Skill comparison
Compare agent skills before installing.
Put high-signal skills side by side and inspect quality, adoption, freshness, install readiness, use-case fit, and warnings in one place.
Comparing 1 skill
Use this as a shortlist, then open the skill detail page before adopting.
Decision summary
Auto Empirical Research Skills is the strongest overall pick here because it has a 100/100 readiness score and fits GitHub automation.
Strongest overall
Auto Empirical Research Skills
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Fastest prototype
Auto Empirical Research Skills
Best first install candidate based on install readiness and adoption.
Freshest repo
Auto Empirical Research Skills
Most recent maintenance signal among this shortlist.
| Signal | Auto Empirical Research Skills 🔬 A curated collection of 23,000+ agent skills for empirical research across 8 social science disciplines. | 精选 23,000+ AI Agent 技能库，覆盖8大社会科学学科的实证研究。CoPaper.AI 20分钟完成一篇可复现的规范实证论文，并支持用户上传 Skills。-- Maintained by CoPaper.AI from Stanford REAP. |
|---|---|
| Quality | 99/100 Excellent |
| Decision verdict | 100/100 Production-ready Use this as a leading candidate, then validate the README and install path in your own agent stack. |
| Adoption | 1.7K stars 0 installs |
| Freshness | Jun 5, 2026 |
| Use-case fit | |
| Stack fit | |
| Platform hints | Stata, AI Agents, Claude Code |
| Warnings | No OpenAgentSkill engagement data yet |
| Best for | GitHub automation workflows · Claude Code teams · teams that value GitHub adoption signals |
| Not ideal for | teams that need a vendor-supported SLA · high-compliance environments without internal security review |
| OpenAgentSkill engagement | 0 views 0 install copies |
| Install | $ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills |