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 ValueByte-AI · Agent Tool · ★ 648
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Awesome-LLM-in-Social-Science safe to install? View the security audit →
Awesome-LLM-in-Social-Science 🔗 Recommended Resource: Check out Awesome-LLM-Psychometrics for a comprehensive collection of papers and resources on LLM psychometrics, including evaluation, validation, and enhancement. Below we compile awesome papers that evaluate Large Language Models (LLMs) from a perspective of Social Science. align LLMs from a perspective of Social Science. employ LLMs to facilitate research, address issues, and enhance tools in Social Science. contribute surveys, perspectives, and datasets on the above topics. The above taxonomies are by no means orthogonal. For example, evaluations require simulations. We categorize these papers based on our understanding of their focus. This collection has a special focus on Psychology and intrinsic values. Welcome to contribute and discuss! 🤩 Papers marked with a ⭐️ are contributed by the maintainers of this repository. If you find them useful, we would greatly appreciate it if you could give the repository a star and cite our paper.
| Stars | 648 |
| Forks | 52 |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 52.058781312747/100 |
| Last Updated | 2026-09-07 |
| Created | 2023-10-15 |
| Est. Tokens | ~24k |
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Awesome-LLM-in-Social-Science is Awesome papers involving LLMs in Social Science.. It is categorized as a Agent Tool with 648 GitHub stars.
You can find installation instructions and usage details in the Awesome-LLM-in-Social-Science GitHub repository at github.com/ValueByte-AI/Awesome-LLM-in-Social-Science. The project has 648 stars and 52 forks, indicating an active community.
Awesome-LLM-in-Social-Science is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to Awesome-LLM-in-Social-Science on Agent Skills Hub include LLMCompiler, Awesome-Agent-Skills-for-Empirical-Research, prompttools. 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: