > max_plans:
                 logger.warning(f"当前月度计划数量 ({len(plans)}) 超出上限 ({max_plans})，将自动删除多余的计划。")
                 plans_to_delete = plans[: len(plans) - max_plans]
                 delete_ids = [p.id for p in plans_to_delete]
-                delete_plans_by_ids(delete_ids)  # type: ignore
-                plans = get_active_plans_for_month(target_month)
+                await delete_plans_by_ids(delete_ids)  # type: ignore
+                plans = await get_active_plans_for_month(target_month)
 
             if plans:
                 plan_texts = "\n".join([f"  {i + 1}. {plan.plan_text}" for i, plan in enumerate(plans)])
@@ -64,11 +64,11 @@ class PlanManager:
                 return False
 
             last_month = self._get_previous_month(target_month)
-            archived_plans = get_archived_plans_for_month(last_month)
+            archived_plans = await get_archived_plans_for_month(last_month)
             plans = await self.llm_generator.generate_plans_with_llm(target_month, archived_plans)
 
             if plans:
-                add_new_plans(plans, target_month)
+                await add_new_plans(plans, target_month)
                 logger.info(f"成功为 {target_month} 生成并保存了 {len(plans)} 条月度计划。")
                 return True
             else:
@@ -95,11 +95,11 @@ class PlanManager:
             if target_month is None:
                 target_month = datetime.now().strftime("%Y-%m")
             logger.info(f" 开始归档 {target_month} 的活跃月度计划...")
-            archived_count = archive_active_plans_for_month(target_month)
+            archived_count = await archive_active_plans_for_month(target_month)
             logger.info(f" 成功归档了 {archived_count} 条 {target_month} 的月度计划。")
         except Exception as e:
             logger.error(f" 归档 {target_month} 月度计划时发生错误: {e}")
 
-    def get_plans_for_schedule(self, month: str, max_count: int) -> List:
+    async def get_plans_for_schedule(self, month: str, max_count: int) -> List:
         avoid_days = global_config.planning_system.avoid_repetition_days
-        return get_smart_plans_for_daily_schedule(month, max_count=max_count, avoid_days=avoid_days)
+        return await get_smart_plans_for_daily_schedule(month, max_count=max_count, avoid_days=avoid_days)
diff --git a/template/model_config_template.toml b/template/model_config_template.toml
index d230d161d..69e992a96 100644
--- a/template/model_config_template.toml
+++ b/template/model_config_template.toml
@@ -53,8 +53,8 @@ price_out = 8.0                    # 输出价格（用于API调用统计，单
 #use_anti_truncation = true        # [可选] 启用反截断功能。当模型输出不完整时，系统会自动重试。建议只为有需要的模型（如Gemini）开启。
 
 [[models]]
-model_identifier = "deepseek-ai/DeepSeek-V3"
-name = "siliconflow-deepseek-v3"
+model_identifier = "deepseek-ai/DeepSeek-V3.1-Terminus"
+name = "siliconflow-deepseek-ai/DeepSeek-V3.1-Terminus"
 api_provider = "SiliconFlow"
 price_in = 2.0
 price_out = 8.0
@@ -122,7 +122,7 @@ price_in = 4.0
 price_out = 16.0
 
 [model_task_config.utils] # 在麦麦的一些组件中使用的模型，例如表情包模块，取名模块，关系模块，是麦麦必须的模型
-model_list = ["siliconflow-deepseek-v3"] # 使用的模型列表，每个子项对应上面的模型名称(name)
+model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.1-Terminus"] # 使用的模型列表，每个子项对应上面的模型名称(name)
 temperature = 0.2                        # 模型温度，新V3建议0.1-0.3
 max_tokens = 800                         # 最大输出token数
 #concurrency_count = 2                   # 并发请求数量，默认为1（不并发），设置为2或更高启用并发
@@ -133,28 +133,28 @@ temperature = 0.7
 max_tokens = 800
 
 [model_task_config.replyer] # 首要回复模型，还用于表达器和表达方式学习
-model_list = ["siliconflow-deepseek-v3"]
+model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.1-Terminus"]
 temperature = 0.2                        # 模型温度，新V3建议0.1-0.3
 max_tokens = 800
 
 [model_task_config.planner] #决策：负责决定麦麦该做什么的模型
-model_list = ["siliconflow-deepseek-v3"]
+model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.1-Terminus"]
 temperature = 0.3
 max_tokens = 800
 
 
 [model_task_config.emotion] #负责麦麦的情绪变化
-model_list = ["siliconflow-deepseek-v3"]
+model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.1-Terminus"]
 temperature = 0.3
 max_tokens = 800
 
 [model_task_config.mood] #负责麦麦的心情变化
-model_list = ["siliconflow-deepseek-v3"]
+model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.1-Terminus"]
 temperature = 0.3
 max_tokens = 800
 
 [model_task_config.maizone] # maizone模型
-model_list = ["siliconflow-deepseek-v3"]
+model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.1-Terminus"]
 temperature = 0.7
 max_tokens = 800
 
@@ -181,7 +181,7 @@ temperature = 0.7
 max_tokens = 800
 
 [model_task_config.schedule_generator]#日程表生成模型
-model_list = ["siliconflow-deepseek-v3"]
+model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.1-Terminus"]
 temperature = 0.7
 max_tokens = 1000
 
@@ -191,12 +191,12 @@ temperature = 0.1                   # 低温度确保检测结果稳定
 max_tokens = 200                    # 检测结果不需要太长的输出
 
 [model_task_config.monthly_plan_generator] # 月层计划生成模型
-model_list = ["siliconflow-deepseek-v3"]
+model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.1-Terminus"]
 temperature = 0.7
 max_tokens = 1000
 
 [model_task_config.relationship_tracker] # 用户关系追踪模型
-model_list = ["siliconflow-deepseek-v3"]
+model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.1-Terminus"]
 temperature = 0.7
 max_tokens = 1000
 
@@ -210,12 +210,12 @@ embedding_dimension = 1024
 #------------LPMM知识库模型------------
 
 [model_task_config.lpmm_entity_extract] # 实体提取模型
-model_list = ["siliconflow-deepseek-v3"]
+model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.1-Terminus"]
 temperature = 0.2
 max_tokens = 800
 
 [model_task_config.lpmm_rdf_build] # RDF构建模型
-model_list = ["siliconflow-deepseek-v3"]
+model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.1-Terminus"]
 temperature = 0.2
 max_tokens = 800