{"skill":{"slug":"a-share-quant-report","displayName":"a-share-quant-report","summary":"面向A股金工研报复现的 skill，自动按研报框架拆解研究问题、选择至少1000只股票的数据集、调用标准化 Python 回测框架，并按研报逻辑系统性展示结果与图片。","tags":{"latest":"1.0.2"},"stats":{"comments":0,"downloads":185,"installsAllTime":0,"installsCurrent":0,"stars":0,"versions":3},"createdAt":1778066198270,"updatedAt":1778492859420},"latestVersion":{"version":"1.0.2","createdAt":1778121756370,"changelog":"**v1.2.0 is a major update focusing on standardized \"研报式\" output, new research pattern support, and integration of a default Python backtest framework.**\n\n- 支持区分“因子研究型”与“热点跟踪型”研报，不再一刀切处理所有研报为因子回测\n- 默认输出结构严格按研报完整链路组织，包括研究问题、因子定义、数据集、回测设定、结果与图片解读等\n- 增加对不少于1000只股票候选池的刚性目标判定和说明，显式报告实际下载数及合理性\n- 新增标准化Python回测脚本（`python_report_style_factor_backtest.py`）为主要执行框架，提升因子回测的一致性和可维护性\n- 附带多份规范文档（如`research-framework-patterns.md`、`report-presentation-guidelines.md`等）统一输出、图片展示和交互流程\n-","license":"MIT-0"},"metadata":{"os":null,"systems":null},"owner":{"handle":"0x2hacks","userId":"s1704gatdk4h5wzqx7bqvvdttn85wjrh","displayName":"0x2Hacks","image":"https://avatars.githubusercontent.com/u/99119378?v=4"},"moderation":null}