diff --git a/mcp/mcp_settings.json b/mcp/mcp_settings.json
index ddf9962..3aa61ae 100644
--- a/mcp/mcp_settings.json
+++ b/mcp/mcp_settings.json
@@ -1,14 +1,5 @@
[
{
- "mcpServers": {
- "rag_retrieve": {
- "transport": "stdio",
- "command": "python",
- "args": [
- "./mcp/rag_retrieve_server.py",
- "{bot_id}"
- ]
- }
- }
+ "mcpServers": {}
}
]
diff --git a/prompt/system_prompt.md b/prompt/system_prompt.md
index 6607564..7888edf 100644
--- a/prompt/system_prompt.md
+++ b/prompt/system_prompt.md
@@ -69,60 +69,6 @@ When creating scripts in `executable_code/`, follow these organization rules:
- Temporary script (when needed): `{agent_dir_path}/executable_code/tmp/test.py`
- Downloaded file: `{agent_dir_path}/download/report.pdf`
-# Retrieval Policy (Priority & Fallback)
-
-### 1. Retrieval Source Priority and Tool Selection
-- Follow this section for source choice, tool choice, query rewrite, `top_k`, fallback, result handling, and citations.
-- Use this default retrieval order and execute it sequentially: skill-enabled knowledge retrieval tools > `rag_retrieve` / `table_rag_retrieve` > local filesystem retrieval.
-- Do NOT answer from model knowledge first.
-- Do NOT skip directly to local filesystem retrieval when an earlier retrieval source may answer the question.
-- When a suitable skill-enabled knowledge retrieval tool is available, use it first.
-- If no suitable skill-enabled retrieval tool is available, or if its result is insufficient, continue with `rag_retrieve` or `table_rag_retrieve`.
-- Use `table_rag_retrieve` first for values, prices, quantities, inventory, specifications, rankings, comparisons, summaries, extraction, lists, tables, name lookup, historical coverage, mixed questions, and unclear cases.
-- Use `rag_retrieve` first only for clearly pure concept, definition, workflow, policy, or explanation questions without structured data needs.
-- After each retrieval step, evaluate sufficiency before moving to the next source. Do NOT run these retrieval sources in parallel.
-
-### 2. Query Preparation
-- Do NOT pass the raw user question unless it already works well for retrieval.
-- Rewrite for recall: extract entity, time scope, attributes, and intent.
-- Add useful variants: synonyms, aliases, abbreviations, related titles, historical names, and category terms.
-- Expand list-style, extraction, overview, historical, roster, timeline, and archive queries more aggressively.
-- Preserve meaning. Do NOT introduce unrelated topics.
-
-### 3. Retrieval Breadth (`top_k`)
-- Apply `top_k` only to `rag_retrieve`. Use the smallest sufficient value, then expand only if coverage is insufficient.
-- Use `30` for simple fact lookup.
-- Use `50` for moderate synthesis, comparison, summarization, or disambiguation.
-- Use `100` for broad recall, such as comprehensive analysis, scattered knowledge, multiple entities or periods, or list / catalog / timeline / roster / overview requests.
-- Raise `top_k` when keyword branches are many or results are too few, repetitive, incomplete, sparse, or too narrow.
-- Use this expansion order: `30 -> 50 -> 100`. If unsure, use `100`.
-
-### 4. Result Evaluation
-- Treat results as insufficient if they are empty, start with `Error:`, say `no excel files found`, are off-topic, miss the core entity or scope, or provide no usable evidence.
-- Also treat results as insufficient when they cover only part of the request, or when full-list, historical, comparison, or mixed data + explanation requests return only partial or truncated coverage.
-
-### 5. Fallback and Sequential Retry
-- If the first retrieval result is insufficient, call the next retrieval source in the default order before replying.
-- If the first RAG tool is insufficient, call the other RAG tool next before moving to local filesystem retrieval.
-- If `table_rag_retrieve` is insufficient or empty, continue with `rag_retrieve`.
-- If `rag_retrieve` is insufficient or empty, continue with `table_rag_retrieve`.
-- If both `rag_retrieve` and `table_rag_retrieve` are insufficient, continue with local filesystem retrieval.
-- Say no relevant information was found only after all applicable skill-enabled retrieval tools, both `rag_retrieve` and `table_rag_retrieve`, and local filesystem retrieval have been tried and still do not provide enough evidence.
-- Do NOT reply that no relevant information was found before the final local filesystem fallback has also been tried.
-
-### 6. Table RAG Result Handling
-- Follow all `[INSTRUCTION]` and `[EXTRA_INSTRUCTION]` content in `table_rag_retrieve` results.
-- If results are truncated, explicitly tell the user total matches (`N+M`), displayed count (`N`), and omitted count (`M`).
-- Cite data sources using filenames from `file_ref_table`.
-
-### 7. Citation Requirements for Retrieved Knowledge
-- When using knowledge from `rag_retrieve` or `table_rag_retrieve`, you MUST generate `` tags.
-- Follow the citation format returned by each tool.
-- Place citations immediately after the paragraph or bullet list that uses the knowledge.
-- Do NOT collect citations at the end.
-- Use 1-2 citations per paragraph or bullet list when possible.
-- If learned knowledge is used, include at least 1 ``.
-
# System Information
Working directory: {agent_dir_path}
diff --git a/skills_autoload/rag-retrieve/.claude-plugin/plugin.json b/skills_autoload/rag-retrieve/.claude-plugin/plugin.json
new file mode 100644
index 0000000..b57fdf3
--- /dev/null
+++ b/skills_autoload/rag-retrieve/.claude-plugin/plugin.json
@@ -0,0 +1,22 @@
+{
+ "name": "rag-retrieve",
+ "description": "rag-retrieve and table-rag-retrieve",
+ "hooks": {
+ "PrePrompt": [
+ {
+ "type": "command",
+ "command": "python hooks/pre_prompt.py"
+ }
+ ]
+ },
+ "mcpServers": {
+ "rag_retrieve": {
+ "transport": "stdio",
+ "command": "python",
+ "args": [
+ "./skills_autoload/rag-retrieve/rag_retrieve_server.py",
+ "{bot_id}"
+ ]
+ }
+ }
+}
diff --git a/skills_autoload/rag-retrieve/README.md b/skills_autoload/rag-retrieve/README.md
new file mode 100644
index 0000000..9177f2a
--- /dev/null
+++ b/skills_autoload/rag-retrieve/README.md
@@ -0,0 +1,153 @@
+# User Context Loader
+
+用户上下文加载器示例 Skill，演示 Claude Plugins 模式的 hooks 机制。
+
+## 功能说明
+
+本 Skill 演示了三种 Hook 类型：
+
+### PrePrompt Hook
+在 system_prompt 加载时执行，动态注入用户上下文信息。
+- 文件: `hooks/pre_prompt.py`
+- 用途: 查询用户信息、偏好设置、历史记录等，注入到 prompt 中
+
+### PostAgent Hook
+在 agent 执行完成后执行，用于后处理。
+- 文件: `hooks/post_agent.py`
+- 用途: 记录分析数据、触发异步任务、发送通知等
+
+### PreSave Hook
+在消息保存前执行，用于内容处理。
+- 文件: `hooks/pre_save.py`
+- 用途: 内容过滤、敏感信息脱敏、格式转换等
+
+## 目录结构
+
+```
+user-context-loader/
+├── README.md # Skill 说明文档
+├── .claude-plugin/
+│ └── plugin.json # Hook 和 MCP 配置文件
+└── hooks/
+ ├── pre_prompt.py # PrePrompt hook 脚本
+ ├── post_agent.py # PostAgent hook 脚本
+ └── pre_save.py # PreSave hook 脚本
+```
+
+## plugin.json 格式
+
+```json
+{
+ "name": "user-context-loader",
+ "description": "用户上下文加载器示例 Skill",
+ "hooks": {
+ "PrePrompt": [
+ {
+ "type": "command",
+ "command": "python hooks/pre_prompt.py"
+ }
+ ],
+ "PostAgent": [
+ {
+ "type": "command",
+ "command": "python hooks/post_agent.py"
+ }
+ ],
+ "PreSave": [
+ {
+ "type": "command",
+ "command": "python hooks/pre_save.py"
+ }
+ ]
+ },
+ "mcpServers": {
+ "server-name": {
+ "command": "node",
+ "args": ["path/to/server.js"],
+ "env": {
+ "API_KEY": "${API_KEY}"
+ }
+ }
+ }
+}
