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Manim Video

OrganizationPopular
browser-use
manim-video

Production pipeline for mathematical and technical animations using Manim Community Edition. Creates 3Blue1Brown-style explainer videos, algorithm visualizations, equation derivations, architecture diagrams, and data stories. Use when users request: animated explanations, math animations, concept visualizations, algorithm walkthroughs, technical explainers, 3Blue1Brown style videos, or any programmatic animation with geometric/mathematical content.

Overview

Publisherbrowser-use
Repositoryvideo-use
Skill namemanim-video
Stars
25.1K
Forks
3K
Bundled files
15
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 15 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by browser-use on GitHub. Read the source before you install it.

Installation

Install the Manim Video AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/browser-use/video-use.git /tmp/video-use
mkdir -p .claude/skills
cp -r /tmp/video-use/skills/manim-video .claude/skills/manim-video
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Manim Video in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Manim Video on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Manim Video is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Manim Video Production Pipeline

Creative Standard

This is educational cinema. Every frame teaches. Every animation reveals structure.

Before writing a single line of code, articulate the narrative arc. What misconception does this correct? What is the "aha moment"? What visual story takes the viewer from confusion to understanding? The user's prompt is a starting point — interpret it with pedagogical ambition.

Geometry before algebra. Show the shape first, the equation second. Visual memory encodes faster than symbolic memory. When the viewer sees the geometric pattern before the formula, the equation feels earned.

First-render excellence is non-negotiable. The output must be visually clear and aesthetically cohesive without revision rounds. If something looks cluttered, poorly timed, or like "AI-generated slides," it is wrong.

Opacity layering directs attention. Never show everything at full brightness. Primary elements at 1.0, contextual elements at 0.4, structural elements (axes, grids) at 0.15. The brain processes visual salience in layers.

Breathing room. Every animation needs self.wait() after it. The viewer needs time to absorb what just appeared. Never rush from one animation to the next. A 2-second pause after a key reveal is never wasted.

Cohesive visual language. All scenes share a color palette, consistent typography sizing, matching animation speeds. A technically correct video where every scene uses random different colors is an aesthetic failure.

Prerequisites

Run scripts/setup.sh to verify all dependencies. Requires: Python 3.10+, Manim Community Edition v0.20+ (pip install manim), LaTeX (texlive-full on Linux, mactex on macOS), and ffmpeg. Reference docs tested against Manim CE v0.20.1.

Modes

ModeInputOutputReference
Concept explainerTopic/conceptAnimated explanation with geometric intuitionreferences/scene-planning.md
Equation derivationMath expressionsStep-by-step animated proofreferences/equations.md
Algorithm visualizationAlgorithm descriptionStep-by-step execution with data structuresreferences/graphs-and-data.md
Data storyData/metricsAnimated charts, comparisons, countersreferences/graphs-and-data.md
Architecture diagramSystem descriptionComponents building up with connectionsreferences/mobjects.md
Paper explainerResearch paperKey findings and methods animatedreferences/scene-planning.md
3D visualization3D conceptRotating surfaces, parametric curves, spatial geometryreferences/camera-and-3d.md

Stack

Single Python script per project. No browser, no Node.js, no GPU required.

LayerToolPurpose
CoreManim Community EditionScene rendering, animation engine
MathLaTeX (texlive/MiKTeX)Equation rendering via MathTex
Video I/OffmpegScene stitching, format conversion, audio muxing
TTSElevenLabs / Qwen3-TTS (optional)Narration voiceover

Pipeline

PLAN --> CODE --> RENDER --> STITCH --> AUDIO (optional) --> REVIEW
  1. PLAN — Write plan.md with narrative arc, scene list, visual elements, color palette, voiceover script
  2. CODE — Write script.py with one class per scene, each independently renderable
  3. RENDERmanim -ql script.py Scene1 Scene2 ... for draft, -qh for production
  4. STITCH — ffmpeg concat of scene clips into final.mp4
  5. AUDIO (optional) — Add voiceover and/or background music via ffmpeg. See references/rendering.md
  6. REVIEW — Render preview stills, verify against plan, adjust

