Why IDE-Level AI Rules Will Always Lose to Model-Level Capabilities (lellansin.github.io)

Claude Skill's launch marks a turning point in the integration of AI capabilities, contrasting sharply with the earlier Cursor rules that sought to enhance model behavior through an external application layer. While Cursor's .cursorrules aimed to create reusable behavioral constraints, they struggled due to limited compatibility with various large language models (LLMs) and were unable to evolve into a standard accepted across the community. In contrast, Claude Skill benefits from its positioning as a foundational model capability, leveraging deep integration during model training and alignment phases to establish itself as a modular and reusable standard. This enables Skills to not only control model behavior but also influence reasoning and manage state, setting them apart as essential components in the evolving landscape of AI/ML. The significance of this development lies in its implications for the future of model architecture and standardization. As the industry shifts from mere text prediction to the prediction and execution of complex tasks, the competition will focus on models' ability to reliably deliver and adapt to real-world scenarios. With Anthropic's advantages in data acquisition and capability standardization, it is poised to lead in this new era, where the capacity to predict high-quality next actions will determine success. This evolution signals a departure from traditional prompt engineering to a more sophisticated integration of capabilities directly within model frameworks, enhancing the utility and effectiveness of LLMs across diverse applications.