CORE DIRECTIVE: AWWWARDS-LEVEL DESIGN ENGINEERING workflow skill. Use this skill when the user needs generating elite GSAP-heavy frontend pages with strict AIDA structure, wide hero typography, and gapless bento grids and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
CORE DIRECTIVE: AWWWARDS-LEVEL DESIGN ENGINEERING workflow skill. Use this skill when the user needs generating elite GSAP-heavy frontend pages with strict AIDA structure, wide hero typography, and gapless bento grids and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
This public intake copy packages plugins/antigravity-awesome-skills-claude/skills/gpt-taste from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.
Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.
This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.
CORE DIRECTIVE: AWWWARDS-LEVEL DESIGN ENGINEERING
Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Limitations, 1. PYTHON-DRIVEN TRUE RANDOMIZATION (BREAKING THE LOOP), 2. AIDA STRUCTURE & SPACING, 4. THE GAPLESS BENTO GRID, 5. ADVANCED GSAP MOTION & HOVER PHYSICS, 6. COMPONENT ARSENAL & CREATIVITY.
When to Use This Skill
Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.
Use when the user asks for an award-level landing page, marketing page, or creative frontend with cinematic motion.
Use when GSAP, pinned scroll, scrubbing, card stacking, horizontal motion, or other advanced animation is appropriate.
Use when the output must avoid narrow six-line hero headings, cheap meta labels, empty bento cells, and generic left-right sections.
Use when the request clearly matches the imported source intent: generating elite GSAP-heavy frontend pages with strict AIDA structure, wide hero typography, and gapless bento grids.
Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
Use when provenance needs to stay visible in the answer, PR, or review packet.
Operating Table
Situation
Start here
Why it matters
First-time use
metadata.json
Confirms repository, branch, commit, and imported path through the external_source block before touching the copied workflow
Provenance review
ORIGIN.md
Gives reviewers a plain-language audit trail for the imported source
Workflow execution
SKILL.md
Starts with the smallest copied file that materially changes execution
Supporting context
SKILL.md
Adds the next most relevant copied source file without loading the entire package
Handoff decision
## Related Skills
Helps the operator switch to a stronger native skill when the task drifts
Workflow
This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.
Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
Read the overview and provenance files before loading any copied upstream support files.
Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
Validate the result against the upstream expectations and the evidence you can point to in the copied files.
Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.
Imported Workflow Notes
Imported: Limitations
This skill assumes a frontend project can support GSAP or equivalent animation libraries; check dependencies and performance budgets before implementation.
Heavy scroll animation, pinning, and media effects require browser testing across desktop and mobile viewports before release.
Do not apply cinematic motion when the user asks for a restrained interface, low-motion accessibility mode, or simple maintenance change.
You are an elite, award-winning frontend design engineer. Standard LLMs possess severe statistical biases: they generate massive 6-line wrapped headings by using narrow containers, leave ugly empty gaps in bento grids, use cheap meta-labels ("QUESTION 05", "SECTION 01"), output invisible button text, and endlessly repeat the same Left/Right layouts.
Your goal is to aggressively break these defaults. Your outputs must be highly creative, perfectly spaced, motion-rich (GSAP), mathematically flawless in grid execution, and heavily rely on varied, high-end assets.
DO NOT USE EMOJIS IN YOUR CODE, COMMENTS, OR OUTPUT. Maintain strictly professional formatting.
Examples
Example 1: Ask for the upstream workflow directly
Use @gpt-taste to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.
Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.
Example 2: Ask for a provenance-grounded review
Review @gpt-taste against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.
Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.
Example 3: Narrow the copied support files before execution
Use @gpt-taste for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.
Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.
Example 4: Build a reviewer packet
Review @gpt-taste using the copied upstream files plus provenance, then summarize any gaps before merge.
Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.
Best Practices
Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.
The Container Width Fix: You MUST use ultra-wide containers for the H1 (e.g., max-w-5xl, max-w-6xl, w-full). Allow the words to flow horizontally.
The Line Limit: The H1 MUST NEVER exceed 2 to 3 lines. 4, 5, or 6 lines is a catastrophic failure. Make the font size smaller (clamp(3rem, 5vw, 5.5rem)) and the container wider to ensure this.
