feat(app-instance): 集成Beaver后端并更新配置管理

集成新的Beaver后端服务到应用实例中，替换原有的nanobot实现。

主要变更包括：
- 在Dockerfile和环境配置中添加Beaver相关路径和配置变量
- 更新工作目录结构从.nanobot到.beaver
- 实现Beaver引擎加载器，支持配置文件加载和工具组装
- 添加内置工具如ListDirectoryTool、ReadFileTool、SearchFilesTool
- 更新消息处理流程，支持通道适配器和网关模式
- 重构技能系统，支持显式工具提示和嵌入式检索
- 改进错误处理和生命周期管理

此变更使应用实例能够使用统一的Beaver后端进行AI代理运行时管理。

app-instance/backend/beaver/tools/assembler/__init__.py

@@ -0,0 +1,5 @@
+"""Tool selection for a single Beaver run."""
+
+from .task_assembler import ToolAssembler
+
+__all__ = ["ToolAssembler"]

app-instance/backend/beaver/tools/assembler/task_assembler.py

@@ -0,0 +1,106 @@
+"""Task-driven tool assembler.
+
+这层和 SkillAssembler 的位置类似：它不执行工具，只决定本轮 run 应该把哪些
+tool schema 暴露给模型。
+"""
+
+from __future__ import annotations
+
+from collections.abc import Sequence
+from typing import TYPE_CHECKING
+
+from beaver.engine.context import SkillContext
+from beaver.foundation.embedding import EmbeddingRetriever
+from beaver.tools.base import ToolSpec
+from beaver.tools.registry import ToolRegistry
+
+if TYPE_CHECKING:
+    from beaver.engine.providers.runtime import ProviderRuntime
+    from beaver.skills.catalog.loader import SkillsLoader
+
+
+class ToolAssembler:
+    """Use skill hints and embedding retrieval to select run-scoped tools."""
+
+    def __init__(
+        self,
+        *,
+        retriever: EmbeddingRetriever | None = None,
+        always_tool_names: Sequence[str] | None = None,
+    ) -> None:
+        self.retriever = retriever or EmbeddingRetriever()
+        self.always_tool_names = tuple(always_tool_names or ("memory", "session_search", "skill_view"))
+
+    async def assemble(
+        self,
+        *,
+        task_description: str,
+        registry: ToolRegistry,
+        skills_loader: SkillsLoader | None = None,
+        activated_skills: Sequence[SkillContext] | None = None,
+        embedding_runtime: ProviderRuntime | None = None,
+        top_k: int = 10,
+    ) -> list[ToolSpec]:
+        """Return selected tool specs for the current run.
+
+        Selection order is intentionally deterministic:
+        1. always tools from config/spec
+        2. tools explicitly declared by activated skills
+        3. embedding top-k tools for the task
+        """
+
+        selected: list[ToolSpec] = []
+        selected_names: set[str] = set()
+
+        def add_specs(specs: Sequence[ToolSpec]) -> None:
+            for spec in specs:
+                if spec.name in selected_names:
+                    continue
+                selected.append(spec)
+                selected_names.add(spec.name)
+
+        add_specs(registry.list_always_specs())
+        add_specs(registry.get_specs(self.always_tool_names))
+
+        skill_tool_names = self._collect_skill_tool_names(
+            skills_loader=skills_loader,
+            activated_skills=activated_skills or (),
+        )
+        add_specs(registry.get_specs(skill_tool_names))
+
+        candidates = [
+            spec.to_embedding_candidate()
+            for spec in registry.list_specs()
+            if spec.name not in selected_names
+        ]
+        retrieved = await self.retriever.retrieve(
+            query=task_description,
+            candidates=candidates,
+            top_k=top_k,
+            api_key=embedding_runtime.api_key if embedding_runtime is not None else None,
+            api_base=embedding_runtime.api_base if embedding_runtime is not None else None,
+            model=embedding_runtime.model if embedding_runtime is not None else None,
+            extra_headers=embedding_runtime.extra_headers if embedding_runtime is not None else None,
+            timeout_seconds=(
+                embedding_runtime.request_timeout_seconds if embedding_runtime is not None else None
+            ),
+            fallback_top_k=top_k,
+        )
+        add_specs(registry.get_specs([item["name"] for item in retrieved]))
+        return selected
+
+    @staticmethod
+    def _collect_skill_tool_names(
+        *,
+        skills_loader: SkillsLoader | None,
+        activated_skills: Sequence[SkillContext],
+    ) -> list[str]:
+        if skills_loader is None or not activated_skills:
+            return []
+
+        result: list[str] = []
+        for skill in activated_skills:
+            for name in skills_loader.get_skill_tool_hints(skill.name):
+                if name not in result:
+                    result.append(name)
+        return result