affaan-m/ecc

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.

소스 보기

사용 방법

  1. 설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
  2. 필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
  3. 입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.
npx skills add https://github.com/affaan-m/ecc --skill manim-video

시작 전 확인

  • 설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
  • 별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.
원본 Skill 문서

원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.

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 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

관련 작업

같은 저장소의 Skills

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