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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

Monolayout is the strongest overall pick here because it has a 39/100 readiness score and fits Coding agents.

Strongest overall

Monolayout

Do a manual repository review before adding this to an agent workflow.

Fastest prototype

Monolayout

Best first install candidate based on install readiness and adoption.

Freshest repo

Monolayout

Most recent maintenance signal among this shortlist.

SignalMonolayout

MonoLayout: Amodal Scene Layout from a single image

Quality
49/100
Needs review
Decision verdict
39/100
Needs manual review

Do a manual repository review before adding this to an agent workflow.

Adoption211 stars
0 installs
FreshnessAug 27, 2023
Use-case fit
Coding agentsRAG and knowledgeWorkflow automation
Stack fit
Content growth agentCoding review agent
Platform hintsPython, Computer Vision, Claude Code
WarningsRepository looks stale · No OpenAgentSkill engagement data yet
Best forCoding agents workflows · Claude Code teams · builders willing to evaluate younger projects
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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DocsAbout OpenAgentSkillAPIllms.txtOpenAPICLICreator KitX Growth KitSubmitBlogGuidesActivity
OpenAgentSkill Registry
PrivacyBuilt for agent-native discovery
Not ideal forteams that require actively maintained dependencies · production agents without a repository review
OpenAgentSkill engagement0 views
0 install copies
Install
$ npx skills add manila95/monolayout