refactor: move bundled skills tree to repo root skills/

- Relocate runtime/skills -> skills/; runtime_skills_root() prefers oclaw/skills
  with fallback to legacy runtime/skills for unmigrated trees.
- session-bootstrap hook handler resolves repo root via PROJECT_ROOT.
- Update architecture/migration docs and in-tree skill references.

Co-authored-by: Cursor <cursoragent@cursor.com>

skills/tushare-finance/scripts/api_client.py

@@ -0,0 +1,340 @@
+#!/usr/bin/env python3
+"""
+Tushare API 客户端
+
+封装 Tushare Pro API，提供简洁易用的接口
+"""
+
+import os
+import pandas as pd
+import tushare as ts
+from typing import Optional, List, Union, Dict
+from pathlib import Path
+import logging
+from datetime import datetime, timedelta
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+class TushareAPI:
+    """Tushare API 客户端"""
+
+    def __init__(self, token: Optional[str] = None):
+        """
+        初始化 API 客户端
+
+        Args:
+            token: Tushare Token，默认从环境变量 TUSHARE_TOKEN 读取
+        """
+        self.token = token or os.getenv('TUSHARE_TOKEN')
+        if not self.token:
+            raise ValueError(
+                "未找到 Tushare Token。请设置环境变量 TUSHARE_TOKEN 或传入 token 参数。\n"
+                "获取 Token: https://tushare.pro"
+            )
+
+        # 初始化 Tushare API
+        self.pro = ts.pro_api(self.token)
+        logger.info("Tushare API 客户端初始化成功")
+
+    # ==================== 股票数据 ====================
+
+    def get_stock_daily(
+        self,
+        ts_code: str,
+        start_date: str,
+        end_date: str
+    ) -> pd.DataFrame:
+        """
+        查询股票日线行情
+
+        Args:
+            ts_code: 股票代码（如 "000001.SZ"）
+            start_date: 开始日期（格式：YYYY-MM-DD 或 YYYYMMDD）
+            end_date: 结束日期（格式：YYYY-MM-DD 或 YYYYMMDD）
+
+        Returns:
+            pd.DataFrame: 日线数据
+        """
+        # 统一日期格式
+        start_date = self._format_date(start_date)
+        end_date = self._format_date(end_date)
+
+        logger.info(f"查询股票日线: {ts_code} ({start_date} ~ {end_date})")
+
+        df = self.pro.daily(
+            ts_code=ts_code,
+            start_date=start_date,
+            end_date=end_date
+        )
+
+        if df.empty:
+            logger.warning(f"未查询到数据: {ts_code}")
+        else:
+            logger.info(f"查询成功，共 {len(df)} 条记录")
+
+        return df
+
+    def get_stock_info(self, ts_code: str) -> Dict:
+        """
+        查询股票基本信息
+
+        Args:
+            ts_code: 股票代码
+
+        Returns:
+            dict: 股票基本信息
+        """
+        logger.info(f"查询股票信息: {ts_code}")
+
+        df = self.pro.stock_basic(
+            ts_code=ts_code,
+            fields='ts_code,name,area,industry,market,list_date'
+        )
+
+        if df.empty:
+            logger.warning(f"未查询到股票信息: {ts_code}")
+            return {}
+
+        return df.iloc[0].to_dict()
+
+    def get_stock_list(self, market: Optional[str] = None) -> pd.DataFrame:
+        """
+        查询股票列表
+
+        Args:
+            market: 市场类型（主板、创业板、科创板、CDR），None 表示全部
+
+        Returns:
+            pd.DataFrame: 股票列表
+        """
+        logger.info(f"查询股票列表: market={market}")
+
+        df = self.pro.stock_basic(market=market)
+        logger.info(f"查询成功，共 {len(df)} 只股票")
+
+        return df
+
+    # ==================== 财务数据 ====================
+
+    def get_financial_indicator(
+        self,
+        ts_code: str,
+        start_date: str,
+        end_date: str
+    ) -> pd.DataFrame:
+        """
+        查询财务指标
+
+        Args:
+            ts_code: 股票代码
+            start_date: 开始日期
+            end_date: 结束日期
+
+        Returns:
+            pd.DataFrame: 财务指标数据
+        """
+        start_date = self._format_date(start_date)
+        end_date = self._format_date(end_date)
+
+        logger.info(f"查询财务指标: {ts_code} ({start_date} ~ {end_date})")
+
+        df = self.pro.fina_indicator(
+            ts_code=ts_code,
+            start_date=start_date,
+            end_date=end_date
+        )
+
+        return df
+
+    def get_income_statement(
+        self,
+        ts_code: str,
+        start_date: str,
+        end_date: str
+    ) -> pd.DataFrame:
+        """
+        查询利润表
+
+        Args:
+            ts_code: 股票代码
+            start_date: 开始日期
+            end_date: 结束日期
+
+        Returns:
+            pd.DataFrame: 利润表数据
+        """
+        start_date = self._format_date(start_date)
+        end_date = self._format_date(end_date)
+
+        logger.info(f"查询利润表: {ts_code} ({start_date} ~ {end_date})")
+
+        df = self.pro.income(
+            ts_code=ts_code,
+            start_date=start_date,
+            end_date=end_date
+        )
+
+        return df
+
+    # ==================== 指数数据 ====================
+
+    def get_index_daily(
+        self,
+        ts_code: str,
+        start_date: str,
+        end_date: str
+    ) -> pd.DataFrame:
+        """
+        查询指数日线行情
+
+        Args:
+            ts_code: 指数代码（如 "000300.SH"）
+            start_date: 开始日期
+            end_date: 结束日期
+
+        Returns:
+            pd.DataFrame: 指数日线数据
+        """
+        start_date = self._format_date(start_date)
+        end_date = self._format_date(end_date)
+
+        logger.info(f"查询指数日线: {ts_code} ({start_date} ~ {end_date})")
+
+        df = self.pro.index_daily(
+            ts_code=ts_code,
+            start_date=start_date,
+            end_date=end_date
+        )
+
+        return df
+
+    def get_index_weight(
+        self,
+        index_code: str,
+        date: str
+    ) -> pd.DataFrame:
+        """
+        查询指数成分权重
+
+        Args:
+            index_code: 指数代码
+            date: 日期
+
+        Returns:
+            pd.DataFrame: 成分权重数据
+        """
+        date = self._format_date(date)
+
+        logger.info(f"查询指数成分: {index_code} {date}")
+
+        df = self.pro.index_weight(
+            index_code=index_code,
+            trade_date=date
+        )
+
+        return df
+
+    # ==================== 批量查询 ====================
+
+    def batch_query(
+        self,
+        ts_codes: List[str],
+        start_date: str,
+        end_date: str
+    ) -> Dict[str, pd.DataFrame]:
+        """
+        批量查询多只股票
+
+        Args:
+            ts_codes: 股票代码列表
+            start_date: 开始日期
+            end_date: 结束日期
+
+        Returns:
+            dict: {股票代码: DataFrame}
+        """
+        logger.info(f"批量查询: {len(ts_codes)} 只股票")
+
+        results = {}
+        for i, ts_code in enumerate(ts_codes, 1):
+            logger.info(f"进度: {i}/{len(ts_codes)} - {ts_code}")
+
+            try:
+                df = self.get_stock_daily(ts_code, start_date, end_date)
+                results[ts_code] = df
+            except Exception as e:
+                logger.error(f"查询失败: {ts_code}, 错误: {e}")
+                results[ts_code] = pd.DataFrame()
+
+        success_count = sum(1 for df in results.values() if not df.empty)
+        logger.info(f"批量查询完成: 成功 {success_count}/{len(ts_codes)}")
+
+        return results
+
+    # ==================== 数据导出 ====================
+
+    def export_data(
+        self,
+        df: pd.DataFrame,
+        output_file: str,
+        format: str = 'csv'
+    ) -> bool:
+        """
+        导出数据到文件
+
+        Args:
+            df: 数据
+            output_file: 输出文件路径
+            format: 输出格式（csv, json, excel）
+
+        Returns:
+            bool: 是否导出成功
+        """
+        try:
+            if format == 'csv':
+                df.to_csv(output_file, index=False)
+            elif format == 'json':
+                df.to_json(output_file, orient='records', indent=2)
+            elif format == 'excel':
+                df.to_excel(output_file, index=False)
+            else:
+                raise ValueError(f"不支持的格式: {format}")
+
+            logger.info(f"数据已导出到: {output_file}")
+            return True
+
+        except Exception as e:
+            logger.error(f"导出失败: {e}")
+            return False
+
+    # ==================== 工具方法 ====================
+
+    @staticmethod
+    def _format_date(date_str: str) -> str:
+        """
+        统一日期格式为 YYYYMMDD
+
+        Args:
+            date_str: 日期字符串（支持 YYYY-MM-DD 或 YYYYMMDD）
+
+        Returns:
+            str: YYYYMMDD 格式的日期
+        """
+        if '-' in date_str:
+            return date_str.replace('-', '')
+        return date_str
+
+
+if __name__ == "__main__":
+    # 示例用法
+    api = TushareAPI()
+
+    # 查询股票数据
+    df = api.get_stock_daily("000001.SZ", "2024-01-01", "2024-12-31")
+    print(df.head())
+
+    # 查询股票信息
+    info = api.get_stock_info("000001.SZ")
+    print(info)