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面向基金投研分析领域的风格漂移任务Skill，围绕「风格漂移识别助手-偏股版」场景提供信息抽取、结构化分析与结果输出。
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Read SKILL.md and any companion files shown by SkillsMP before deciding whether to install.
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npx skills add https://github.com/aifinlab/FinClaw --skill style-drift-detection-equity-biasedThe command stays on one line. Scroll horizontally to inspect it before copying.
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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 | style-drift-detection-equity-biased |
| description | 面向基金投研分析领域的风格漂移任务Skill，围绕「风格漂移识别助手-偏股版」场景提供信息抽取、结构化分析与结果输出。 |
pip install -r requirements.txt
python scripts/main.py --input sample.txt --output-json result.json --output-md report.md
{
"skill": "风格漂移识别助手-偏股版",
"domain": "投研分析",
"scene": "风格漂移",
"input_summary": {
"fund_code": "000000",
"fund_name": "示例基金",
"period": "2024Q4",
"data_coverage": "净值/持仓/披露/市场"
},
"key_findings": [
"关键结论1",
"关键结论2"
],
"data_quality": {
"has_text": true,
"text_length": 1200
},
"limitations": [
"仅基于输入信息形成初步判断"
]
}