No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by agentscope-ai · Agent Tool · ★ 829
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is OpenJudge safe to install? View the security audit →
Holistic Evaluation, Quality Rewards: Driving Application Excellence 🌟 If you find OpenJudge helpful, please give us a Star! 🌟 🌐 Website 中文 OpenJudge is an open-source evaluation framework for AI applications (e.g., AI agents or chatbots) designed to evaluate quality and drive continuous application optimization. In practice, application excellence depends on a trustworthy evaluation workflow: Collect test data → Define graders → Run evaluatio
| Stars | 829 |
| Forks | 67 |
| Language | Python |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 60.0328618065128/100 |
| Open Issues | 13 |
| Last Updated | 2026-09-11 |
| Created | 2025-07-08 |
| Platforms | python |
| Est. Tokens | ~18k |
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OpenJudge is OpenJudge: A Unified Framework for Holistic Evaluation and Quality Rewards. It is categorized as a Agent Tool with 829 GitHub stars.
OpenJudge is primarily written in Python. It covers topics such as agent, agent-skills, ai-agent.
You can find installation instructions and usage details in the OpenJudge GitHub repository at github.com/agentscope-ai/OpenJudge. The project has 829 stars and 67 forks, indicating an active community.
OpenJudge is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to OpenJudge on Agent Skills Hub include claude-code-plugins-plus-skills, trpc-agent-go, AgentChat. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: