Manim video

Skill mturac/everything-openai-codex/skills/manim-video

EOC: open-source operating system for OpenAI Codex workflows with agents, skills, hooks, rules, memory, safety gates, and cross-harness adapters.

Install

npx -y skills add mturac/everything-openai-codex --skill manim-video

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What its author says it does

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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.

SKILL.md

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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