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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

Machine Learning Systems Design is the strongest overall pick here because it has a 79/100 readiness score and fits GitHub automation.

Strongest overall

Machine Learning Systems Design

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Fastest prototype

Machine Learning Systems Design

Best first install candidate based on install readiness and adoption.

Freshest repo

Machine Learning Systems Design

Most recent maintenance signal among this shortlist.

SignalMachine Learning Systems Design

A booklet on machine learning systems design with exercises. NOT the repo for the book "Designing Machine Learning Systems", which is `dmls-book`

Quality
77/100
Strong
Decision verdict
79/100
Strong shortlist

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

Adoption10K stars
0 installs
FreshnessApr 15, 2023
Use-case fit
GitHub automationCoding agentsRAG and knowledge
Stack fit
Content growth agentCoding review agent
Platform hintsHTML, MLOps, Claude Code
WarningsRepository looks stale · No OpenAgentSkill engagement data yet
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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PrivacyBuilt for agent-native discovery
Best forGitHub automation workflows · Claude Code teams · teams that value GitHub adoption signals
Not ideal forteams that require actively maintained dependencies · production agents without a repository review
OpenAgentSkill engagement0 views
0 install copies
Install
$ npx skills add chiphuyen/machine-learning-systems-design