+```
+
+## Hook 脚本格式
+
+Hook 脚本通过子进程执行，通过环境变量接收参数，通过 stdout 返回结果。
+
+### 可用环境变量
+
+| 环境变量 | 说明 | 适用于 |
+|---------|------|--------|
+| `ASSISTANT_ID` | Bot ID | 所有 hook |
+| `USER_IDENTIFIER` | 用户标识 | 所有 hook |
+| `SESSION_ID` | 会话 ID | 所有 hook |
+| `LANGUAGE` | 语言代码 | 所有 hook |
+| `HOOK_TYPE` | Hook 类型 | 所有 hook |
+| `CONTENT` | 消息内容 | PreSave |
+| `ROLE` | 消息角色 | PreSave |
+| `RESPONSE` | Agent 响应 | PostAgent |
+| `METADATA` | 元数据 JSON | PostAgent |
+
+### PrePrompt 示例
+
+```python
+#!/usr/bin/env python3
+import os
+import sys
+
+def main():
+ user_identifier = os.environ.get('USER_IDENTIFIER', '')
+ bot_id = os.environ.get('ASSISTANT_ID', '')
+
+ # 输出要注入到 prompt 中的内容
+ print(f"## User Context\n\n用户: {user_identifier}")
+ return 0
+
+if __name__ == '__main__':
+ sys.exit(main())
+```
+
+### PreSave 示例
+
+```python
+#!/usr/bin/env python3
+import os
+import sys
+
+def main():
+ content = os.environ.get('CONTENT', '')
+
+ # 处理内容并输出
+ print(content) # 输出处理后的内容
+ return 0
+
+if __name__ == '__main__':
+ sys.exit(main())
+```
+
+### PostAgent 示例
+
+```python
+#!/usr/bin/env python3
+import os
+import sys
+
+def main():
+ response = os.environ.get('RESPONSE', '')
+ session_id = os.environ.get('SESSION_ID', '')
+
+ # 记录日志（输出到 stderr）
+ print(f"Session {session_id}: Response length {len(response)}", file=sys.stderr)
+ return 0
+
+if __name__ == '__main__':
+ sys.exit(main())
+```
+
+## 使用场景
+
+1. **PrePrompt**: 用户登录时自动加载其偏好设置、历史订单等
+2. **PostAgent**: 记录对话分析数据，触发后续业务流程
+3. **PreSave**: 敏感信息脱敏后再存储，如手机号、邮箱等
diff --git a/skills_autoload/rag-retrieve/hooks/pre_prompt.py b/skills_autoload/rag-retrieve/hooks/pre_prompt.py
new file mode 100644
index 0000000..11f445d
--- /dev/null
+++ b/skills_autoload/rag-retrieve/hooks/pre_prompt.py
@@ -0,0 +1,20 @@
+#!/usr/bin/env python3
+"""
+PreMemoryPrompt Hook - 用户上下文加载器示例
+
+在记忆提取提示词（FACT_RETRIEVAL_PROMPT）加载时执行，
+读取同目录下的 memory_prompt.md 作为自定义记忆提取提示词模板。
+"""
+import sys
+from pathlib import Path
+
+
+def main():
+ prompt_file = Path(__file__).parent / "retrieval-policy.md"
+ if prompt_file.exists():
+ print(prompt_file.read_text(encoding="utf-8"))
+ return 0
+
+
+if __name__ == '__main__':
+ sys.exit(main())
diff --git a/skills_autoload/rag-retrieve/hooks/retrieval-policy.md b/skills_autoload/rag-retrieve/hooks/retrieval-policy.md
new file mode 100644
index 0000000..6527185
--- /dev/null
+++ b/skills_autoload/rag-retrieve/hooks/retrieval-policy.md
@@ -0,0 +1,53 @@
+# Retrieval Policy
+
+### 1. Retrieval Order and Tool Selection
+- Follow this section for source choice, tool choice, query rewrite, `top_k`, fallback, result handling, and citations.
+- Use this default retrieval order and execute it sequentially: skill-enabled knowledge retrieval tools > `rag_retrieve` / `table_rag_retrieve` > local filesystem retrieval.
+- Do NOT answer from model knowledge first.
+- Do NOT skip directly to local filesystem retrieval when an earlier retrieval source may answer the question.
+- When a suitable skill-enabled knowledge retrieval tool is available, use it first.
