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A股逆向选择/信息不对称分析。当用户说"逆向选择"、"adverse selection"、"信息不对称"、"知情交易者"、"逆向选择成本"、"信息劣势"时触发。基于 cn-stock-data 获取数据，度量逆向选择与信息不对称程度。支持 formal/brief 两种输出风格。
A股量价异常检测/异动监控。当用户说"异常检测"、"异动"、"anomaly"、"量价异常"、"异常波动"、"XX有异动"时触发。量化检测股价和成交量异常。支持formal和brief风格。
A股自相关/序列相关性/收益率自相关结构分析。当用户说"自相关"、"autocorrelation"、"序列相关"、"收益率预测性"、"动量还是反转"、"自相关系数"、"ACF"、"PACF"、"Ljung-Box"、"收益率是否可预测"、"随机游走检验"时触发。MUST USE when user asks about return autocorrelation, serial correlation tests, or whether a stock's returns are predictable. 量化分析收益率的自相关结构（ACF/PACF、Ljung-Box检验、随机游走检验）。支持formal和brief风格。
Based on SOC occupation classification
Showing SKILL.md
| name | table-structure-recognition |
| description | 用于表头行列识别的表格结构识别原子 skill，适用于通用行业文档解析场景。 |
本 Skill 支持多种表格输入格式，核心数据来源包括：
说明：本 Skill 不包含表格采集功能，需要用户提供表格文件。建议表格格式规范，以便进行准确的结构识别。
本 Skill 提供全面的表格结构识别能力，涵盖多种识别功能：
{
"table_info": {
"table_id": 1,
"position": {
"page": 5,
"bbox": [100, 200, 500, 600]
},
"source_file": "document.pdf"
},
"structure": {
"row_count": 10,
"column_count": 5,
"header_rows": 2,
"data_rows": 8,
"has_merged_cells": true
},
"headers"