manim-video

Build reusable Manim explainers for technical concepts, graphs, system diagrams, and product walkthroughs, then hand off to the wider ECC video stack if needed. Use when the user wants a clean animated explainer rather than a generic talking-head script.

144,923 stars
Complexity: medium

About this skill

This skill empowers an AI agent to produce high-quality, animated technical explainers using Manim, a powerful Python library renowned for creating precise mathematical and scientific animations. It is ideal for scenarios where clarity, motion, and structural accuracy are critical for explaining complex ideas, serving as a superior alternative to static visuals or generic 'talking-head' scripts. The skill can expertly visualize diverse content, including intricate technical concepts, dynamic data graphs, system architectures, workflow diagrams, metric progressions, and even concise product or launch explainers. The generated video output is production-ready and can be seamlessly integrated into broader video production workflows, particularly within the ECC video stack, if further processing is required.

Best use case

Creating animated visualizations for complex technical concepts or algorithms. Generating dynamic explanations of mathematical graphs, data trends, or scientific principles. Illustrating system architectures, software workflows, or process diagrams with clarity. Producing engaging, short product walkthroughs or launch explainers for marketing or educational purposes. Replacing static diagrams or text-heavy explanations with precise, animated visuals to enhance understanding.

Build reusable Manim explainers for technical concepts, graphs, system diagrams, and product walkthroughs, then hand off to the wider ECC video stack if needed. Use when the user wants a clean animated explainer rather than a generic talking-head script.

A high-definition video file (e.g., MP4) containing a precisely animated Manim explainer, ready for immediate use, presentation, or further integration into a video production pipeline.

Practical example

Example input

Generate a Manim animation to explain the quicksort algorithm, showing the partitioning process and recursive calls on subarrays. Emphasize the pivot selection and element swapping.

Example output

Here is an MP4 video file demonstrating the quicksort algorithm. The animation visually breaks down the partitioning process, highlights pivot selections, and clearly illustrates element swaps and recursive calls on subarrays, providing a step-by-step understanding of the algorithm's execution.

When to use this skill

  • The user explicitly requests a technical explainer animation.
  • The concept to be explained involves a graph, workflow, architecture, metric progression, or system diagram.
  • A short product or launch explainer is needed for a website, presentation, or landing page.
  • The desired visual output should be precise, structured, and clear, rather than generically cinematic or photorealistic.

When not to use this skill

  • The user requires photorealistic video footage or live-action recording.
  • The primary goal is a 'talking-head' style video script without animation.
  • The explanation is best served by a very simple image or text, and animation would be overkill.
  • Complex 3D rendering or highly artistic, non-technical animations are required.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/manim-video/SKILL.md --create-dirs "https://raw.githubusercontent.com/affaan-m/everything-claude-code/main/skills/manim-video/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/manim-video/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How manim-video Compares

Feature / Agentmanim-videoStandard Approach
Platform SupportClaudeLimited / Varies
Context Awareness High Baseline
Installation ComplexitymediumN/A

Frequently Asked Questions

What does this skill do?

Build reusable Manim explainers for technical concepts, graphs, system diagrams, and product walkthroughs, then hand off to the wider ECC video stack if needed. Use when the user wants a clean animated explainer rather than a generic talking-head script.

Which AI agents support this skill?

This skill is designed for Claude.

How difficult is it to install?

The installation complexity is rated as medium. You can find the installation instructions above.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# 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

1. Define the core visual thesis in one sentence.
2. Break the concept into 3 to 6 scenes.
3. Decide what each scene proves.
4. Write the scene outline before writing Manim code.
5. Render the smallest working version first.
6. Tighten typography, spacing, color, and pacing after the render works.
7. 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](assets/network_graph_scene.py) as a starting point for network-graph explainers.

Example smoke test:

```bash
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

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