+- If no suitable skill-enabled retrieval tool is available, or if its result is insufficient, continue with `rag_retrieve` or `table_rag_retrieve`.
+- Use `table_rag_retrieve` first for values, prices, quantities, inventory, specifications, rankings, comparisons, summaries, extraction, lists, tables, name lookup, historical coverage, mixed questions, and unclear cases.
+- Use `rag_retrieve` first only for clearly pure concept, definition, workflow, policy, or explanation questions without structured data needs.
+- After each retrieval step, evaluate sufficiency before moving to the next source. Do NOT run these retrieval sources in parallel.
+
+### 2. Query Preparation
+- Do NOT pass the raw user question unless it already works well for retrieval.
+- Rewrite for recall: extract entity, time scope, attributes, and intent.
+- Add useful variants: synonyms, aliases, abbreviations, related titles, historical names, and category terms.
+- Expand list-style, extraction, overview, historical, roster, timeline, and archive queries more aggressively.
+- Preserve meaning. Do NOT introduce unrelated topics.
+
+### 3. Retrieval Breadth (`top_k`)
+- Apply `top_k` only to `rag_retrieve`. Use the smallest sufficient value, then expand only if coverage is insufficient.
+- Use `30` for simple fact lookup.
+- Use `50` for moderate synthesis, comparison, summarization, or disambiguation.
+- Use `100` for broad recall, such as comprehensive analysis, scattered knowledge, multiple entities or periods, or list / catalog / timeline / roster / overview requests.
+- Raise `top_k` when keyword branches are many or results are too few, repetitive, incomplete, sparse, or too narrow.
+- Use this expansion order: `30 -> 50 -> 100`. If unsure, use `100`.
+
+### 4. Result Evaluation
+- Treat results as insufficient if they are empty, start with `Error:`, say `no excel files found`, are off-topic, miss the core entity or scope, or provide no usable evidence.
+- Also treat results as insufficient when they cover only part of the request, or when full-list, historical, comparison, or mixed data + explanation requests return only partial or truncated coverage.
+
+### 5. Fallback and Sequential Retry
+- If the first retrieval result is insufficient, call the next retrieval source in the default order before replying.
+- If the first RAG tool is insufficient, call the other RAG tool next before moving to local filesystem retrieval.
+- If `table_rag_retrieve` is insufficient or empty, continue with `rag_retrieve`.
+- If `rag_retrieve` is insufficient or empty, continue with `table_rag_retrieve`.
+- If both `rag_retrieve` and `table_rag_retrieve` are insufficient, continue with local filesystem retrieval.
+- Say no relevant information was found only after all applicable skill-enabled retrieval tools, both `rag_retrieve` and `table_rag_retrieve`, and local filesystem retrieval have been tried and still do not provide enough evidence.
+- Do NOT reply that no relevant information was found before the final local filesystem fallback has also been tried.
+
+### 6. Table RAG Result Handling
+- Follow all `[INSTRUCTION]` and `[EXTRA_INSTRUCTION]` content in `table_rag_retrieve` results.
+- If results are truncated, explicitly tell the user total matches (`N+M`), displayed count (`N`), and omitted count (`M`).
+- Cite data sources using filenames from `file_ref_table`.
+
+### 7. Citation Requirements for Retrieved Knowledge
+- When using knowledge from `rag_retrieve` or `table_rag_retrieve`, you MUST generate `` tags.
+- Follow the citation format returned by each tool.
+- Place citations immediately after the paragraph or bullet list that uses the knowledge.
+- Do NOT collect citations at the end.
+- Use 1-2 citations per paragraph or bullet list when possible.
+- If learned knowledge is used, include at least 1 ``.
