重构仓库目录为统一的 runtime 分层并清理历史 openclaw 残留。

本次迁移将网关/通道/工具/技能/脚本与协议资源集中到新结构，统一路径常量与脚本转发机制，减少顶层噪音并保证运行与测试行为一致。

Made-with: Cursor

runtime/tools/base.py

@@ -0,0 +1,80 @@
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Any, Callable
+
+
+ToolHandler = Callable[[dict[str, Any]], Any]
+
+
+@dataclass(frozen=True)
+class ToolRateLimit:
+    """Best-effort per-tool rate limiting metadata (enforced by runtime when implemented)."""
+
+    # tokens per window (simple leaky bucket style); None means unlimited.
+    limit: int | None = None
+    window_s: int = 60
+
+
+@dataclass(frozen=True)
+class ToolSpec:
+    name: str
+    description: str
+    parameters: dict[str, Any]
+    handler: ToolHandler
+    tags: frozenset[str] = field(default_factory=frozenset)
+    # Contract metadata (non-OpenAI; used by orchestrator/runtime)
+    version: str = "v1"
+    risk_level: str = "low"  # low|high (extendable)
+    timeout_s: float | None = None
+    rate_limit: ToolRateLimit | None = None
+    required_permissions: frozenset[str] = field(default_factory=frozenset)
+    execution_mode: str = "in_process"  # in_process|subprocess (best-effort)
+    #: If true, may run in parallel with other consecutive read-only tools (cc-mini-style batching).
+    read_only: bool = False
+
+    def is_read_only(self) -> bool:
+        """Compatibility helper mirroring cc-mini Tool.is_read_only()."""
+        return bool(self.read_only)
+
+    def as_openai_tool(self) -> dict[str, Any]:
+        return {
+            "type": "function",
+            "function": {
+                "name": self.name,
+                "description": self.description,
+                "parameters": self.parameters,
+            },
+        }
+
+
+class ToolRegistry:
+    def __init__(self, tools: list[ToolSpec] | None = None):
+        self._tools: dict[str, ToolSpec] = {}
+        self._openai_tools_cache: list[dict[str, Any]] | None = None
+        if tools:
+            for t in tools:
+                self.register(t)
+
+    def register(self, tool: ToolSpec) -> None:
+        self._tools[tool.name] = tool
+        self._openai_tools_cache = None
+
+    def get(self, name: str) -> ToolSpec | None:
+        return self._tools.get(name)
+
+    def list(self) -> list[ToolSpec]:
+        return list(self._tools.values())
+
+    def as_openai_tools(self) -> list[dict[str, Any]]:
+        if self._openai_tools_cache is None:
+            self._openai_tools_cache = [t.as_openai_tool() for t in self.list()]
+        return list(self._openai_tools_cache)
+
+
+__all__ = [
+    "ToolHandler",
+    "ToolRateLimit",
+    "ToolSpec",
+    "ToolRegistry",
+]