manim-video
Manimを使用して数学的および技術的な解説アニメーションを自動生成し、抽象的な概念、アルゴリズムフロー、システムアーキテクチャを明確な視覚的ビデオに変換します。
npx skills add https://github.com/affaan-m/everything-claude-code --skill manim-videoBefore / After 効果比較
1 组数学的な図形やアニメーションを手動で描画するには、複雑なアニメーションソフトウェアを習得し、タイムラインやキーフレームを調整する必要があります。5分間の技術解説動画の完成には2〜3日かかります。
コードで数学的な概念や運動法則を記述することで、Manimが自動的に高精細なアニメーションをレンダリングし、正確な幾何学的変換やデータ視覚化を迅速に生成します。5分間の動画を半日で完成させることができます。
manim-video
Manim Video
Use Manim for technical explainers where motion, structure, and clarity matter more than photorealism.
When to Activate
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the user wants a technical explainer animation
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the concept is a graph, workflow, architecture, metric progression, or system diagram
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the user wants a short product or launch explainer for X or a landing page
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the visual should feel precise instead of generically cinematic
Tool Requirements
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manimCLI for scene rendering -
ffmpegfor post-processing if needed -
video-editingfor final assembly or polish -
remotion-video-creationwhen the final package needs composited UI, captions, or additional motion layers
Default Output
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short 16:9 MP4
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one thumbnail or poster frame
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storyboard plus scene plan
Workflow
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Define the core visual thesis in one sentence.
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Break the concept into 3 to 6 scenes.
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Decide what each scene proves.
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Write the scene outline before writing Manim code.
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Render the smallest working version first.
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Tighten typography, spacing, color, and pacing after the render works.
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Hand off to the wider video stack only if it adds value.
Scene Planning Rules
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each scene should prove one thing
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avoid overstuffed diagrams
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prefer progressive reveal over full-screen clutter
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use motion to explain state change, not just to keep the screen busy
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title cards should be short and loaded with meaning
Network Graph Default
For social-graph and network-optimization explainers:
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show the current graph before showing the optimized graph
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distinguish low-signal follow clutter from high-signal bridges
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highlight warm-path nodes and target clusters
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if useful, add a final scene showing the self-improvement lineage that informed the skill
Render Conventions
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default to 16:9 landscape unless the user asks for vertical
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start with a low-quality smoke test render
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only push to higher quality after composition and timing are stable
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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:
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core visual thesis
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storyboard
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scene outline
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render plan
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any follow-on polish recommendations
Related Skills
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video-editingfor final polish -
remotion-video-creationfor motion-heavy post-processing or compositing -
content-enginewhen 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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