diff --git a/skills_autoload/rag-retrieve/mcp_common.py b/skills_autoload/rag-retrieve/mcp_common.py
new file mode 100644
index 0000000..5bf5935
--- /dev/null
+++ b/skills_autoload/rag-retrieve/mcp_common.py
@@ -0,0 +1,251 @@
+#!/usr/bin/env python3
+"""
+MCP服务器通用工具函数
+提供路径处理、文件验证、请求处理等公共功能
+"""
+
+import json
+import os
+import sys
+import asyncio
+from typing import Any, Dict, List, Optional, Union
+import re
+
+def get_allowed_directory():
+ """获取允许访问的目录"""
+ # 优先使用命令行参数传入的dataset_dir
+ if len(sys.argv) > 1:
+ dataset_dir = sys.argv[1]
+ return os.path.abspath(dataset_dir)
+
+ # 从环境变量读取项目数据目录
+ project_dir = os.getenv("PROJECT_DATA_DIR", "./projects/data")
+ return os.path.abspath(project_dir)
+
+
+def resolve_file_path(file_path: str, default_subfolder: str = "default") -> str:
+ """
+ 解析文件路径，支持 folder/document.txt 和 document.txt 两种格式
+
+ Args:
+ file_path: 输入的文件路径
+ default_subfolder: 当只传入文件名时使用的默认子文件夹名称
+
+ Returns:
+ 解析后的完整文件路径
+ """
+ # 如果路径包含文件夹分隔符，直接使用
+ if '/' in file_path or '\\' in file_path:
+ clean_path = file_path.replace('\\', '/')
+
+ # 移除 projects/ 前缀（如果存在）
+ if clean_path.startswith('projects/'):
+ clean_path = clean_path[9:] # 移除 'projects/' 前缀
+ elif clean_path.startswith('./projects/'):
+ clean_path = clean_path[11:] # 移除 './projects/' 前缀
+ else:
+ # 如果只有文件名，添加默认子文件夹
+ clean_path = f"{default_subfolder}/{file_path}"
+
+ # 获取允许的目录
+ project_data_dir = get_allowed_directory()
+
+ # 尝试在项目目录中查找文件
+ full_path = os.path.join(project_data_dir, clean_path.lstrip('./'))
+ if os.path.exists(full_path):
+ return full_path
+
+ # 如果直接路径不存在，尝试递归查找
+ found = find_file_in_project(clean_path, project_data_dir)
+ if found:
+ return found
+
+ # 如果是纯文件名且在default子文件夹中不存在，尝试在根目录查找
+ if '/' not in file_path and '\\' not in file_path:
+ root_path = os.path.join(project_data_dir, file_path)
+ if os.path.exists(root_path):
+ return root_path
+
+ raise FileNotFoundError(f"File not found: {file_path} (searched in {project_data_dir})")
+
+
+def find_file_in_project(filename: str, project_dir: str) -> Optional[str]:
+ """在项目目录中递归查找文件"""
+ # 如果filename包含路径，只搜索指定的路径
+ if '/' in filename:
+ parts = filename.split('/')
+ target_file = parts[-1]
+ search_dir = os.path.join(project_dir, *parts[:-1])
+
+ if os.path.exists(search_dir):
+ target_path = os.path.join(search_dir, target_file)
+ if os.path.exists(target_path):
+ return target_path
+ else:
+ # 纯文件名，递归搜索整个项目目录
+ for root, dirs, files in os.walk(project_dir):
+ if filename in files:
+ return os.path.join(root, filename)
+ return None
+
+
+def load_tools_from_json(tools_file_name: str) -> List[Dict[str, Any]]:
+ """从 JSON 文件加载工具定义"""
+ try:
+ tools_file = os.path.join(os.path.dirname(__file__), tools_file_name)
+ if os.path.exists(tools_file):
+ with open(tools_file, 'r', encoding='utf-8') as f:
+ return json.load(f)
+ else:
+ # 如果 JSON 文件不存在，使用默认定义
+ return []
+ except Exception as e:
+ print(f"Warning: Unable to load tool definition JSON file: {str(e)}")
+ return []
+
+
+def create_error_response(request_id: Any, code: int, message: str) -> Dict[str, Any]:
+ """创建标准化的错误响应"""
+ return {
+ "jsonrpc": "2.0",
+ "id": request_id,
+ "error": {
+ "code": code,
+ "message": message
+ }
+ }
+
+
+def create_success_response(request_id: Any, result: Any) -> Dict[str, Any]:
+ """创建标准化的成功响应"""
+ return {
+ "jsonrpc": "2.0",
+ "id": request_id,
