Field manual

Field manual

Opus 5.5 Code-driven animation

Analysis of yihui-dev/awesome-opus5-5-videos: Curating 853 Claude Opus 5.5 animation prompts and 523 Skillry side-by-side remakes across Motion graphics, Explainers, 3D scenes, and interactive games.

yihui-dev/awesome-opus5-5-videos
stars
—
forks
—
License
MIT
Data as of
—
Reading time
7 min
Updated
Summary and key figures

Curated repository of 853 Claude Opus 5.5 animation prompts and 523 Skillry live remakes. Generates high frame-rate motion via Canvas, SVG, GSAP, Three.js, and GLSL code with deterministic frame-based rendering pipelines.

Total curated prompts
853prompts directory and JSON
Skillry live remakes
523Side-by-side comparisons
Core motion categories
4Motion, explainers, 3D, games
Repository license
MITOpen-source MIT License
On this page

01Use cases and architecture

System purpose

yihui-dev/awesome-opus5-5-videos is an open-source curated dataset collecting animation and video prompts generated by community creators using Claude Opus 5.5. The project demonstrates the capacity of large language models to compile natural language prompts directly into self-contained HTML, Canvas 2D, SVG, GSAP timelines, Three.js geometry, or GLSL fragment shaders, rather than relying on diffusion models to produce raster video. Official repository

Use cases:

  • Review and reuse 853 community-tested Claude Opus 5.5 animation prompts.

  • Compare 523 original community clips with live remake renders on the Skillry platform.

  • Master prompt structures and syntax boundaries for Canvas, SVG, GSAP, and Three.js under AI code generation workflows.

  • Build deterministic production pipelines spanning text prompts, standalone HTML files, headless browser frame stepping, and FFmpeg MP4 encoding.

  1. Prompt input layer

    • 853 structured prompts
    • Motion constraints and timing
    • Visual style and palette directives

  2. Code synthesis model

    • Claude Opus 5.5
    • Claude Code CLI
    • Autonomous agent runtimes

  3. Frontend motion stack

    • Canvas 2D
    • SVG / GSAP
    • Three.js / WebGL
    • GLSL Shaders
    • Web Audio

  4. Output and delivery pipeline

    • Self-contained HTML file
    • 60fps in-browser playback
    • Playwright deterministic capture
    • FFmpeg MP4 encoding

Compared to diffusion-based video generators, code-driven animation offers zero resolution compression artifacts, infinitely scalable vector paths, reversible code edits, and complete control over frame counts. The repository is released under the MIT license, pinned through 2026-10-12, containing 853 prompts and 100 featured highlight works.

02Prerequisites and setup

Installation and verification

  • Not completed:

    Git 2.30 or later with terminal environment.

  • Not completed:

    Python 3.9+ or Node.js 18+ (for local preview server hosting).

  • Not completed:

    Access to Claude Opus 5.5 (Claude Code, Anthropic Console API, or compatible agent runtime).

  • Not completed:

    (Optional) Playwright and FFmpeg installed for frame capture and MP4 video encoding.

Clone the official repository to your local environment:

bash
git clone https://github.com/yihui-dev/awesome-opus5-5-videos.git
cd awesome-opus5-5-videos

Verify prompt dataset count and directory structure:

bash
ls prompts | wc -l

Start a local static server to preview prompt files and datasets:

bash
# Start local preview server with Python
python3 -m http.server 8080

# Or start with Node.js serve
npx serve -l 8080

Distribution of animation categories and technical stacks:

CategoryTotal promptsFeatured examplesCore technology stackPrimary use cases
Motion graphics50158Canvas, SVG, GSAP, CSSBrand identity, SaaS product demos, animated infographics
3D scenes12314Three.js, WebGL, GLSLGeometric architectures, procedural landscapes, particle fields, physics
Explainers12216Canvas, SVG, GSAP, Web AudioScientific explanations, architecture walkthroughs, algorithm visualizations
Games & interactive10712Three.js, Canvas, Web AudioRetro pixel games, interactive physics sandboxes, control interface prototypes

03Technical stack and prompt architecture

Core capabilities

Motion

prompts/motion/*.md

2D motion graphics and UI animations

Uses Canvas 2D and GSAP timelines to create typography morphing, staggered element sequences, and geometric path transitions.

3D Scene

prompts/3d/*.md

Spatial geometry and shader rendering

Loads Three.js scene graphs and GLSL fragment shaders to orchestrate camera orbits, dynamic lighting, and large-scale particle systems.

Explainer

prompts/explainer/*.md

Diagrammatic architecture and audio sync

Combines SVG vector layers with Web Audio API synthesizers to advance conceptual diagrams and synthesized sound along synced cues.

Interactive

prompts/interactive/*.md

Real-time controls and game mechanics

Packages keyboard and pointer event listeners with collision detection loops and state machines to deliver playable interactive prototypes.

Prompt engineering architectural specifications:

  1. Self-Contained Single File: Prompts require the model to package HTML markup, CSS styling, JavaScript code, and external CDN dependencies (such as cdnjs imports for Three.js or GSAP) within one file, eliminating local asset loading failures.

  2. Fixed Aspect Ratio and Responsive Scaling: Standardizes canvas viewports to 16:9 (1920x1080) or 1:1 (1080x1080), calculating coordinates against base dimensions and scaling via CSS.

  3. Decoupled State Pipeline: Encapsulates render logic into a pure draw(frameIndex) function, enabling both real-time browser playback and frame-stepped headless browser capture.

04Engineering and reproducibility rules

Operational principles

Deterministic frame indexing over dynamic timestamps

Avoid Date.now() and dynamic requestAnimationFrame delta counters in render loops. Use a global frameIndex counter as the single independent state variable to guarantee identical pixels across runs.

Seeded pseudorandom generators over Math.random

Native Math.random() produces differing particle layouts and trajectories on reload. Require Opus 5.5 to implement Mulberry32 or LCG PRNG algorithms with a fixed seed to freeze visual appearance.

Pin explicit CDN library versions

Specify exact library versions in prompts (such as Three.js r128 or GSAP 3.12.5). Avoid unpkg or cdnjs latest aliases to prevent external breaking changes from deprecating APIs.

Preallocate memory and object pools

Require models to allocate objects and arrays outside render loops for high frame-rate Canvas animations. Never invoke new inside draw loops to prevent garbage collection pauses and frame drops.

05Input, execution, and outputs

Worked example

Using the deterministic frame rendering prompt (prompts/0xkarmi-129839.md) from the repository, generate a standalone HTML animation via Claude Code and verify headless rendering:

Opus 5.5 prompt execution transcript
$ git clone https://github.com/yihui-dev/awesome-opus5-5-videos.git
ok: Cloning into 'awesome-opus5-5-videos'... done.
$ ls prompts | wc -l
ok: 853
$ claude -p "Read prompts/0xkarmi-129839.md. Implement a self-contained HTML page: 1080x1080 canvas, Mulberry32 PRNG seed 42, deterministic draw(f) loop for 720 frames at 60fps. Save to butterfly.html"
ok: Created butterfly.html (self-contained HTML, Canvas 2D, 720 frames deterministic loop)
$ npx playwright screenshot --viewport-size=1080,1080 butterfly.html preview-frame-0.png
ok: Captured preview-frame-0.png (1080x1080, 0 frame drift)

Prompt input structure example:

markdown
Build a self-contained HTML page:
1. Canvas viewport: Fixed 1080x1080 pixels, background #0A0A0B.
2. Render loop: Deterministic draw(frameIndex) function, 720 frames (12 seconds at 60fps).
3. Deterministic constraints: No Date.now() or Math.random(); implement Mulberry32(seed=42) PRNG.
4. Visuals: Multi-layer vector geometric butterfly, wingbeat cycle of 60 frames, closed-loop particle flow.
5. Controls: Play/pause and frame-by-frame step buttons with frameIndex indicator.

Output verification criteria:

  • Open butterfly.html in a browser and observe smooth 60 fps playback upon clicking play.

  • Frame 0 and frame 720 canvas outputs align with zero boundary pixel shifts.

  • Reloading the page 5 times produces identical image hashes at frame 360.

06Failure conditions and recovery

Warnings and pitfalls

07Advanced workflows

Next steps

  1. Compare Skillry live remakes: Browse the Skillry Opus 5.5 Remake Library to inspect variations across models and prompts.

  2. Build automated frame-capture and encoding pipelines: Write Playwright scripts calling draw(f) per frame, exporting PNG sequences for FFmpeg encoding (ffmpeg -framerate 60 -i frame_%04d.png -c:v libx264 -pix_fmt yuv420p output.mp4).

  3. Integrate GSAP Skills standards: Consult skills-atlas-gsap-skills to translate GSAP animations into production React components using useGSAP().

References