{"skill":{"slug":"cda","displayName":"Causal Dynamics Architecture","summary":"因果动力学架构（Causal Dynamics Architecture, CDA）领域知识参考。 当用户讨论 CDA 架构设计、因果机制网络、哈密顿约束、因果封装递归、 物理约束神经网络、因果推断与深度学习融合、统计力学启发的人工智能时触发。 提供架构全局认知和详细参考文件的按需深入能力。","tags":{"latest":"1.0.0"},"stats":{"comments":0,"downloads":51,"installsAllTime":0,"installsCurrent":0,"stars":0,"versions":1},"createdAt":1777218029265,"updatedAt":1777218107182},"latestVersion":{"version":"1.0.0","createdAt":1777218029265,"changelog":"- Initial public release of the \"cda\" skill, providing comprehensive reference knowledge on Causal Dynamics Architecture (CDA).\n- Includes detailed descriptions and documentation covering core concepts, system architecture, key data structures, terminology, and capability comparisons with existing AI methods.\n- Reference files offer a full knowledge chain from the evolution of digital civilization to third-generation AI paradigms, and to the technical implementation of CDA.\n- Guides users on how to access in-depth documentation and technical details on demand.\n- Outlines known limitations, open questions, originality assessment, and a phased implementation roadmap.","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}