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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

Torch Conv Kan is the strongest overall pick here because it has a 55/100 readiness score and fits Coding agents.

Strongest overall

Torch Conv Kan

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Fastest prototype

Torch Conv Kan

Best first install candidate based on install readiness and adoption.

Freshest repo

Torch Conv Kan

Most recent maintenance signal among this shortlist.

SignalTorch Conv Kan

This project is dedicated to the implementation and research of Kolmogorov-Arnold convolutional networks. The repository includes implementations of 1D, 2D, and 3D convolutions with different kernels, ResNet-like and DenseNet-like models, training code based on accelerate/PyTorch, as well as scripts for experiments with CIFAR-10 and Tiny ImageNet.

Quality
53/100
Needs review
Decision verdict
55/100
Needs manual review

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

Adoption530 stars
0 installs
FreshnessNov 19, 2024
Use-case fit
Coding agentsGitHub automationRAG and knowledge
Stack fit
Coding review agentRAG knowledge base
Platform hintsPython, Computer Vision, Claude Code
WarningsRepository looks stale · No OpenAgentSkill engagement data yet
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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
PrivacyBuilt for agent-native discovery
Coding agents 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 IvanDrokin/torch-conv-kan