Flagged: sudo usage. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by adithya-s-k · Agent Tool · ★ 1.1k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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Manim Skills Repository ⚡ Quick Start: Add both Manim skills to your AI agent instantly: A comprehensive collection of best practices, patterns, and examples for both Manim Community Edition and ManimGL (3Blue1Brown's version). This repository provides battle-tested code examples and guidelines for creating mathematical animations. https://github.com/user-attachments/assets/3cd398b7-7cc6-43c1-a6e9-20077be6b009 📚 About the Two Versions Manim Community Edition () Repository: https://github.com/ManimCommunity/manim Focus: Community-maintained, stable, well-documented Best For: Production use, educational content, collaborative projects Command: CLI Import: ManimGL () Repository: https://github.com/3b1b/manim Focus: Grant Sanderson's (3Blue1Brown) original version with OpenGL rendering Best For: Interactive development, 3D scenes, rapid proto
| Stars | 1,109 |
| Forks | 93 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 69.1701297574863/100 |
| Open Issues | 2 |
| Last Updated | 2026-01-23 |
| Created | 2026-01-22 |
| Platforms | python |
| Est. Tokens | ~16k |
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manim_skill is Agent skills for Manim to create 3Blue1Brown style animations.. It is categorized as a Agent Tool with 1.1k GitHub stars.
manim_skill is primarily written in Python. It covers topics such as agent-skills.
You can find installation instructions and usage details in the manim_skill GitHub repository at github.com/adithya-s-k/manim_skill. The project has 1.1k stars and 93 forks, indicating an active community.
manim_skill is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to manim_skill on Agent Skills Hub include Generative-Media-Skills, skills, playwright-skill. 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: