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