M

manim-video

by @affaan-mv
4.4(5)

使用 Manim 自动生成数学和技术讲解动画,将抽象概念、算法流程和系统架构转化为清晰的可视化视频

video-editingvideo-productioncontent-creationautomationGitHub
安装方式
npx skills add https://github.com/affaan-m/everything-claude-code --skill manim-video
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Before / After 效果对比

1 组
使用前

手动绘制数学图形和动画,需要学习复杂的动画软件,调整时间轴和关键帧,一个 5 分钟的技术讲解视频需要 2-3 天完成

使用后

用代码描述数学概念和运动规律,Manim 自动渲染高清动画,快速生成精确的几何变换和数据可视化,半天完成 5 分钟视频

SKILL.md

manim-video

Manim Video

Use Manim for technical explainers where motion, structure, and clarity matter more than photorealism.

When to Activate

  • the user wants a technical explainer animation

  • the concept is a graph, workflow, architecture, metric progression, or system diagram

  • the user wants a short product or launch explainer for X or a landing page

  • the visual should feel precise instead of generically cinematic

Tool Requirements

  • manim CLI for scene rendering

  • ffmpeg for post-processing if needed

  • video-editing for final assembly or polish

  • remotion-video-creation when the final package needs composited UI, captions, or additional motion layers

Default Output

  • short 16:9 MP4

  • one thumbnail or poster frame

  • storyboard plus scene plan

Workflow

  • Define the core visual thesis in one sentence.

  • Break the concept into 3 to 6 scenes.

  • Decide what each scene proves.

  • Write the scene outline before writing Manim code.

  • Render the smallest working version first.

  • Tighten typography, spacing, color, and pacing after the render works.

  • Hand off to the wider video stack only if it adds value.

Scene Planning Rules

  • each scene should prove one thing

  • avoid overstuffed diagrams

  • prefer progressive reveal over full-screen clutter

  • use motion to explain state change, not just to keep the screen busy

  • title cards should be short and loaded with meaning

Network Graph Default

For social-graph and network-optimization explainers:

  • show the current graph before showing the optimized graph

  • distinguish low-signal follow clutter from high-signal bridges

  • highlight warm-path nodes and target clusters

  • if useful, add a final scene showing the self-improvement lineage that informed the skill

Render Conventions

  • default to 16:9 landscape unless the user asks for vertical

  • start with a low-quality smoke test render

  • only push to higher quality after composition and timing are stable

  • export one clean thumbnail frame that reads at social size

Reusable Starter

Use assets/network_graph_scene.py as a starting point for network-graph explainers.

Example smoke test:

manim -ql assets/network_graph_scene.py NetworkGraphExplainer

Output Format

Return:

  • core visual thesis

  • storyboard

  • scene outline

  • render plan

  • any follow-on polish recommendations

Related Skills

  • video-editing for final polish

  • remotion-video-creation for motion-heavy post-processing or compositing

  • content-engine when the animation is part of a broader launch

Weekly Installs519Repositoryaffaan-m/everyt…ude-codeGitHub Stars152.8KFirst Seen11 days agoSecurity AuditsGen Agent Trust HubPassSocketPassSnykPassInstalled oncodex483opencode460antigravity455gemini-cli455cursor455kimi-cli454

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统计数据

安装量4.5K
评分4.4 / 5.0
版本
更新日期2026年8月4日
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4.4(5)
5
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4
20%
3
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1
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🔧Claude Code

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创建2026年4月14日
最后更新2026年8月4日
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