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 AISmithLab · Agent Tool · ★ 95
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is CoBRA safe to install? View the security audit →
Toward Precise and Consistent Agent Behaviors across Models Anchored by Validated Social Science Knowledge 🌐 Project Page: cobra.clawder.ai | 📄 Paper: arXiv 2509.13588 If you find CoBRA useful, please star ⭐ this repo to help others discover it! 💡 What is Cognitive Bias? Systematic deviations from rat
| Stars | 95 |
| Forks | 2 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 62.3765199868197/100 |
| Open Issues | 2 |
| Last Updated | 2026-04-21 |
| Created | 2025-10-25 |
| Platforms | python |
| Est. Tokens | ~567k |
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CoBRA is [🏆 CHI26 Best Paper] CoBRA: Reproducible control of LLM agent behavior via classic social science experiments. It is categorized as a Agent Tool with 95 GitHub stars.
CoBRA is primarily written in Python. It covers topics such as agent-based-modeling, ai-agent, ai-tools.
You can find installation instructions and usage details in the CoBRA GitHub repository at github.com/AISmithLab/CoBRA. The project has 95 stars and 2 forks, indicating an active community.
CoBRA is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to CoBRA on Agent Skills Hub include toolsdk-mcp-registry, deterministic-agent-control-protocol, ai-agents-reality-check. 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: