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
Dowhy is the strongest overall pick here because it has a 100/100 readiness score and fits Coding agents.
Strongest overall
Dowhy
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Fastest prototype
Dowhy
Best first install candidate based on install readiness and adoption.
Freshest repo
Dowhy
Most recent maintenance signal among this shortlist.
| Signal | Dowhy DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks. |
|---|---|
| Quality | 100/100 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 | 8.2K stars 0 installs |
| Freshness | Jun 16, 2026 |
| Use-case fit | |
| Stack fit | |
| Platform hints | Python, Machine Learning, Claude Code |
| Warnings | No OpenAgentSkill engagement data yet |
| Best for | Coding 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 py-why/dowhy |