by MemTensor · Agent Tool · ★ 302
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
npx skills add MemTensor/skills-vote --skill skills-vote
npx skills add MemTensor/skills-vote --skill skills-vote-local
Taken from the project's README on GitHub.
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 302 |
| Forks | 17 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 67/100 |
| Open Issues | 1 |
| Last Updated | 2026-08-05 |
| Created | 2026-04-03 |
| Platforms | python |
| Est. Tokens | ~15k |
SkillsVote Lifecycle Governance of Agent Skills: From Collection and Recommendation to Evolution Route skills just in time, learn from task execution, and evolve reusable skill libraries through attribution-grounded feedback. Powered by MemTensor <img src=
Looking for a skills-vote alternative? If you're comparing skills-vote with other agent tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
AIlice is a fully autonomous, general-purpose AI agent.
Official Agent skills of Oxylabs products
Shell and coding agent on mcp clients
The llama-cpp-agent framework is a tool designed for easy interaction with Large Language Models (LLMs). Allow
Jetpack Compose Agent Skill — AI-powered coding guidance with actual androidx/androidx source code receipts. W
Design, conduct and analyze results of AI-powered surveys and experiments. Simulate social science and market
Explore other popular agent tool tools:
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: