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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

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Decision summary

No Reference Image Quality Assessment Using BRISQUE Model is the strongest overall pick here because it has a 39/100 readiness score and fits Coding agents.

Strongest overall

No Reference Image Quality Assessment Using BRISQUE Model

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

Fastest prototype

No Reference Image Quality Assessment Using BRISQUE Model

Best first install candidate based on install readiness and adoption.

Freshest repo

No Reference Image Quality Assessment Using BRISQUE Model

Most recent maintenance signal among this shortlist.

SignalNo Reference Image Quality Assessment Using BRISQUE Model

Implementation of the paper "No Reference Image Quality Assessment in the Spatial Domain" by A Mittal et al. in OpenCV (using both C++ and Python)

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

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

Adoption210 stars
0 installs
FreshnessSep 27, 2021
Use-case fit
Coding agentsRAG and knowledgeWorkflow automation
Workflow fit
Content growth agentCoding review agent
Platform hintsC++, Computer Vision, Claude Code
Warnings
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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Repository looks stale · No OpenAgentSkill engagement data yet
Best forCoding agents workflows · Claude Code teams · builders willing to evaluate younger projects
Not ideal forteams that require actively maintained dependencies · production agents without a repository review
OpenAgentSkill engagement0 views
0 install copies
Install
$ npx skills add krshrimali/No-Reference-Image-Quality-Assessment-using-BRISQUE-Model