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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.

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Decision summary

Benchm Ml is the strongest overall pick here because it has a 75/100 readiness score and fits Sports analytics.

Strongest overall

Benchm Ml

Shortlist this skill and compare it with close alternatives before production adoption.

Fastest prototype

Benchm Ml

Best first install candidate based on install readiness and adoption.

Freshest repo

Benchm Ml

Most recent maintenance signal among this shortlist.

SignalBenchm Ml

A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).

Quality
73/100
Strong
Decision verdict
75/100
Strong shortlist

Shortlist this skill and compare it with close alternatives before production adoption.

Adoption1.9K stars
0 installs
FreshnessSep 16, 2022
Use-case fit
Sports analyticsGitHub automationCoding agents
Workflow fit
Content growth agentCoding review agent
Platform hintsR, Machine Learning, Claude Code
WarningsRepository looks stale · No OpenAgentSkill engagement data yet
Best forSports analytics workflows · Claude Code teams · teams that value GitHub adoption signals
OpenAgentSkill

The skill layer for AI agents: discover, compare, audit, and install reusable capabilities across Codex, Claude Code, Cursor, and agent runtimes.

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OpenAgentSkill Registry
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Not ideal forteams that require actively maintained dependencies · production agents without a repository review
OpenAgentSkill engagement0 views
0 install copies
Install
$ npx skills add szilard/benchm-ml