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skills-vote AI Agent Skill
View Source: memtensor/skills-vote
MediumInstallation
npx skills add memtensor/skills-vote --skill skills-vote 200
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SkillsVote
🧠 The Next-Gen Agent-Native Skill Recommendation Engine
Empowering AI agents with just-in-time, dynamically routed skills.
Powered by MemTensor
🌟 Why SkillsVote?
Say goodbye to massive, hardcoded, and bloated skill lists! SkillsVote is building an intelligent, dynamic ecosystem for skill recommendation, feedback, and long-term skill evolution.
Acting as a smart gateway, SkillsVote delivers just-in-time recommendations, dynamically routing your AI agents to the exact skills they need. The result? ⚡ Maximized token efficiency and 🎯 sky-high task success rates.
🌍 The World's Largest Skill Library
At the product level, we are mining the vast open-source universe of GitHub to build an unprecedented library:
- 🔥 1.68M+ discovered
SKILL.mdfiles - 💎 790K+ format-valid skills after validation
💡 About This Repository
This is the open-source, local-first core of SkillsVote. It equips you with:
- a powerful static analysis pipeline for skill profiling, and
- a smart local agentic recommendation pipeline.
📰 Latest News
- 🌅 [2026-04-09] Special Share. Core contributor's share on Linux.do.
- 📣 [2026-04-08] Social Launch. Our launch announcement is now live on WeChat Blog and rednote.
- 🚀 [2026-04-03] Launch Day! Published the very first open-source release of our recommendation and evaluation demos.
✨ Key Features
- 🔍 Rich Skill Profiling. SkillsVote doesn't just read skills; it understands them. We build a structured, comprehensive profile for every skill—covering OS requirements, env variables, CLI needs, and MCP dependencies. This makes browsing our website a breeze while ensuring strict quality control.
- 🏗️ Real Task Construction & Execution. We go far beyond static inspection! Skills that pass our verifiability screening are put to the test: we construct executable tasks, controlled sandboxes, and strict validators to prove that a skill actually helps agents get real work done.
- 🧠 Agentic Recommendation Engine. Given a user task and a local skill directory, our agentic navigation searches your directory and returns the perfect recommended skill set, complete with foolproof usage guidance.
For a detailed breakdown of the quality and verifiability criteria used in our evaluation pipeline, see Appendix: Evaluation Metrics Unpacked.
🚀 Quick Start
Option 1: Install the Hosted Skill (Recommended)
🤖 Agent Setup Prompt
Supercharge your agents (Codex, Claude Code, OpenClaw) by integrating SkillsVote directly! Just drop this prompt into your agent:
1. Install the skill by running `npx skills add MemTensor/skills-vote --skill skills-vote`
2. Create or update `.env` file located in the root directory of the installed `skills-vote` skill and set `SKILLS_VOTE_API_KEY="YOUR_API_KEY"`
Do not configure this as a system or user-level environment variable unless explicitly requested.🔧 Manual Setup Alternative
Are you a CLI warrior? Set it up manually based on your OS:
Windows PowerShell
[Environment]::SetEnvironmentVariable("SKILLS_VOTE_API_KEY", "YOUR_API_KEY", "User")
npx skills add MemTensor/skills-vote --skill skills-voteMacOS/linux (Bash/Zsh)
# For zsh, use ~/.zshrc instead
echo 'export SKILLS_VOTE_API_KEY="YOUR_API_KEY"' >> ~/.bashrc && source ~/.bashrc
npx skills add MemTensor/skills-vote --skill skills-vote[!note]
Don't forget to replaceYOUR_API_KEYwith your actual key!
Option 2: Run the Local Demo 🏠
Want to test drive the core engine locally? Just follow these 3 easy steps:
1. Install dependencies
uv sync2. Configure Environment
Copy the example config and fill in your Anthropic credentials.
cp .env.example .envUse ANTHROPIC_API_KEY when calling the official Anthropic API. Use ANTHROPIC_AUTH_TOKEN and ANTHROPIC_BASE_URL when calling a third-party Anthropic-compatible service.
3. Run the examples
bash examples/evaluate.sh
bash examples/recommend.shOutputs are written to output/evaluate_results.jsonl and output/recommend_result.json.
You can override the query with:
bash examples/recommend.sh -q "Summarize a pull request and highlight risky changes"If you want to use your own local skills, update skills_dir in scripts/configs/recommend.yaml and scripts/configs/evaluate.yaml, then rerun the same commands.
📎 Appendix
📊 Evaluation Metrics Unpacked
Table 1. Quality Evaluation
| Metric | Description | Why it matters |
|---|---|---|
| Content Consistency | Whether the skill stays centered on one clear, stable purpose and whether the rest of the content consistently supports that purpose. | A recommended skill should be a stable capability unit, not a mixed bundle of unrelated topics. |
| Reference Completeness | Whether the referenced scripts, resources, templates, and dependencies are present and usable as documented. | Broken references and missing artifacts are one of the most common failure modes in open-source skill libraries. |
| Task Orientation | Whether the skill provides actionable guidance for completing work rather than only background information. | SkillsVote is recommending executable skills, not just retrieving knowledge. |
Table 2. Verifiability Evaluation
| Metric | Description | Why it matters |
|---|---|---|
| Success Verifiability | Whether results can be judged programmatically with low ambiguity. | Subjective skills such as brainstorming or poetry writing are not suitable for automatic validation. |
| Environment Controllability | Whether the required environment can be reproduced, reset, and executed reliably in a controlled sandbox. | Skills that depend on live external systems or open-world state are hard to benchmark deterministically. |
| Task Constructability | Whether many realistic task instances and validators can be generated at reasonable cost. | Some domains require expensive hardware, large datasets, or heavy manual work and do not scale well for evaluation. |
📄 License
This repository is licensed under the MIT License. See LICENSE.
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memtensor/skills-vote
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How to use this skill
Install skills-vote by running npx skills add memtensor/skills-vote --skill skills-vote in your project directory. Run the install command above in your project directory. The skill file will be downloaded from GitHub and placed in your project.
No configuration needed. Your AI agent (Claude Code, Cursor, Windsurf, etc.) automatically detects installed skills and uses them as context when generating code.
The skill enhances your agent's understanding of skills-vote, helping it follow established patterns, avoid common mistakes, and produce production-ready output.
What you get
Skills are plain-text instruction files — not executable code. They encode expert knowledge about frameworks, languages, or tools that your AI agent reads to improve its output. This means zero runtime overhead, no dependency conflicts, and full transparency: you can read and review every instruction before installing.
Compatibility
This skill works with any AI coding agent that supports the skills.sh format, including Claude Code (Anthropic), Cursor, Windsurf, Cline, Aider, and other tools that read project-level context files. Skills are framework-agnostic at the transport level — the content inside determines which language or framework it applies to.
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