Source profileQuality 87/100

nexu-io/open-design/plugins/_official/scenarios/od-web-effect-extractor/SKILL.md

od-web-effect-extractor

Extract visual effects, animation systems, Canvas/WebGL/Shader behavior, and interaction details from a reference website, then rebuild them as an editable Open Design web artifact.

Source repository stars
82,073
Declared platforms
0
Static risk flags
0
Last source update
2026-07-28
Source checked
2026-07-28

Decision brief

What it does—and where it fits

Use this plugin when the user gives a website URL and asks Open Design to recreate, remix, or learn from its visual effects. It is especially useful for hero backgrounds, WebGL scenes, Canvas animations, shader effects, cursor trails, scroll-driven motion, animated typography, a…

Best for

    Not for

    • Tasks that require unconfirmed production actions or broad system permissions.
    • Environments where the pinned source and install steps cannot be inspected.

    Compatibility matrix

    Platform support, with evidence labels

    PlatformStatusEvidenceWhat to check
    CodexNot declaredNo explicit evidencePortability before use
    Claude CodeNot declaredNo explicit evidencePortability before use
    CursorNot declaredNo explicit evidencePortability before use
    Gemini CLINot declaredNo explicit evidencePortability before use
    Open the compatibility checker

    Installation

    Inspect first. Install second.

    The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.

    Source-detected install commandSource
    npx skills add https://github.com/nexu-io/open-design --skill "plugins/_official/scenarios/od-web-effect-extractor"
    Safe inspection promptEditorial

    Inspect the Agent Skill "od-web-effect-extractor" from https://github.com/nexu-io/open-design/blob/89d6d4ef21baf80f871595abdf6f7de6e941dd44/plugins/_official/scenarios/od-web-effect-extractor/SKILL.md at commit 89d6d4ef21baf80f871595abdf6f7de6e941dd44. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.

    Workflow

    What the source asks the agent to do

    1. 01

      Workflow

      Start from the supplied URL and define the exact effect to recreate. If the user is vague, inspect the first viewport first and infer the likely target effect.

      URL and capture timestampViewport sizes to test, usually desktop and mobileMain effect target and fallback effect targets
    2. 02

      Scope

      The goal is to produce a faithful, editable Open Design artifact that preserves the reference site's visual effect and interaction logic while replacing private content, trademarks, tracking code, and nonessential app logic.

      Layout structure, viewport behavior, breakpoints, and composition rhythmColor, typography, blend modes, gradients, texture, lighting, and post-processingMotion timing, easing, scroll triggers, pointer response, and idle animation loops
    3. 03

      1. Prepare The Capture

      Start from the supplied URL and define the exact effect to recreate. If the user is vague, inspect the first viewport first and infer the likely target effect.

      URL and capture timestampViewport sizes to test, usually desktop and mobileMain effect target and fallback effect targets
    4. 04

      2. Runtime Inspection

      Capture runtime evidence before simplifying anything.

      DOM tree, computed styles, fonts, CSS variables, custom properties, and animation namesCanvas elements, WebGL contexts, renderer attributes, supported extensions, and resolution scalingGlobal framework markers such as THREE, BABYLON, NEXTDATA, NUXT, vite, GSAP, Lenis, Framer Motion, or custom scene globals
    5. 05

      3. Extract The Visual Model

      Turn the raw capture into a compact visual model:

      Scene graph or layer stackAsset list with source, dimensions, and roleShader/material list with uniforms and dependencies

    Permission review

    Static risk signals and limitations

    No configured static risk pattern was detected

    This is not proof of safety. Runtime behavior, indirect dependencies, and hidden external systems are outside the static scan.

    Evidence record

    Why each signal appears

    EvidenceSourceComputedTestedEditorial
    SignalValueEvidence typeMeaning
    Quality score87/100ComputedDocumentation, specificity, maintenance, and trust rules
    Repository stars82,073SourceRepository attention, not individual Skill quality
    Compatibility0 platformsSourceDeclared in the catalog source record
    Usage guideautomated source guideEditorialGenerated or reviewed according to the visible evidence level

    Pinned source

    Provenance and original SKILL.md

    Repository
    nexu-io/open-design
    Skill path
    plugins/_official/scenarios/od-web-effect-extractor/SKILL.md
    Commit
    89d6d4ef21baf80f871595abdf6f7de6e941dd44
    License
    Apache-2.0
    Collected
    2026-07-28
    Default branch
    main
    View the original SKILL.md

    Web Effect Extractor

    Use this plugin when the user gives a website URL and asks Open Design to recreate, remix, or learn from its visual effects. It is especially useful for hero backgrounds, WebGL scenes, Canvas animations, shader effects, cursor trails, scroll-driven motion, animated typography, and interaction patterns that are hard to understand from screenshots alone.

    This workflow is inspired by the MIT-licensed web-shader-extractor skill from lixiaolin94/skills, adapted for Open Design's website recreation and editable artifact workflow.

    Scope

    The goal is to produce a faithful, editable Open Design artifact that preserves the reference site's visual effect and interaction logic while replacing private content, trademarks, tracking code, and nonessential app logic.

    Extract only what is needed to understand and recreate the visual behavior:

    • Layout structure, viewport behavior, breakpoints, and composition rhythm
    • Color, typography, blend modes, gradients, texture, lighting, and post-processing
    • Motion timing, easing, scroll triggers, pointer response, and idle animation loops
    • Canvas, WebGL, Three.js, shader, particle, and framebuffer pipeline details
    • Public static assets needed for visual parity, with provenance noted

    Do not copy private product copy, hidden application state, user data, analytics, auth flows, or unrelated business logic.

    Workflow

    1. Prepare The Capture

    Start from the supplied URL and define the exact effect to recreate. If the user is vague, inspect the first viewport first and infer the likely target effect.

    Before extracting runtime details, prefer a browser context that can evaluate page scripts and inspect canvas/WebGL state. If Chrome DevTools MCP is available, use it for runtime interception. If it is not available, continue with the strongest available browser automation and clearly state that shader/runtime capture may be incomplete.

    Record:

    • URL and capture timestamp
    • Viewport sizes to test, usually desktop and mobile
    • Main effect target and fallback effect targets
    • Framework clues from HTML, network requests, globals, and package signatures

    2. Runtime Inspection

    Capture runtime evidence before simplifying anything.

    Inspect:

    • DOM tree, computed styles, fonts, CSS variables, custom properties, and animation names
    • Canvas elements, WebGL contexts, renderer attributes, supported extensions, and resolution scaling
    • Global framework markers such as THREE, BABYLON, __NEXT_DATA__, __NUXT__, vite, GSAP, Lenis, Framer Motion, or custom scene globals
    • Networked JS bundles, images, videos, fonts, LUTs, textures, HDRIs, and data files

    When WebGL is present and tooling allows pre-page-load injection, intercept:

    • gl.shaderSource() for vertex and fragment source
    • gl.uniform*() calls for names and observed values
    • gl.bindFramebuffer() to understand multipass render order
    • gl.drawArrays() and gl.drawElements() to understand draw-call order
    • texture and buffer setup when it affects the final look

    When 2D Canvas is present, inspect the render loop, draw primitives, image sources, compositing operations, and device-pixel-ratio handling.

    3. Extract The Visual Model

    Turn the raw capture into a compact visual model:

    • Scene graph or layer stack
    • Asset list with source, dimensions, and role
    • Shader/material list with uniforms and dependencies
    • Motion map with trigger, duration, easing, repeat behavior, and responsive differences
    • Interaction map for pointer, keyboard, scroll, resize, and reduced-motion handling
    • Known uncertainties, such as minified bundle branches or runtime values that could not be observed

    Prefer evidence over guesses. If an effect cannot be observed directly, mark it as an inference.

    4. Rebuild In Open Design

    Create a standalone web artifact that can be inspected and edited.

    Choose the simplest implementation that preserves the effect:

    • Static DOM/CSS for layout-only references
    • CSS animation or WAAPI for simple motion
    • GSAP for sequenced or scroll-linked motion
    • Canvas 2D for 2D procedural effects
    • Native WebGL2 for compact full-screen shader effects
    • Three.js when the reference uses 3D, cameras, materials, textures, post-processing, or GPGPU patterns

    Keep the rebuild self-contained unless a dependency materially reduces complexity. If dependencies are used, load them in a transparent, reviewable way.

    Important reconstruction rules:

    • Preserve color management and output color space.
    • Preserve the reference time base, such as seconds, elapsed milliseconds, frame count, or scroll progress.
    • Preserve multipass ordering for shader effects.
    • Do not tune random values to hide a root-cause mismatch; fix the model instead.
    • Add prefers-reduced-motion fallbacks for intense effects.
    • Replace copyrighted or brand-specific content unless the user explicitly owns it or asks to preserve it.

    5. Verify Visual Parity

    Open the reference and the rebuilt artifact at the same viewport sizes. Compare:

    • First viewport framing
    • Color, brightness, contrast, and blending
    • Motion rhythm and loop continuity
    • Pointer and scroll response
    • Mobile behavior and performance
    • Canvas/WebGL nonblank rendering

    For WebGL and Canvas work, verify that the canvas is not blank and that animation advances over time. If possible, compare screenshots or pixel samples across at least two timestamps.

    6. Deliver

    Finish with:

    • The rebuilt artifact path or preview URL
    • A short extraction summary
    • Any visual differences that remain
    • Asset provenance and any replaced assets
    • Notes on what the user can safely edit next

    If the user asks for an extraction report, create EXTRACTION-REPORT.md with the source URL, capture method, visual model, implementation choices, validation notes, and remaining gaps.

    Quality Bar

    • The artifact must run independently from the source website.
    • The primary visual effect must be recognizable without explanation.
    • The implementation must be understandable enough for a designer or engineer to edit.
    • The workflow must avoid copying unrelated application code.
    • The response must distinguish captured facts from inferred reconstruction choices.

    Alternatives

    Compare before choosing

    Computed 997

    event4u-app/agent-config

    design-review

    Use when the user says "review the design", "check the UI", or wants a comprehensive UI/UX review. Uses a 7-phase methodology covering interaction, responsiveness, accessibility, and more.

    Computed 9827

    MoizIbnYousaf/marketing-cli

    higgsfield-generate

    Use when the user wants to generate an image or video via Higgsfield AI. Covers 30+ models: Soul V2, Seedance 2.0, Kling 3.0, Veo 3.1, GPT Image 2, Nano Banana 2. Also covers Marketing Studio — branded ad video/image with avatars and products. Use whenever: "generate an image", "make a video", "animate this photo", "image-to-video", "img2vid", "edit this image with AI", "produce a clip", "create an ad", "make a UGC video", "marketing video", "brand video", "TV spot", "import product from URL", "

    Computed 9637,126

    github/awesome-copilot

    arize-evaluator

    Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.

    Computed 9631,966

    K-Dense-AI/scientific-agent-skills

    neuropixels-analysis

    Analyze Neuropixels extracellular recordings end-to-end with SpikeInterface. Covers loading SpikeGLX/Open Ephys/NWB data, preprocessing, drift/motion correction, Kilosort4 (and CPU) spike sorting, quality metrics, and unit curation (threshold-based, model-based UnitRefine, and AI-assisted visual review). Use when working with Neuropixels 1.0/2.0 recordings, spike sorting, or extracellular electrophysiology analysis.