by InternScience · Codex Skill · ★ 227
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ResearchClawBench         Evaluating AI Agents for Automated Research from Re-Discovery to New-Discovery Quick Start Add Your Agent ResearchClawBench is a benchmark that measures
| Stars | 227 |
| Forks | 21 |
| Language | Jupyter Notebook |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 61.21581102341/100 |
| Open Issues | 2 |
| Last Updated | 2026-07-28 |
| Created | 2026-03-18 |
| Platforms | claude-code, codex |
| Est. Tokens | ~24k |
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ResearchClawBench is 🦞 ResearchClawBench: Evaluating AI Agents for Automated Research from Re-Discovery to New-Discovery. It is categorized as a Codex Skill with 227 GitHub stars.
ResearchClawBench is primarily written in Jupyter Notebook. It covers topics such as agent, ai, ai-agent.
You can find installation instructions and usage details in the ResearchClawBench GitHub repository at github.com/InternScience/ResearchClawBench. The project has 227 stars and 21 forks, indicating an active community.
ResearchClawBench is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to ResearchClawBench on Agent Skills Hub include AutoR, AutoR, De-Anthropocentric-Research-Engine. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.