Skill comparison
Use this as a shortlist, then open the skill detail page before adopting.
Decision summary
Strongest overall
hypothesis-generation
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Fastest prototype
hypothesis-generation
Best first install candidate based on install readiness and adoption.
Freshest repo
hypothesis-generation
Most recent maintenance signal among this shortlist.
| Signal | hypothesis-generation Structured hypothesis formulation from observations. Use when you have experimental observations or data and need to formulate testable hypotheses with predictions, propose mechanisms, and design experiments to test them. Follows scientific method framework. For open-ended ideation use scientific-brainstorming; for automated LLM-driven hypothesis testing on datasets use hypogenic. |
|---|---|
| Quality | 82/100 Strong |
| Decision verdict | 93/100 Production-ready Use this as a leading candidate, then validate the README and install path in your own agent stack. |
| Adoption | 3.6K stars Verified outcomes are shown on each skill page |
| Freshness | Sep 1, 2026 |
| Use-case fit | |
| Workflow fit | |
| Platform hints | Claude Code |
| Warnings | No OpenAgentSkill engagement data yet |
| Best for | Research agents 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 --skill hypothesis-generation |