chore(v2): 从兴邦预审v2白名单抽取独立可运行基座
按 docs/v2/05-implementation-plan.md §1 候选迁移表与 claude_use/T00 白名单， 从 55e2cdb 抽取 224 个文件（2.48 MB）到独立目录 F:/mini-OpenClaw-v2。 - 排除：凭据(.env/server-deploy.conf/旧DELIVERY.md)、运行数据(xingbang_data 等 4 个 SQLite)、依赖(venv/node_modules 符号链接)、归档(_backup*/test-evidence/ ori_file)、Office 临时文件 - 目标目录独立 git 仓库，初始恢复点；Gitea 远程与分支待 OQ-06 Q2 定案 - 未部署、未启动服务、未调用模型 Co-Authored-By: Claude Code <noreply@anthropic.com>
This commit is contained in:
Claude Code
@@ -0,0 +1,23 @@
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"""Core Tools factory — returns all 5 tools for the Agent."""
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from pathlib import Path
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from typing import List
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from langchain_core.tools import BaseTool
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from .terminal_tool import create_terminal_tool
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from .python_repl_tool import create_python_repl_tool
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from .fetch_url_tool import create_fetch_url_tool
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from .read_file_tool import create_read_file_tool
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from .search_knowledge_tool import create_search_knowledge_tool
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def get_all_tools(base_dir: Path) -> List[BaseTool]:
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"""Create and return all 5 core tools, sandboxed to base_dir."""
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return [
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create_terminal_tool(base_dir),
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create_python_repl_tool(),
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create_fetch_url_tool(),
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create_read_file_tool(base_dir),
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create_search_knowledge_tool(base_dir),
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]
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@@ -0,0 +1,59 @@
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"""FetchURLTool — Fetch a URL and return cleaned Markdown content."""
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from typing import Type
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import html2text
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import requests
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from langchain_core.tools import BaseTool
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from pydantic import BaseModel, Field
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class FetchURLInput(BaseModel):
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url: str = Field(description="The URL to fetch content from")
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class FetchURLTool(BaseTool):
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name: str = "fetch_url"
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description: str = (
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"Fetch the content of a web page and return it as cleaned Markdown text. "
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"Use this to retrieve information from the internet. "
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"Input should be a valid URL (starting with http:// or https://)."
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)
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args_schema: Type[BaseModel] = FetchURLInput
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def _run(self, url: str) -> str:
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try:
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headers = {
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"User-Agent": "Mozilla/5.0 (compatible; MiniOpenClaw/0.1)"
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}
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resp = requests.get(url, headers=headers, timeout=15)
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resp.raise_for_status()
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content_type = resp.headers.get("content-type", "")
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# If JSON, return directly
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if "application/json" in content_type:
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text = resp.text
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if len(text) > 5000:
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text = text[:5000] + "\n...[truncated]"
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return text
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# Convert HTML to Markdown
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converter = html2text.HTML2Text()
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converter.ignore_links = False
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converter.ignore_images = True
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converter.body_width = 0
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markdown = converter.handle(resp.text)
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if len(markdown) > 5000:
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markdown = markdown[:5000] + "\n...[truncated]"
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return markdown
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except requests.Timeout:
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return "❌ Request timed out (15s limit)"
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except requests.RequestException as e:
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return f"❌ Fetch error: {str(e)}"
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def create_fetch_url_tool() -> FetchURLTool:
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return FetchURLTool()
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@@ -0,0 +1,17 @@
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"""Python REPL Tool — wraps LangChain experimental PythonREPLTool."""
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from langchain_core.tools import BaseTool
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from langchain_experimental.tools import PythonREPLTool
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def create_python_repl_tool() -> BaseTool:
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tool = PythonREPLTool()
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tool.name = "python_repl"
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tool.description = (
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"Execute Python code in an interactive REPL environment. "
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"Use this for calculations, data processing, running scripts, "
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"and any task that benefits from programmatic execution. "
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"Input should be valid Python code. "
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"Use print() to see output."
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)
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return tool
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@@ -0,0 +1,53 @@
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"""ReadFileTool — sandboxed file reading within project directory."""
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from pathlib import Path
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from typing import Type
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from langchain_core.tools import BaseTool
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from pydantic import BaseModel, Field
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class ReadFileInput(BaseModel):
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file_path: str = Field(
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description="Relative path of the file to read (relative to project root)"
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)
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class SandboxedReadFileTool(BaseTool):
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name: str = "read_file"
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description: str = (
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"Read the content of a local file. Path is relative to the project root. "
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"Use this to read SKILL.md files, MEMORY.md, configuration files, etc. "
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"Example: read_file('skills/get_weather/SKILL.md')"
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)
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args_schema: Type[BaseModel] = ReadFileInput
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root_dir: str = ""
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def _run(self, file_path: str) -> str:
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try:
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root = Path(self.root_dir)
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# Normalize path
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normalized = file_path.replace("\\", "/").lstrip("./")
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full_path = (root / normalized).resolve()
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# Sandbox check
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if not str(full_path).startswith(str(root.resolve())):
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return f"❌ Access denied: path escapes project root"
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if not full_path.exists():
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return f"❌ File not found: {file_path}"
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if not full_path.is_file():
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return f"❌ Not a file: {file_path}"
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content = full_path.read_text(encoding="utf-8")
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if len(content) > 10000:
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content = content[:10000] + "\n...[truncated]"
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return content
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except Exception as e:
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return f"❌ Error reading file: {str(e)}"
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def create_read_file_tool(base_dir: Path) -> SandboxedReadFileTool:
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return SandboxedReadFileTool(root_dir=str(base_dir))
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@@ -0,0 +1,35 @@
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"""Tools scoped to an immutable authorized risk run, not the production database."""
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import json
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from copy import deepcopy
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from langchain_core.tools import tool
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def create_risk_tools(context, evidence, native_skills):
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snapshot = deepcopy(context)
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sources = deepcopy(evidence)
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skills = deepcopy(native_skills)
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@tool
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def list_data_cards() -> str:
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"""List authorized peer data-card snapshots available for this risk task."""
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return json.dumps([{"index":i,"name":d["card"],"version":d["version"],"records":len(d["records"])} for i,d in enumerate(snapshot.get("peer_datasets",[]))],ensure_ascii=False)
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@tool
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def read_data_card(index: int) -> str:
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"""Read one authorized data-card snapshot, including periods and raw evidence."""
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cards = snapshot.get("peer_datasets", [])
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if index < 0 or index >= len(cards):
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return json.dumps({"error":"数据卡不在本次授权范围"},ensure_ascii=False)
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return json.dumps(cards[index],ensure_ascii=False)
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@tool
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def read_checks_and_rules() -> str:
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"""Read deterministic Skill outputs, human rules and rule findings for this run."""
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return json.dumps({"values":snapshot.get("scalar_values",{}),"human_rules":snapshot.get("human_rules",[]),"findings":snapshot.get("rule_findings",[]),"evidence":sources},ensure_ascii=False)
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@tool
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def read_selected_skill(skill_id: str) -> str:
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"""Read only a native SKILL.md explicitly selected in the published plan."""
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return skills.get(skill_id,"此 Skill 未被所选方案授权")
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return [list_data_cards, read_data_card, read_checks_and_rules, read_selected_skill]
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@@ -0,0 +1,102 @@
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"""SearchKnowledgeBaseTool — LlamaIndex hybrid search (BM25 + Vector)."""
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import os
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from pathlib import Path
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from typing import Type, Optional
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from langchain_core.tools import BaseTool
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from pydantic import BaseModel, Field
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class SearchKnowledgeInput(BaseModel):
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query: str = Field(description="The search query to find relevant knowledge")
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class SearchKnowledgeBaseTool(BaseTool):
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name: str = "search_knowledge_base"
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description: str = (
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"Search the local knowledge base using hybrid retrieval (keyword + semantic). "
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"Use this when the user asks about specific knowledge or documents. "
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"Returns the most relevant passages from the knowledge base."
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)
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args_schema: Type[BaseModel] = SearchKnowledgeInput
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base_dir: str = ""
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_index: Optional[object] = None
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class Config:
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arbitrary_types_allowed = True
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def _build_index(self):
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"""Build or load LlamaIndex index from knowledge/ directory."""
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knowledge_dir = Path(self.base_dir) / "knowledge"
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storage_dir = Path(self.base_dir) / "storage"
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if not knowledge_dir.exists() or not any(knowledge_dir.iterdir()):
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return None
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try:
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from llama_index.core import (
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SimpleDirectoryReader,
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StorageContext,
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VectorStoreIndex,
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load_index_from_storage,
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)
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from llama_index.core.settings import Settings
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from llama_index.embeddings.openai import OpenAIEmbedding
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Settings.embed_model = OpenAIEmbedding(
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model=os.getenv("EMBEDDING_MODEL", "text-embedding-3-small"),
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api_key=os.getenv("OPENAI_API_KEY"),
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api_base=os.getenv("OPENAI_BASE_URL", "https://ai.devtool.tech/proxy/v1"),
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)
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# Try loading persisted index
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if storage_dir.exists() and any(storage_dir.iterdir()):
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try:
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storage_context = StorageContext.from_defaults(
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persist_dir=str(storage_dir)
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)
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return load_index_from_storage(storage_context)
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except Exception:
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pass
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# Build fresh index
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documents = SimpleDirectoryReader(
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str(knowledge_dir), recursive=True
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).load_data()
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if not documents:
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return None
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index = VectorStoreIndex.from_documents(documents)
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storage_dir.mkdir(parents=True, exist_ok=True)
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index.storage_context.persist(persist_dir=str(storage_dir))
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return index
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except ImportError as e:
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print(f"⚠️ LlamaIndex not fully installed: {e}")
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return None
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except Exception as e:
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print(f"⚠️ Index build error: {e}")
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return None
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def _run(self, query: str) -> str:
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if self._index is None:
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self._index = self._build_index()
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if self._index is None:
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return "📭 Knowledge base is empty. Add documents to backend/knowledge/ to enable search."
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try:
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query_engine = self._index.as_query_engine(similarity_top_k=3)
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response = query_engine.query(query)
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result = str(response)
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if len(result) > 5000:
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result = result[:5000] + "\n...[truncated]"
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return result
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except Exception as e:
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return f"❌ Search error: {str(e)}"
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def create_search_knowledge_tool(base_dir: Path) -> SearchKnowledgeBaseTool:
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return SearchKnowledgeBaseTool(base_dir=str(base_dir))
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@@ -0,0 +1,118 @@
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"""Skills Scanner — Scan skills/ directory and generate SKILLS_SNAPSHOT.md."""
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from hashlib import sha256
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from pathlib import Path
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import re
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import yaml
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MAX_SKILL_BYTES = 64 * 1024
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def parse_skill(content: str) -> dict:
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"""Validate the small, declarative SKILL.md format used by the workbench."""
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if not isinstance(content, str) or not content.startswith("---") or len(content.encode("utf-8")) > MAX_SKILL_BYTES:
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raise ValueError("SKILL.md 缺少 YAML 头或超过 64 KB")
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parts = content.split("---", 2)
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meta = yaml.safe_load(parts[1]) if len(parts) == 3 else None
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if not isinstance(meta, dict) or not isinstance(meta.get("name"), str) or not meta["name"].strip():
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raise ValueError("SKILL.md 必须声明 name")
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if not isinstance(meta.get("description"), str) or not meta["description"].strip():
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raise ValueError("SKILL.md 必须声明 description")
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details = meta.get("metadata", {})
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if not isinstance(details, dict):
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raise ValueError("SKILL.md 的 metadata 必须是对象")
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for field in ("inputs", "audience"):
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value = meta.get(field, details.get(field))
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if value is not None and (not isinstance(value, list) or not all(isinstance(item, str) for item in value)):
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raise ValueError(f"SKILL.md 的 {field} 必须是文本列表")
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return meta
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def skill_attributes(meta: dict) -> dict:
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return {**meta, **meta.get("metadata", {})}
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def skill_slug(meta: dict) -> str:
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slug = re.sub(r"[^a-z0-9-]+", "-", meta["name"].strip().lower().replace("_", "-")).strip("-")
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if not slug or len(slug) > 64:
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raise ValueError("Skill 名称需使用 1–64 位英文小写字母、数字或连字符")
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return slug
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def install_skill(base_dir: Path, content: str) -> dict:
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"""Install one uploaded SKILL.md under the managed imported-skills directory."""
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meta = parse_skill(content)
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slug = skill_slug(meta)
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root = (base_dir / "skills").resolve()
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target = (root / "imported" / slug / "SKILL.md").resolve()
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if not target.is_relative_to(root):
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raise ValueError("Skill 目录不在允许范围内")
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if target.exists():
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raise FileExistsError("同名 Skill 已存在，请先归档旧版本或修改 name")
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target.parent.mkdir(parents=True, exist_ok=False)
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temporary = target.with_suffix(".tmp")
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temporary.write_text(content, encoding="utf-8")
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temporary.replace(target)
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legacy_id = target.relative_to(root).as_posix()
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return {"id": legacy_id, "legacy_id": legacy_id, "name": meta["name"], "description": meta["description"], "meta": skill_attributes(meta)}
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def discover_skills(base_dir: Path) -> list[dict]:
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"""Read the native Skills catalog without changing its generated snapshot."""
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root = (base_dir / "skills").resolve()
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entries = []
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for file in sorted(root.rglob("SKILL.md")):
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resolved = file.resolve()
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if not resolved.is_relative_to(root):
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continue
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try:
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content = file.read_text(encoding="utf-8")
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meta = parse_skill(content)
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attributes = skill_attributes(meta)
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if str(attributes.get("status", "published")) == "archived":
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continue
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legacy_id = file.relative_to(root).as_posix()
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entries.append({
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"id": legacy_id,
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"component_id": str(attributes.get("id", legacy_id)),
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"legacy_id": legacy_id,
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"kind": str(attributes.get("kind", "skill")),
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"version": str(attributes.get("version", "legacy")),
|
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"schema_version": int(attributes.get("schema_version", 1)),
|
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"status": str(attributes.get("status", "published")),
|
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"name": str(meta.get("name", file.parent.name)),
|
||||
"description": str(meta.get("description", "")),
|
||||
"content_hash": sha256(content.encode("utf-8")).hexdigest(),
|
||||
"content": content,
|
||||
})
|
||||
except (OSError, ValueError, TypeError, yaml.YAMLError):
|
||||
continue
|
||||
return entries
|
||||
|
||||
|
||||
def scan_skills(base_dir: Path) -> str:
|
||||
"""Scan all SKILL.md files and generate SKILLS_SNAPSHOT.md."""
|
||||
skills_dir = base_dir / "skills"
|
||||
snapshot_path = base_dir / "SKILLS_SNAPSHOT.md"
|
||||
|
||||
if not skills_dir.exists():
|
||||
skills_dir.mkdir(parents=True)
|
||||
|
||||
skills = [{**s, "location":f"./backend/skills/{s['legacy_id']}"} for s in discover_skills(base_dir)]
|
||||
|
||||
# Build XML-style snapshot
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||||
lines = ["<available_skills>"]
|
||||
for s in skills:
|
||||
lines.append(" <skill>")
|
||||
lines.append(f" <name>{s['name']}</name>")
|
||||
lines.append(f" <description>{s['description']}</description>")
|
||||
lines.append(f" <location>{s['location']}</location>")
|
||||
lines.append(" </skill>")
|
||||
lines.append("</available_skills>")
|
||||
|
||||
snapshot = "\n".join(lines)
|
||||
snapshot_path.write_text(snapshot, encoding="utf-8")
|
||||
print(f"📋 Skills snapshot: {len(skills)} skills found")
|
||||
return snapshot
|
||||
@@ -0,0 +1,78 @@
|
||||
"""SafeTerminalTool — sandboxed shell execution with command blacklist."""
|
||||
|
||||
import subprocess
|
||||
from pathlib import Path
|
||||
from typing import Type
|
||||
|
||||
from langchain_core.tools import BaseTool
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
BLACKLISTED_COMMANDS = [
|
||||
"rm -rf /",
|
||||
"rm -rf /*",
|
||||
"mkfs",
|
||||
"dd if=",
|
||||
":(){:|:&};:",
|
||||
"chmod -R 777 /",
|
||||
"shutdown",
|
||||
"reboot",
|
||||
"halt",
|
||||
"poweroff",
|
||||
"format c:",
|
||||
"del /f /s /q c:",
|
||||
]
|
||||
|
||||
|
||||
class TerminalInput(BaseModel):
|
||||
command: str = Field(description="The shell command to execute")
|
||||
|
||||
|
||||
class SafeTerminalTool(BaseTool):
|
||||
name: str = "terminal"
|
||||
description: str = (
|
||||
"Execute shell commands in a sandboxed environment. "
|
||||
"The working directory is restricted to the project root. "
|
||||
"Use this for file operations, installing packages, running scripts, etc."
|
||||
)
|
||||
args_schema: Type[BaseModel] = TerminalInput
|
||||
root_dir: str = ""
|
||||
|
||||
def _is_safe(self, command: str) -> bool:
|
||||
cmd_lower = command.lower().strip()
|
||||
for blocked in BLACKLISTED_COMMANDS:
|
||||
if blocked in cmd_lower:
|
||||
return False
|
||||
return True
|
||||
|
||||
def _run(self, command: str) -> str:
|
||||
if not self._is_safe(command):
|
||||
return f"❌ Command blocked for safety: {command}"
|
||||
try:
|
||||
result = subprocess.run(
|
||||
command,
|
||||
shell=True,
|
||||
cwd=self.root_dir,
|
||||
capture_output=True,
|
||||
text=True,
|
||||
timeout=30,
|
||||
encoding="utf-8",
|
||||
errors="replace",
|
||||
)
|
||||
output = result.stdout
|
||||
if result.stderr:
|
||||
output += f"\n[stderr]: {result.stderr}"
|
||||
if not output.strip():
|
||||
output = "(command completed with no output)"
|
||||
# Truncate very long output
|
||||
if len(output) > 5000:
|
||||
output = output[:5000] + "\n...[truncated]"
|
||||
return output
|
||||
except subprocess.TimeoutExpired:
|
||||
return "❌ Command timed out (30s limit)"
|
||||
except Exception as e:
|
||||
return f"❌ Error: {str(e)}"
|
||||
|
||||
|
||||
def create_terminal_tool(base_dir: Path) -> SafeTerminalTool:
|
||||
return SafeTerminalTool(root_dir=str(base_dir))
|
||||
Reference in New Issue
Block a user