Project Structure

project-name/
  plan.md                # Narrative arc, scene breakdown
  script.py              # All scenes in one file
  concat.txt             # ffmpeg scene list
  final.mp4              # Stitched output
  media/                 # Auto-generated by Manim
    videos/script/480p15/

Creative Direction

Color Palettes

PaletteBackgroundPrimarySecondaryAccentUse case
Classic 3B1B#1C1C1C#58C4DD (BLUE)#83C167 (GREEN)#FFFF00 (YELLOW)General math/CS
Warm academic#2D2B55#FF6B6B#FFD93D#6BCB77Approachable
Neon tech#0A0A0A#00F5FF#FF00FF#39FF14Systems, architecture
Monochrome#1A1A2E#EAEAEA#888888#FFFFFFMinimalist

Animation Speed

Contextrun_timeself.wait() after
Title/intro appear1.5s1.0s
Key equation reveal2.0s2.0s
Transform/morph1.5s1.5s
Supporting label0.8s0.5s
FadeOut cleanup0.5s0.3s
"Aha moment" reveal2.5s3.0s

Typography Scale

RoleFont sizeUsage
Title48Scene titles, opening text
Heading36Section headers within a scene
Body30Explanatory text
Label24Annotations, axis labels
Caption20Subtitles, fine print

Fonts

Use monospace fonts for all text. Manim's Pango renderer produces broken kerning with proportional fonts at all sizes. See references/visual-design.md for full recommendations.

python
MONO = "Menlo"  # define once at top of file

Text("Fourier Series", font_size=48, font=MONO, weight=BOLD)  # titles
Text("n=1: sin(x)", font_size=20, font=MONO)                  # labels
MathTex(r"\nabla L")                                            # math (uses LaTeX)

Minimum font_size=18 for readability.

Per-Scene Variation

Never use identical config for all scenes. For each scene:

  • Different dominant color from the palette
  • Different layout — don't always center everything
  • Different animation entry — vary between Write, FadeIn, GrowFromCenter, Create
  • Different visual weight — some scenes dense, others sparse

Workflow

Step 1: Plan (plan.md)

Before any code, write plan.md. See references/scene-planning.md for the comprehensive template.

Step 2: Code (script.py)

One class per scene. Every scene is independently renderable.

python
from manim import *

BG = "#1C1C1C"
PRIMARY = "#58C4DD"
SECONDARY = "#83C167"
ACCENT = "#FFFF00"
MONO = "Menlo"

class Scene1_Introduction(Scene):
    def construct(self):
        self.camera.background_color = BG
        title = Text("Why Does This Work?", font_size=48, color=PRIMARY, weight=BOLD, font=MONO)
        self.add_subcaption("Why does this work?", duration=2)
        self.play(Write(title), run_time=1.5)
        self.wait(1.0)
        self.play(FadeOut(title), run_time=0.5)

Key patterns:

  • Subtitles on every animation: self.add_subcaption("text", duration=N) or subcaption="text" on self.play()
  • Shared color constants at file top for cross-scene consistency
  • self.camera.background_color set in every scene
  • Clean exits — FadeOut all mobjects at scene end: self.play(FadeOut(Group(*self.mobjects)))

Step 3: Render

bash
manim -ql script.py Scene1_Introduction Scene2_CoreConcept  # draft
manim -qh script.py Scene1_Introduction Scene2_CoreConcept  # production

Step 4: Stitch

bash
cat > concat.txt << 'EOF'
file 'media/videos/script/480p15/Scene1_Introduction.mp4'
file 'media/videos/script/480p15/Scene2_CoreConcept.mp4'
EOF
ffmpeg -y -f concat -safe 0 -i concat.txt -c copy final.mp4

