```

feat(engine): 添加技能查看工具并优化异步任务管理

- 添加SkillViewTool到引擎加载器中，增强技能管理功能
- 在AgentLoop中引入_active_direct_task来跟踪活跃任务
- 实现直接任务执行时的同步处理逻辑
- 更新工具实例化方式以支持依赖注入

feat(config): 增加智能体运行时参数配置支持

- 扩展AgentDefaultsConfig添加max_tokens和temperature字段
- 实现配置解析函数_first_config_value处理多个配置源
- 支持通过Web API动态更新智能体运行时参数
- 添加前端页面配置表单和验证逻辑

refactor(provider): 统一最大令牌数参数类型为可选整型

- 将所有LLM提供者的max_tokens参数改为int | None类型
- 为AnthropicProvider实现模型特定的最大令牌数默认值
- 调整参数传递逻辑，优先级：调用参数 > 配置文件 > 模型默认值
- 移除硬编码的默认值，改用条件判断

feat(process): 增强事件投影功能

- 添加工具调用开始/结束事件的映射逻辑
- 实现技能激活事件的识别和展示
- 添加辅助函数处理工具调用名称和参数提取
- 优化运行记录关联逻辑，提升事件匹配准确性

fix(web): 更新网络请求客户端信任环境设置

- 将WebFetchTool和WebSearchTool的trust_env参数设为True
- 确保HTTP客户端能够正确使用系统代理配置
- 修复可能的网络连接问题

test: 添加配置加载和事件投影相关测试

- 新增智能体默认参数配置测试用例
- 实现API配置持久化和重载测试
- 添加技能卡片和工具事件的投影测试
```

app-instance/backend/beaver/engine/loader.py

@@ -44,6 +44,7 @@ from beaver.tools.builtins import (
     SpawnTool,
     SessionSearchTool,
     SkillManageTool,
+    SkillViewTool,
     SkillsListTool,
     TerminalTool,
     TodoTool,
@@ -220,16 +221,17 @@ class EngineLoader:
                     ObjectBackedTool(WriteFileTool()),
                     ObjectBackedTool(PatchFileTool()),
                     ObjectBackedTool(WebFetchTool()),
-            ObjectBackedTool(WebSearchTool()),
-            ObjectBackedTool(TerminalTool()),
-            ObjectBackedTool(ProcessTool()),
-            ObjectBackedTool(ExecuteCodeTool()),
-            ObjectBackedTool(TodoTool()),
-            ObjectBackedTool(ClarifyTool()),
-            ObjectBackedTool(SendMessageTool()),
-            ObjectBackedTool(DelegateTool()),
-            ObjectBackedTool(SpawnTool()),
-            SkillsListTool(),
+                    ObjectBackedTool(WebSearchTool()),
+                    ObjectBackedTool(TerminalTool()),
+                    ObjectBackedTool(ProcessTool()),
+                    ObjectBackedTool(ExecuteCodeTool()),
+                    ObjectBackedTool(TodoTool()),
+                    ObjectBackedTool(ClarifyTool()),
+                    ObjectBackedTool(SendMessageTool()),
+                    ObjectBackedTool(DelegateTool()),
+                    ObjectBackedTool(SpawnTool()),
+                    SkillsListTool(),
+                    ObjectBackedTool(SkillViewTool(loader=skills_loader)),
                     SkillManageTool(),
                     CronTool(),
                 ]

app-instance/backend/beaver/engine/loop.py

@@ -48,7 +48,7 @@ class AgentProfile:
     name: str = "default"
     system_prompt: str = ""
     default_model: str = "gpt-4.1-mini"
-    max_tokens: int = 4096
+    max_tokens: int | None = None
     max_context_messages: int = 1000
     temperature: float = 0.2
     max_tool_iterations: int = 30
@@ -89,6 +89,7 @@ class AgentLoop:
         self.loaded: EngineLoadResult | None = None
         self.runtime_services: dict[str, Any] = {}
         self._run_queue: asyncio.Queue[_DirectRunRequest | None] | None = None
+        self._active_direct_task: asyncio.Task[Any] | None = None
         self._running = False
         self._stop_requested = False
 
@@ -130,6 +131,8 @@ class AgentLoop:
                 if item.future.cancelled():
                     continue
 
+                previous_direct_task = self._active_direct_task
+                self._active_direct_task = asyncio.current_task()
                 try:
                     result = await self._process_direct_impl(item.task, **item.kwargs)
                 except asyncio.CancelledError:
@@ -142,6 +145,8 @@ class AgentLoop:
                 else:
                     if not item.future.done():
                         item.future.set_result(result)
+                finally:
+                    self._active_direct_task = previous_direct_task
         finally:
             if self._run_queue is not None:
                 while True:
@@ -183,6 +188,9 @@ class AgentLoop:
         if self._stop_requested:
             raise RuntimeError("AgentLoop.submit_direct() is not accepting new tasks after stop()")
 
+        if asyncio.current_task() is self._active_direct_task:
+            return await self._process_direct_impl(task, **kwargs)
+
         future: asyncio.Future[AgentRunResult] = asyncio.get_running_loop().create_future()
         await self._run_queue.put(_DirectRunRequest(task=task, kwargs=dict(kwargs), future=future))
         return await future
@@ -363,7 +371,7 @@ class AgentLoop:
         resolved_request_timeout_seconds = configured_provider.get("request_timeout_seconds")
         resolved_embedding_model = embedding_model or config.default_embedding_model
         resolved_embedding_target = embedding_target or config.resolve_embedding_target()
-        resolved_max_tokens = max_tokens or self.profile.max_tokens
+        resolved_max_tokens = self.profile.max_tokens if max_tokens is None else max_tokens
         resolved_temperature = self.profile.temperature if temperature is None else temperature
         resolved_max_tool_iterations = (
             self.profile.max_tool_iterations if max_tool_iterations is None else max_tool_iterations
@@ -892,7 +900,7 @@ class AgentLoop:
         provider: Any,
         messages: list[dict[str, Any]],
         model: str,
-        max_tokens: int,
+        max_tokens: int | None,
         temperature: float,
         thinking_enabled: bool | None,
     ) -> str:

app-instance/backend/beaver/engine/providers/anthropic.py

@@ -43,7 +43,7 @@ class AnthropicProvider(LLMProvider):
         messages: list[dict[str, Any]],
         tools: list[dict[str, Any]] | None = None,
         model: str | None = None,
-        max_tokens: int = 4096,
+        max_tokens: int | None = None,
         temperature: float = 0.7,
         thinking_enabled: bool | None = None,
     ) -> LLMResponse:
@@ -57,9 +57,14 @@ class AnthropicProvider(LLMProvider):
             "model": model or self.default_model,
             "system": system_prompt or "",
             "messages": anthropic_messages,
-            "max_tokens": max(1, max_tokens),
             "temperature": temperature,
         }
+        resolved_max_tokens = (
+            _default_max_tokens_for_model(model or self.default_model)
+            if max_tokens is None
+            else max(1, max_tokens)
+        )
+        kwargs["max_tokens"] = resolved_max_tokens
         if tools:
             kwargs["tools"] = _convert_tools(tools)
 
@@ -100,6 +105,17 @@ class AnthropicProvider(LLMProvider):
         return self.default_model
 
 
+def _default_max_tokens_for_model(model: str) -> int:
+    """Return a conservative native output ceiling for Anthropic Messages."""
+
+    normalized = model.lower().replace("_", "-")
+    if "sonnet-4" in normalized or "opus-4" in normalized or "3-7" in normalized or "3.7" in normalized:
+        return 64_000
+    if "haiku" in normalized:
+        return 4_096
+    return 8_192
+
+
 def _convert_messages(messages: list[dict[str, Any]]) -> tuple[str, list[dict[str, Any]]]:
     system_prompt = ""
     converted: list[dict[str, Any]] = []

app-instance/backend/beaver/engine/providers/base.py

@@ -88,7 +88,7 @@ class LLMProvider(ABC):
         messages: list[dict[str, Any]],
         tools: list[dict[str, Any]] | None = None,
         model: str | None = None,
-        max_tokens: int = 4096,
+        max_tokens: int | None = None,
         temperature: float = 0.7,
         thinking_enabled: bool | None = None,
     ) -> LLMResponse:

app-instance/backend/beaver/engine/providers/chain.py

@@ -56,7 +56,7 @@ class FallbackProviderChain(LLMProvider):
         messages: list[dict],
         tools: list[dict] | None = None,
         model: str | None = None,
-        max_tokens: int = 4096,
+        max_tokens: int | None = None,
         temperature: float = 0.7,
         thinking_enabled: bool | None = None,
     ) -> LLMResponse:
@@ -115,7 +115,7 @@ class FallbackProviderChain(LLMProvider):
         messages: list[dict],
         tools: list[dict] | None,
         model: str,
-        max_tokens: int,
+        max_tokens: int | None,
         temperature: float,
         thinking_enabled: bool | None,
     ) -> LLMResponse:

app-instance/backend/beaver/engine/providers/codex.py

@@ -39,7 +39,7 @@ class OpenAICodexProvider(LLMProvider):
         messages: list[dict[str, Any]],
         tools: list[dict[str, Any]] | None = None,
         model: str | None = None,
-        max_tokens: int = 4096,
+        max_tokens: int | None = None,
         temperature: float = 0.7,
         thinking_enabled: bool | None = None,
     ) -> LLMResponse:

app-instance/backend/beaver/engine/providers/custom.py

@@ -47,7 +47,7 @@ class CustomProvider(LLMProvider):
         messages: list[dict[str, Any]],
         tools: list[dict[str, Any]] | None = None,
         model: str | None = None,
-        max_tokens: int = 4096,
+        max_tokens: int | None = None,
         temperature: float = 0.7,
         thinking_enabled: bool | None = None,
     ) -> LLMResponse:
@@ -55,9 +55,10 @@ class CustomProvider(LLMProvider):
         kwargs: dict[str, Any] = {
             "model": model or self.default_model,
             "messages": self.sanitize_empty_content(messages),
-            "max_tokens": max(1, max_tokens),
             "temperature": temperature,
         }
+        if max_tokens is not None:
+            kwargs["max_tokens"] = max(1, max_tokens)
         if tools:
             kwargs.update(tools=tools, tool_choice="auto")
         try:

app-instance/backend/beaver/engine/providers/litellm.py

@@ -197,7 +197,7 @@ class LiteLLMProvider(LLMProvider):
         messages: list[dict[str, Any]],
         tools: list[dict[str, Any]] | None = None,
         model: str | None = None,
-        max_tokens: int = 4096,
+        max_tokens: int | None = None,
         temperature: float = 0.7,
         thinking_enabled: bool | None = None,
     ) -> LLMResponse:
@@ -210,10 +210,11 @@ class LiteLLMProvider(LLMProvider):
         kwargs: dict[str, Any] = {
             "model": resolved_model,
             "messages": sanitized_messages,
-            "max_tokens": max(1, max_tokens),
             "temperature": temperature,
             "timeout": self.request_timeout_seconds or 45.0,
         }
+        if max_tokens is not None:
+            kwargs["max_tokens"] = max(1, max_tokens)
         if self.api_key:
             kwargs["api_key"] = self.api_key
         if self.api_base: