{"skill":{"slug":"cda-code-lab","displayName":"CDA Code Lab","summary":"CDA 架构代码工坊——按 Causal Dynamics Architecture 规范生成可执行的 Python 仿真代码。 覆盖核心组件：CDABlock 管线、PINN 机制函数、哈密顿投影模块、辛积分器、NOTEARS 因果发现、 贝叶斯在线更新、CER 因果封装递归。 生成的代码可直接运行，依赖 Py...","tags":{"latest":"1.0.0"},"stats":{"comments":0,"downloads":40,"installsAllTime":0,"installsCurrent":0,"stars":0,"versions":1},"createdAt":1777233606794,"updatedAt":1777234308537},"latestVersion":{"version":"1.0.0","createdAt":1777233606794,"changelog":"CDA 架构代码工坊 1.0.0 – 初始发布\n\n- 支持按 Causal Dynamics Architecture (CDA) 规范生成可执行的 Python 仿真代码。\n- 覆盖核心组件：CDABlock 管线、PINN 机制函数、哈密顿投影、辛积分器、NOTEARS 因果发现、贝叶斯在线更新、CER 因果封装递归等。\n- 代码可直接运行，主要依赖 PyTorch，可读取 CDA Data Synthesizer 输出的 JSON 数据。\n- 提供详细的组件划分、生成流程和代码生成原则，自带引用标准与实现文档导航。\n- 适用于因果动力学仿真、机制函数实现、实验原型搭建等多种研发场景。","license":"MIT-0"},"metadata":{"os":null,"systems":null},"owner":{"handle":"wangjiaocheng","userId":"s175zq13kt63ypfry5rgxh1pv18440m2","displayName":"波动几何","image":"https://avatars.githubusercontent.com/u/25478397?v=4"},"moderation":null}