Agent Skills for Context Engineering (github.com)

A new open-source collection of Agent Skills has been launched, focusing on context engineering to enhance the effectiveness of AI agent systems. Context engineering differs from prompt engineering by emphasizing the systematic curation of all information entering a model's context window. As language models have limited attention resources, mastering this discipline addresses issues like attention degradation and enables the development of robust, production-ready agents. The skills cover a range of critical areas, including understanding context dynamics, recognizing patterns of context failure, and optimizing memory systems. The release is significant for the AI/ML community as it establishes foundational knowledge necessary for building and optimizing multi-agent architectures and enhancing operational efficiency. With a platform-agnostic approach, these skills can be implemented across various systems, including Claude Code and Cursor. The included resources, such as practical examples illustrated with Python pseudocode and a structured template method, facilitate immediate application of these principles in diverse environments. Notably, contributions to the repository are encouraged, promoting collaboration and continuous improvement in context engineering practices within the community.