TL;DR
A dataset of context, skill and subagent files and an open-source tool to identify and extract such files automatically are provided to enable large-scale empirical studies of collaborative GitHub repositories involving coding agents.
Coding-agent evaluation has progressed from resolving individual issues to carrying out long-horizon development, yet task completion is still largely assessed within a single codebase. In software ecosystems, many features and bug fixes require coordinated changes across multiple repositories. We introduce WideSWE to...
A large-scale dataset of agent activity on GitHub, exposing not only what agents contribute but also how they work enables research on agent behavior, efficiency and cost, task formulation, failure modes, and human-agent collaboration in agentic software engineering.
AI coding agents such as Claude Code, Cursor, Codex, and OpenCode have matured into capable multi-step software engineers, yet nearly all of them still assume a developer is sitting at a terminal. PromptBridge removes that assumption by connecting six messaging platforms, Telegram, Discord, Slack, Microsoft Teams, GitH...
AI-research agents, or autoresearch systems, combine language models with tools, search, evaluation, and iterative modification of research artifacts. Their public software ecology is hard to compare because repositories, papers, benchmarks, libraries, and companion artifacts are often counted as one population. We con...
A longitudinal analysis of generative-AI-related discussions in the Visual Studio Code (VS Code) GitHub repository, using 43,806 candidate issues created between January 2021 and June 2026, suggests that GitHub Issues provide a practical, workflow-oriented perspective on generative AI that complements survey-based stud...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
