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 4 skills

Use this as a shortlist, then open the skill detail page before adopting.

Add more skills

Decision summary

Scikit Learn is the strongest overall pick here because it has a 100/100 readiness score and fits GitHub automation.

Strongest overall

Scikit Learn

Use this as a leading candidate, then validate the README and install path in your own agent stack.

Fastest prototype

Scikit Learn

Best first install candidate based on install readiness and adoption.

Freshest repo

Awpy

Most recent maintenance signal among this shortlist.

SignalAwpy

Python library to parse, analyze and visualize Counter-Strike 2 data

Nautilus Trader

Production-grade Rust-native trading engine with deterministic event-driven architecture

Pandas

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

Scikit Learn

scikit-learn: machine learning in Python

Quality
79/100
Strong
100/100
Excellent
100/100
Excellent
100/100
Excellent
Decision verdict
90/100
Production-ready

Use this as a leading candidate, then validate the README and install path in your own agent stack.

100/100
Production-ready

Use this as a leading candidate, then validate the README and install path in your own agent stack.

100/100
Production-ready

Use this as a leading candidate, then validate the README and install path in your own agent stack.

100/100
Production-ready

Use this as a leading candidate, then validate the README and install path in your own agent stack.

Adoption583 stars
Verified outcomes are shown on each skill page
23K stars
Verified outcomes are shown on each skill page
49K stars
Verified outcomes are shown on each skill page
66K stars
Verified outcomes are shown on each skill page
FreshnessJun 18, 2026Jun 14, 2026Jun 14, 2026Jun 17, 2026
Use-case fit
Workflow fit
Platform hintsPython, Sports Analytics, Claude CodeRust, Sports Analytics, Claude CodePython, Data Analysis, Claude CodePython, Data Analysis, Claude Code
WarningsNo OpenAgentSkill engagement data yetNo OpenAgentSkill engagement data yetNo OpenAgentSkill engagement data yetNo OpenAgentSkill engagement data yet
Best forSports analytics workflows · Claude Code teams · teams that value GitHub adoption signalsFinance and quant workflows · Claude Code teams · teams that value GitHub adoption signalsData analysis workflows · Claude Code teams · teams that value GitHub adoption signalsGitHub automation workflows · Claude Code teams · teams that value GitHub adoption signals
Not ideal forteams that need a vendor-supported SLA · high-compliance environments without internal security reviewteams that need a vendor-supported SLA · high-compliance environments without internal security reviewteams that need a vendor-supported SLA · high-compliance environments without internal security reviewteams that need a vendor-supported SLA · high-compliance environments without internal security review
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Install
$ npx skills add pnxenopoulos/awpy
$ npx skills add nautechsystems/nautilus_trader
$ npx skills add pandas-dev/pandas
$ npx skills add scikit-learn/scikit-learn