Step 5: Review

bash
manim -ql --format=png -s script.py Scene2_CoreConcept  # preview still

Critical Implementation Notes

Raw Strings for LaTeX

python
# WRONG: MathTex("\frac{1}{2}")
# RIGHT:
MathTex(r"\frac{1}{2}")

buff >= 0.5 for Edge Text

python
label.to_edge(DOWN, buff=0.5)  # never < 0.5

FadeOut Before Replacing Text

python
self.play(ReplacementTransform(note1, note2))  # not Write(note2) on top

Never Animate Non-Added Mobjects

python
self.play(Create(circle))  # must add first
self.play(circle.animate.set_color(RED))  # then animate

Performance Targets

QualityResolutionFPSSpeed
-ql (draft)854x480155-15s/scene
-qm (medium)1280x7203015-60s/scene
-qh (production)1920x10806030-120s/scene

Always iterate at -ql. Only render -qh for final output.

References

FileContents
references/animations.mdCore animations, rate functions, composition, .animate syntax, timing patterns
references/mobjects.mdText, shapes, VGroup/Group, positioning, styling, custom mobjects
references/visual-design.md12 design principles, opacity layering, layout templates, color palettes
references/equations.mdLaTeX in Manim, TransformMatchingTex, derivation patterns
references/graphs-and-data.mdAxes, plotting, BarChart, animated data, algorithm visualization
references/camera-and-3d.mdMovingCameraScene, ThreeDScene, 3D surfaces, camera control
references/scene-planning.mdNarrative arcs, layout templates, scene transitions, planning template
references/rendering.mdCLI reference, quality presets, ffmpeg, voiceover workflow, GIF export
references/troubleshooting.mdLaTeX errors, animation errors, common mistakes, debugging
references/animation-design-thinking.mdWhen to animate vs show static, decomposition, pacing, narration sync
references/updaters-and-trackers.mdValueTracker, add_updater, always_redraw, time-based updaters, patterns
references/paper-explainer.mdTurning research papers into animations — workflow, templates, domain patterns
references/decorations.mdSurroundingRectangle, Brace, arrows, DashedLine, Angle, annotation lifecycle
references/production-quality.mdPre-code, pre-render, post-render checklists, spatial layout, color, tempo

Creative Divergence (use only when user requests experimental/creative/unique output)

If the user asks for creative, experimental, or unconventional explanatory approaches, select a strategy and reason through it BEFORE designing the animation.

  • SCAMPER — when the user wants a fresh take on a standard explanation
  • Assumption Reversal — when the user wants to challenge how something is typically taught

SCAMPER Transformation

Take a standard mathematical/technical visualization and transform it:

  • Substitute: replace the standard visual metaphor (number line → winding path, matrix → city grid)
  • Combine: merge two explanation approaches (algebraic + geometric simultaneously)
  • Reverse: derive backward — start from the result and deconstruct to axioms
  • Modify: exaggerate a parameter to show why it matters (10x the learning rate, 1000x the sample size)
  • Eliminate: remove all notation — explain purely through animation and spatial relationships

Assumption Reversal

  1. List what's "standard" about how this topic is visualized (left-to-right, 2D, discrete steps, formal notation)
  2. Pick the most fundamental assumption
  3. Reverse it (right-to-left derivation, 3D embedding of a 2D concept, continuous morphing instead of steps, zero notation)
  4. Explore what the reversal reveals that the standard approach hides

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Manim Video AI skill do?

Production pipeline for mathematical and technical animations using Manim Community Edition. Creates 3Blue1Brown-style explainer videos, algorithm visualizations, equation derivations, architecture diagrams, and data stories. Use when users request: animated explanations, math animations, concept visualizations, algorithm walkthroughs, technical explainers, 3Blue1Brown style videos, or any programmatic animation with geometric/mathematical content.

Why use Manim Video on TypingMind?

Because you install it once and use it with any model. Manim Video is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Manim Video in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/browser-use/video-use/tree/main/skills/manim-video. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Manim Video?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Manim Video?

As many as you like. As long as a model supports skills, you can use Manim Video with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Manim Video AI skill free?

Yes. It is published on GitHub by browser-use under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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