chore: initial commit for Pretor v0.1.0-alpha

正式发布 Pretor 平台的首个 alpha 版本。本项目旨在构建一个基于分布式架构的多智能体协同工作流水线。

核心功能实现：
1. 建立基于 BaseIndividual 的动态插件加载机制。
2. 实现三类核心 worker_individual 子个体。
3. 集成 Ray 框架支持分布式集群调度。
4. 基于 PostgreSQL 的全量持久化存储方案。
5. 提供完整的 FastAPI 后端与 React 前端交互界面。

pretor/worker_individual/skill_individual.py

@@ -0,0 +1,110 @@
+#  Copyright 2026 zhaoxi826
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#       http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+
+from pretor.worker_individual.base_individual import BaseIndividual, WorkerIndividualDeps
+from pretor.utils.logger import get_logger
+import os
+import json
+from pydantic_ai import Tool
+import importlib.util
+
+logger = get_logger('skill_individual')
+
+class SkillIndividual(BaseIndividual):
+    """
+    专家子个体：拥有专业 skill 的 agent。
+    """
+
+    def __init__(self, agent_config: dict):
+        super().__init__(agent_config)
+
+    async def _load_skill_tools(self):
+        """动态加载已绑定的 skill 工具。"""
+        tools = []
+        bound_skill = self.agent_config.get("bound_skill", "")
+        # bound_skill can be string or dict {"skill_name": ["file1", "file2"]}
+        skill_mapper = {}
+        if isinstance(bound_skill, str) and bound_skill:
+            try:
+                skill_mapper = json.loads(bound_skill)
+            except json.JSONDecodeError:
+                pass
+        elif isinstance(bound_skill, dict):
+            skill_mapper = bound_skill
+
+        skill_base_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "plugin", "skill"))
+
+        for skill_name, _ in skill_mapper.items():
+            skill_path = os.path.join(skill_base_dir, skill_name)
+            metadata_path = os.path.join(skill_path, "metadata.json")
+            if not os.path.exists(metadata_path):
+                continue
+
+            try:
+                with open(metadata_path, 'r', encoding='utf-8') as f:
+                    metadata = json.load(f)
+            except Exception as e:
+                logger.error(f"Failed to load metadata for skill {skill_name}: {e}")
+                continue
+
+            if "functions" in metadata:
+                for func_info in metadata["functions"]:
+                    # Ensure path is absolute
+                    script_path = func_info.get("file_path", "")
+                    if not os.path.isabs(script_path):
+                        script_path = os.path.join(skill_path, script_path)
+
+                    if not os.path.exists(script_path):
+                        logger.warning(f"Skill script not found: {script_path}")
+                        continue
+
+                    func_name = func_info.get("name")
+                    try:
+                        # Dynamically load the python module
+                        spec = importlib.util.spec_from_file_location(func_name, script_path)
+                        module = importlib.util.module_from_spec(spec)
+                        spec.loader.exec_module(module)
+
+                        func = getattr(module, func_name)
+                        if callable(func):
+                            # Convert to PydanticAI Tool
+                            tool = Tool(func, name=func_name, description=func_info.get("docstring", ""))
+                            tools.append(tool)
+                            logger.info(f"Loaded skill tool: {func_name} from {skill_name}")
+                    except Exception as e:
+                        logger.error(f"Failed to load function {func_name} from {script_path}: {e}")
+
+        return tools
+
+    async def run(self, task_event: dict) -> dict:
+        if self.agent is None:
+            system_prompt = self.agent_config.get("prompt",
+                                                  "你是一个拥有专业技能的专家级AI助手，请利用你的专业知识完成给定的任务。")
+            await self._init_agent("skill_individual", system_prompt)
+
+        deps = WorkerIndividualDeps(task_event=task_event)
+        self.agent.retries = 3
+
+        tools = await self._load_skill_tools()
+
+        try:
+            result = await self.agent.run(
+                f"请执行以下任务：\n{task_event}",
+                deps=deps,
+                tools=tools if tools else None
+            )
+            return {"output": result.data.output}
+        except Exception as e:
+            logger.exception(f"SkillIndividual {self.agent_id} 执行失败: {e}")
+            raise