Claw4Science: A Dataset and Platform for the OpenClaw Scientific Agent Ecosystem

Large language models have enabled a new class of scientific software in the form of AI agents that can execute research workflows across bioinformatics, drug discovery, and related domains. Among these systems, OpenClaw introduced a skill-based design that allows workflows to be expressed as structured Markdown files, lowering the barrier to contribution and enabling rapid ecosystem growth. However, this growth has led to fragmentation. Projects are distributed across independent repositories, skills vary widely in quality, naming is inconsistent, and there is no unified way to discover or compare available tools. In this work, we construct the first curated dataset of the OpenClaw scientific ecosystem. The dataset includes 91 projects organized by functional role and 2,230 skills spanning 34 scientific categories. Based on this dataset, we perform a systematic analysis of the structure, distribution, and emerging patterns of scientific agent development. To make this ecosystem accessible in practice, we further build Claw4Science, a public platform at https://claw4science.org, which is built on top of our dataset. The platform organizes projects and aggregates distributed skill repositories into a unified interface, with a focus on bioinformatics and scientific workflows, providing a practical entry point for navigating the ecosystem. Our results show that the OpenClaw ecosystem reflects a shift from isolated systems to a more modular and shareable model of scientific computation. At the same time, challenges in evaluation, reproducibility, and governance remain open. We argue that our dataset provides a foundation for future benchmark development and standardized infrastructure for scientific AI agents.