While Large Language Models (LLMs) have demonstrated remarkable capabilities, research shows their effectiveness is highly dependent not only on explicit prompts but also on the broader context provided. This requirement is particularly pronounced in software engineering, where the goals, architecture, and collaborative conventions of an existing project play critical roles in the response quality. To support this, many AI coding assistants have introduced ways for developers to author persistent, machine-readable directives that encode a project’s unique constraints. While this practice is growing, the content of these directives remains unstudied.

This paper presents the first large-scale empirical study to characterize this emerging form of developer-provided context. Through a qualitative analysis of 401 open-source repositories containing cursor rules, we developed a comprehensive taxonomy of project context that developers consider essential, organized into four high-level themes: Conventions, Guidelines, Project Information, and LLM Directives. Our study also explores how this context varies across different project types and programming languages, offering implications for the next generation of context-aware AI developer tools.