No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by telekinesis-ai · Agent Tool · ★ 81
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is telekinesis-examples safe to install? View the security audit →
Telekinesis Docs • Discord • LinkedIn • X • Website Telekinesis Agentic OS Telekinesis is the Agentic OS for general-purpose robots. It provides a unified Python library that brings the entire Physical AI stack together: Vision-Language-Action models, World Models, perception, planning, hardware interfaces, and more. This repository contains standalone, chainable Python examples of those Skills. Full documentation: [docs.telekinesis.ai](https://docs.tele
| Stars | 81 |
| Forks | 5 |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 61.7260150279927/100 |
| Last Updated | 2026-09-17 |
| Created | 2025-12-19 |
| Est. Tokens | ~16k |
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telekinesis-examples is Telekinesis Agentic Skill Library: build AI-powered Computer Vision, Robotics and Physical AI applications.. It is categorized as a Agent Tool with 81 GitHub stars.
You can find installation instructions and usage details in the telekinesis-examples GitHub repository at github.com/telekinesis-ai/telekinesis-examples. The project has 81 stars and 5 forks, indicating an active community.
telekinesis-examples is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to telekinesis-examples on Agent Skills Hub include MinerU-Skill, Context-Engine, alice. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. 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: