Aug 2026· Proceedings of the 21st International Conference on Software Technologies· 1 citation· ⚡ 1 influential· 35 references
Computer Science
TL;DR
An empirical study of configuration prompt files in Cursor, a widely used AI-assisted code editor, shows that .cursorrules files emerged rapidly from mid-2024 and shows that there is a continuity of themes and topics between the now-legacy .cursorrules files and the current standard .mdc files.
Abstract
Prompts are the primary mechanism for communicating with AI agents, and they directly influence the quality and reliability of AI-generated code. As AI-assisted programming becomes widely adopted, modern tools increasingly combine dynamic conversational prompts with static configuration-like prompt files. Despite the growing focus on prompt engineering, prior research has primarily focused on conversational prompts, while prompt files remain understudied. To address this gap, we conduct an empirical study of configuration prompt files in Cursor, a widely used AI-assisted code editor. We collect and analyze over 12,110 .cursorrules files from 11,427 GitHub repositories to characterize their distribution, evolution, and maintenance. Complementing this, we perform qualitative analysis on a random sample of 65 prompt files and develop a 65-code codebook capturing how developers express programming intent, project context, engineering practices, and security considerations. Our results show that .cursorrules files emerged rapidly from mid-2024. Their adoption is concentrated in small-scale, low-activity, single-maintainer repositories, suggesting toy projects rather than professional development. The content of prompt files is dominated by guidance on code quality and engineering practices, project structure and configuration, and maintainability, while security-related content appears less frequently. Our analysis shows that there is a continuity of themes and topics between the now-legacy .cursorrules files and the current standard .mdc files.
The recent meteoric rise of LLMs (Large Language Models) and associated tools was largely unexpected and surprising to most. The rapid ascent of this technology has caught many software developers unawares, leaving them suddenly somewhat ignorant, and arguably under-skilled.
LLMs, whilst still advancing, have recently demonstrated impressive capabilities in their ability to assist software developers in their day-to-day tasks (e.g., coding new features, and locating and fixing issues). However, the use and adoption of LLMs presents many larger challenges for society as a whole; many of which are not in themselves technical concerns.
This paper examines the current and perceived impact of this technology in the context of Open Source. We identify several social, economic, environmental, political, legal, and technical concerns regarding the use of LLMs in Open Source projects.
We contribute guidance around defining an AI Policy for Open Source projects. We further offer an AI Policy Score Card to assist projects in clearly defining and declaring how they wish to work with AI or not.
Adam Retter· Balisage Series on Markup Te...· 0 citations
An agent skill is a folder containing a SKILL.md file with instructions for a language-model agent, optionally accompanied by scripts and reference files. The agent loads the skill when it judges that a task matches the skill description. Anthropic introduced the format in October 2025 as an open specification. Nine months later, we find that skill files in the millions sit in public GitHub repositories. Skills are unlike the artifacts the SE research community usually mines: they are written mainly in natural language, a model selects them probabilistically at run time, and no compiler or type checker verifies the selection. They also have no central registry or package manager, so they spread by copying folders between repositories. How developers write, reuse, and maintain skills is therefore an empirical question, and no existing dataset records this population. We present GitSkills, a dataset of 3,797,117 SKILL.md files collected from 282,200 public repositories in July 2026. The dataset retains every file occurrence with its repository, path, and content hash. It groups identical files into 1,877,981 distinct contents and enriches one representative per group with the full text, parsed front matter, folder contents, repository metadata, and, for a subset, the commit history of the file. A single self- contained SQLite file supports research on the adoption, reuse, structure, authorship, maintenance, and security of agent skills.
Giuseppe Destefanis, Daniel Graziotin, Matteo Vaccargiu et al.· 0 citations
AI coding assistants such as GitHub Copilot and Cursor have evolved from code-suggestion tools into conversational collaborators, enabling vibe-coding workflows in which developers guide AI-generated code through natural-language dialogue. Although researchers have increasingly recognized the importance of AI coding agents and begun examining their impact on open-source development, a comprehensive understanding of how developers'chat-based interactions with AI relate to subsequent open-source development and collaboration remains limited. This hinders efforts to effectively design, evaluate, and govern AI-assisted open-source software development. To address this gap, we collected 13,360 AI conversation sessions comprising 79,172 user messages from 1,356 OSS repositories, linked them to repository development histories, and complemented this analysis with a targeted developer survey. We find heavier AI use in smaller, less mature, and less collaborative repositories. After AI adoption, projects tended to show more active contributors and lower contributor concentration (p<.001), although communication remained highly concentrated. Code Writing was the dominant chat purpose, and nearly all AI chat sessions were followed by subsequent commits. We find no broad deterioration in code-quality signals or pull request merging rates. However, developers perceive others'AI-generated code as harder to maintain than their own (p = .029) and view AI as lowering barriers to OSS contribution. While most developers (68%) are willing to share their chat, concerns remain around appearing incompetent, increasing reviewer burden, and exposing ideas to competitors. These findings provide a large-scale empirical characterization of AI-assisted OSS contribution and offer practical insights for designing and governing responsible vibe-coding practices in open-source development.
Zihan Fang, Yueke Zhang, Ningzhi Tang et al.· 0 citations
Refactoring is essential for maintaining and evolving software systems, yet we still have limited insight into how automated code-generation agents describe these changes in pull requests. In this study, we analyze refactoring-related pull requests produced by five AI coding agents, with a particular focus on how their intentions are communicated through pull request descriptions. To better understand recurring transformation patterns, we extract refactoring descriptors terms that capture different refactoring activities and use them to identify similarities across agents. We then classify the pull requests into three main categories: internal quality attributes, external quality attributes, and code smells. Our dataset includes 2,288 unique AIgenerated refactoring pull requests from OpenAI Codex, Devin, GitHub Copilot, Cursor, and Claude Code. The results reveal several limitations in how AI systems perform and document refactoring, highlighting the need for clearer and more structured communication in collaborative software development.
Aymen Masmoudi, Belhassen Khefacha, Andrew Haralambous et al.· Annual International Compute...· 0 citations
Refactoring is essential for maintaining and evolving software systems, yet we still have limited insight into how automated code-generation agents describe these changes in pull requests. In this study, we analyze refactoring-related pull requests produced by five AI coding agents, with a particular focus on how their intentions are communicated through pull request descriptions. To better understand recurring transformation patterns, we extract refactoring descriptors terms that capture different refactoring activities and use them to identify similarities across agents. We then classify the pull requests into three main categories: internal quality attributes, external quality attributes, and code smells. Our dataset includes 2,288 unique AIgenerated refactoring pull requests from OpenAI Codex, Devin, GitHub Copilot, Cursor, and Claude Code. The results reveal several limitations in how AI systems perform and document refactoring, highlighting the need for clearer and more structured communication in collaborative software development.
Aymen Masmoudi, Belhassen Khefacha, Andrew Haralambous et al.· Annual International Compute...· 0 citations
Prompt engineering is now a significant aspect of large language models (LLMs) to make them as effective in applications like conversational agents, educational assistants, automated code-generating systems, and content generation systems. Nonetheless, urgent design is frequently done in informal and solitary fashion devoid of systematic processes of collective enhancement, quality evaluation, or evolutionary monitoring. This paper suggests NeuroPrompt, an open-source prompt engineering system, which combines version tracking with evolution, directed acyclic graph (DAG) based lineage modeling, and community-based quality assessment. Users can create prompts, fork better prompts, and assess the performance of prompts with a multi-rater consensus system to assess the prompts based on clarity, creativity, and usefulness. The forking is directed by a utility-based decision model that ensures that unnecessary prompt duplication is avoided, and the effectiveness of optimization is enhanced. Experimental analysis reveals that collaborative prompt evolution is more effective in prompt quality and the overall accuracy of prompt generation in providing relevant and useful responses to tasks is 96.3% in comparison against baseline prompt design methods which has a score of about 88-91. There are also the results of the improved consensus reliability and lesser variance of ratings among the community evaluators. The suggested system will change timely engineering into a transparent and organized evolutionary process allowing sharing of knowledge systematically and optimizing prompt through collaborative optimization to large-scale AI initiatives.
D. Ragunath, Vaishak C J· International Conference Com...· 0 citations
