manim_skill: Agent skills for Manim to create 3Blue1Brown style animations

by adithya-s-k · Agent Tool · ★ 1.3k

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Featured in: Claude Video Editing & Remotion Skills

How to install manim_skill

npx skills add adithya-s-k/manim_skill/skills/manimce-best-practices
npx skills add adithya-s-k/manim_skill/skills/manimgl-best-practices

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 69/100

Flagged: sudo usage. Scanned against 43 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

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.

agent-skills

Quick Facts

Stars1,283
Forks104
LanguagePython
CategoryAgent Tool
LicenseMIT
Quality Score69/100
Open Issues3
Last Updated2026-01-23
Created2026-01-22
Platformspython
Est. Tokens~16k

About manim_skill

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.

Compatible Skills

These tools work well together with manim_skill for enhanced workflows:

  • agent-skills — semantic(0.42)+complementary+same_lang+similar_pop+shared_platform (65%)
  • skills-agent-proto — semantic(0.36)+complementary+same_lang+similar_pop+shared_platform (63%)
  • huashu-skills — semantic(0.33)+complementary+same_lang+similar_pop+shared_platform (62%)
  • ai-assistant-workspace — semantic(0.69)+same_lang+similar_pop+shared_platform (59%)
  • awesome-skills — semantic(0.26)+complementary+same_lang+similar_pop+shared_platform (59%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 43 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:

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