MemTensor

MemTensor / skills-vote

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The Next-Gen Agent-Native Skill Recommendation Engine

skills.vote agent-skill agent-skills llm llm-agent
17
2
89% credibility
Found Apr 08, 2026 at 17 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

SkillsVote is a recommendation engine that helps AI agents discover, evaluate, and use relevant skills from a massive open-source library for specific user tasks.

How It Works

1
🔍 Discover SkillsVote

You hear about SkillsVote, a helpful guide that finds the perfect skills for your AI assistant to tackle any task.

2
🚀 Add to Your AI Assistant

You easily add SkillsVote as a special helper inside your AI tool, like Claude or Codex, with a simple command.

3
🔑 Unlock with Your Passcode

You enter your personal passcode to connect to the huge skill library and start getting smart suggestions.

4
💭 Describe Your Task

When your AI needs help with a job, like summarizing news or fixing code, it asks SkillsVote what skills to use.

5
⭐ Get Perfect Skill Matches

SkillsVote quickly suggests the best matching skills from millions available, with tips on how to use them.

6
📥 Download and Try Skills

The recommended skills download automatically, and your AI puts them to work right away.

7
👍 Share How It Went

After finishing, you tell SkillsVote what worked well to improve future suggestions.

🎉 Smarter AI Every Time

Your AI assistant now handles complex tasks effortlessly with an ever-growing set of perfect skills.

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Star Growth

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AI-Generated Review

What is skills-vote?

Skills-vote is a Python-based, agent-native recommendation engine that dynamically suggests skills to AI agents from a massive GitHub-mined library of over 1.68M SKILL.md files. It solves the bloat of hardcoded skill lists by delivering just-in-time recommendations, complete with profiles on OS needs, dependencies, and usage guidance, boosting token efficiency and task success. Users get a hosted API at skills.vote for instant access or a local pipeline to evaluate and recommend from custom skill directories.

Why is it gaining traction?

It stands out with agentic search over local skills dirs and rigorous evaluation for quality, verifiability, and environment fit—far beyond simple keyword matching. Developers hook into it via npx skills add for tools like Claude Code or Codex, auto-downloading repos with feedback loops for skill evolution. The next-gen focus on vote canvassing skills and voter skills via skills in poll vote makes it a fresh take on next gen GitHub ecosystems like apsim next gen github or crowdstrike next gen siem github.

Who should use this?

AI agent builders integrating skills into workflows for Claude, Codex, or OpenClaw. Devs curating local skill libraries for reproducible agent tasks, like evaluating emby next gen github tools or uad next gen github plugins. Teams needing dynamic routing for gta vice city next gen github mods or vote canvassing skills in agent-native setups.

Verdict

Grab it if you're prototyping agent skills—solid local demos and MIT license make experimentation easy, despite 17 stars signaling early maturity. The 0.8999999761581421% credibility score reflects promise in a nascent space, but watch for broader adoption.

(198 words)

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