+ "result": result
+ }
+
+
+def create_initialize_response(request_id: Any, server_name: str, server_version: str = "1.0.0") -> Dict[str, Any]:
+ """创建标准化的初始化响应"""
+ return {
+ "jsonrpc": "2.0",
+ "id": request_id,
+ "result": {
+ "protocolVersion": "2024-11-05",
+ "capabilities": {
+ "tools": {}
+ },
+ "serverInfo": {
+ "name": server_name,
+ "version": server_version
+ }
+ }
+ }
+
+
+def create_ping_response(request_id: Any) -> Dict[str, Any]:
+ """创建标准化的ping响应"""
+ return {
+ "jsonrpc": "2.0",
+ "id": request_id,
+ "result": {
+ "pong": True
+ }
+ }
+
+
+def create_tools_list_response(request_id: Any, tools: List[Dict[str, Any]]) -> Dict[str, Any]:
+ """创建标准化的工具列表响应"""
+ return {
+ "jsonrpc": "2.0",
+ "id": request_id,
+ "result": {
+ "tools": tools
+ }
+ }
+
+
+def is_regex_pattern(pattern: str) -> bool:
+ """检测字符串是否为正则表达式模式"""
+ # 检查 /pattern/ 格式
+ if pattern.startswith('/') and pattern.endswith('/') and len(pattern) > 2:
+ return True
+
+ # 检查 r"pattern" 或 r'pattern' 格式
+ if pattern.startswith(('r"', "r'")) and pattern.endswith(('"', "'")) and len(pattern) > 3:
+ return True
+
+ # 检查是否包含正则特殊字符
+ regex_chars = {'*', '+', '?', '|', '(', ')', '[', ']', '{', '}', '^', '$', '\\', '.'}
+ return any(char in pattern for char in regex_chars)
+
+
+def compile_pattern(pattern: str) -> Union[re.Pattern, str, None]:
+ """编译正则表达式模式，如果不是正则则返回原字符串"""
+ if not is_regex_pattern(pattern):
+ return pattern
+
+ try:
+ # 处理 /pattern/ 格式
+ if pattern.startswith('/') and pattern.endswith('/'):
+ regex_body = pattern[1:-1]
+ return re.compile(regex_body)
+
+ # 处理 r"pattern" 或 r'pattern' 格式
+ if pattern.startswith(('r"', "r'")) and pattern.endswith(('"', "'")):
+ regex_body = pattern[2:-1]
+ return re.compile(regex_body)
+
+ # 直接编译包含正则字符的字符串
+ return re.compile(pattern)
+ except re.error as e:
+ # 如果编译失败，返回None表示无效的正则
+ print(f"Warning: Regular expression '{pattern}' compilation failed: {e}")
+ return None
+
+
+async def handle_mcp_streaming(request_handler):
+ """处理MCP请求的标准主循环"""
+ try:
+ while True:
+ # Read from stdin
+ line = await asyncio.get_event_loop().run_in_executor(None, sys.stdin.readline)
+ if not line:
+ break
+
+ line = line.strip()
+ if not line:
+ continue
+
+ try:
+ request = json.loads(line)
+ response = await request_handler(request)
+
+ # Write to stdout
+ sys.stdout.write(json.dumps(response, ensure_ascii=False) + "\n")
+ sys.stdout.flush()
+
+ except json.JSONDecodeError:
+ error_response = {
+ "jsonrpc": "2.0",
+ "error": {
+ "code": -32700,
+ "message": "Parse error"
+ }
+ }
+ sys.stdout.write(json.dumps(error_response, ensure_ascii=False) + "\n")
+ sys.stdout.flush()
+
+ except Exception as e:
+ error_response = {
+ "jsonrpc": "2.0",
+ "error": {
+ "code": -32603,
+ "message": f"Internal error: {str(e)}"
+ }
+ }
+ sys.stdout.write(json.dumps(error_response, ensure_ascii=False) + "\n")
+ sys.stdout.flush()
+
+ except KeyboardInterrupt:
+ pass
diff --git a/mcp/rag_retrieve_server.py b/skills_autoload/rag-retrieve/rag_retrieve_server.py
similarity index 100%
rename from mcp/rag_retrieve_server.py
rename to skills_autoload/rag-retrieve/rag_retrieve_server.py
diff --git a/mcp/tools/rag_retrieve_tools.json b/skills_autoload/rag-retrieve/rag_retrieve_tools.json
similarity index 100%
rename from mcp/tools/rag_retrieve_tools.json
rename to skills_autoload/rag-retrieve/rag_retrieve_tools.json
diff --git a/utils/multi_project_manager.py b/utils/multi_project_manager.py
index be29923..a5bca91 100644
--- a/utils/multi_project_manager.py
+++ b/utils/multi_project_manager.py
@@ -320,6 +320,12 @@ def create_robot_project(dataset_ids: List[str], bot_id: str, force_rebuild: boo
str: 机器人项目目录路径
"""
+ skills = list(skills or [])
+ has_rag_retrieve = any(Path(skill.lstrip("@")).name == "rag-retrieve" for skill in skills)
+ if dataset_ids and not has_rag_retrieve:
+ skills.append("@skills_autoload/rag-retrieve")
+ logger.info("Auto loaded skill '@skills_autoload/rag-retrieve' because dataset_ids is not empty")
+
logger.info(f"Ensuring robot project exists: {bot_id}, skills: {skills}")
# 创建机器人目录结构（如果不存在）
@@ -375,27 +381,27 @@ def _extract_skills_to_robot(bot_id: str, skills: List[str], project_path: Path)
- 如果是简单名称（如 "rag-retrieve"），从以下目录按优先级顺序查找：
1. projects/uploads/{bot_id}/skills/
2. skills/
+ - 如果是以 @ 开头的仓库相对路径（如 "@skills_autoload/rag-retrieve"），则从仓库根目录直接解析
搜索目录优先级：先搜索 projects/uploads/{bot_id}/skills/，再搜索 skills/
Args:
bot_id: 机器人 ID
- skills: 技能文件名列表（如 ["rag-retrieve", "projects/uploads/{bot_id}/skills/rag-retrieve"]）
+ skills: 技能文件名列表（如 ["rag-retrieve", "@skills_autoload/rag-retrieve", "projects/uploads/{bot_id}/skills/rag-retrieve"]）
project_path: 项目路径
"""
- import zipfile
-
# skills 源目录（按优先级顺序）
+ repo_root = Path(__file__).resolve().parent.parent
skills_source_dirs = [
project_path / "uploads" / bot_id / "skills",
- Path("skills"),
+ repo_root / "skills",
]
skills_target_dir = project_path / "robot" / bot_id / "skills"
skills_target_dir.mkdir(parents=True, exist_ok=True)
logger.info(f"Copying skills to {skills_target_dir}")
# 清理不在列表中的多余 skill 文件夹
- expected_skill_names = {os.path.basename(skill) for skill in skills}
+ expected_skill_names = {Path(skill.lstrip("@")).name for skill in skills}
if skills_target_dir.exists():
for item in skills_target_dir.iterdir():
if item.is_dir() and item.name not in expected_skill_names:
@@ -403,7 +409,8 @@ def _extract_skills_to_robot(bot_id: str, skills: List[str], project_path: Path)
shutil.rmtree(item)
for skill in skills:
- target_dir = skills_target_dir / os.path.basename(skill)
+ skill_name = Path(skill.lstrip("@")).name
+ target_dir = skills_target_dir / skill_name
# 如果目标目录已存在，跳过复制
if target_dir.exists():
@@ -412,20 +419,25 @@ def _extract_skills_to_robot(bot_id: str, skills: List[str], project_path: Path)
source_dir = None
- # 简单名称：按优先级顺序在多个目录中查找
- for base_dir in skills_source_dirs:
- candidate_dir = base_dir / skill
+ if skill.startswith("@"):
+ candidate_dir = repo_root / skill.lstrip("@")
if candidate_dir.exists():
source_dir = candidate_dir
- logger.info(f" Found skill '{skill}' in {base_dir}")
- break
+ logger.info(f" Found skill '{skill}' at {candidate_dir}")
+
+ # 简单名称：按优先级顺序在多个目录中查找
+ if source_dir is None:
+ for base_dir in skills_source_dirs:
+ candidate_dir = base_dir / skill
+ if candidate_dir.exists():
+ source_dir = candidate_dir
+ logger.info(f" Found skill '{skill}' in {base_dir}")
+ break
if source_dir is None:
logger.warning(f" Skill directory '{skill}' not found in any source directory: {[str(d) for d in skills_source_dirs]}")
continue
- target_dir = skills_target_dir / os.path.basename(skill)
-
try:
shutil.copytree(source_dir, target_dir)
logger.info(f" Copied: {source_dir} -> {target_dir}")