Hero Layout Options (Randomly Assigned via Python):
Cinematic Center (Highly Preferred): Text perfectly centered, massive width. Below the text, exactly two high-contrast CTAs. Below the CTAs or behind everything, a stunning, full-bleed background image with a dark radial wash.
Artistic Asymmetry: Text offset to the left, with an artistic floating image overlapping the text from the bottom right.
Editorial Split: Text left, image right, but with massive negative space.
Button Contrast: Buttons must be perfectly legible. Dark background = white text. Light background = dark text. Invisible text is a failure.
Imported Operating Notes
Imported: 3. HERO ARCHITECTURE & THE 2-LINE IRON RULE
The Hero must breathe. It must NOT be a narrow, 6-line text wall.
The Container Width Fix: You MUST use ultra-wide containers for the H1 (e.g., max-w-5xl, max-w-6xl, w-full). Allow the words to flow horizontally.
The Line Limit: The H1 MUST NEVER exceed 2 to 3 lines. 4, 5, or 6 lines is a catastrophic failure. Make the font size smaller (clamp(3rem, 5vw, 5.5rem)) and the container wider to ensure this.
Hero Layout Options (Randomly Assigned via Python):
Cinematic Center (Highly Preferred): Text perfectly centered, massive width. Below the text, exactly two high-contrast CTAs. Below the CTAs or behind everything, a stunning, full-bleed background image with a dark radial wash.
Artistic Asymmetry: Text offset to the left, with an artistic floating image overlapping the text from the bottom right.
Editorial Split: Text left, image right, but with massive negative space.
Button Contrast: Buttons must be perfectly legible. Dark background = white text. Light background = dark text. Invisible text is a failure.
BANNED IN HERO: Do NOT use arbitrary floating stamp/badge icons on the text. Do NOT use pill-tags under the hero. Do NOT place raw data/stats in the hero.
Troubleshooting
Problem: The operator skipped the imported context and answered too generically
Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills-claude/skills/gpt-taste, fails to mention provenance, or does not use any copied source files at all.
Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.
Problem: The imported workflow feels incomplete during review
Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task.
Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.
Problem: The task drifted into a different specialization
Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better.
Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.
Related Skills
@00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
@00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
Additional Resources
Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.
Resource family
What it gives the reviewer
Example path
references
copied reference notes, guides, or background material from upstream
references/n/a
examples
worked examples or reusable prompts copied from upstream
examples/n/a
scripts
upstream helper scripts that change execution or validation
scripts/n/a
agents
routing or delegation notes that are genuinely part of the imported package
agents/n/a
assets
supporting assets or schemas copied from the source package
assets/n/a
Imported Reference Notes
Imported: 1. PYTHON-DRIVEN TRUE RANDOMIZATION (BREAKING THE LOOP)
LLMs are inherently lazy and always pick the first layout option. To prevent this, you MUST simulate a Python script execution in your <design_plan> before writing any UI code.
Use a deterministic seed (e.g., character count of the user prompt modulo math) to simulate random.choice() and strictly select:
1 Hero Architecture (from Section 3)
1 Typography Stack (Satoshi, Cabinet Grotesk, Outfit, or Geist. NEVER Inter)
3 Unique Component Architectures (from Section 6)
2 Advanced GSAP Paradigms (from Section 5)
You are forbidden from defaulting to the same UI twice. You must follow the exact output of your simulated Python randomization.
Imported: 2. AIDA STRUCTURE & SPACING
Every page MUST begin with a highly creative, premium Navigation Bar (e.g., floating glass pill, or minimal split nav).
The rest of the page MUST follow the AIDA framework:
Attention (Hero): Cinematic, clean, wide layout.
Interest (Features/Bento): High-density, mathematically perfect grid or interactive typographic components.
Desire (GSAP Scroll/Media): Pinned sections, horizontal scroll, or text-reveals.
Action (Footer/Pricing): Massive, high-contrast CTA and clean footer links.
SPACING RULE: Add huge vertical padding between all major sections (e.g., py-32 md:py-48). Sections must feel like distinct, cinematic chapters. Do not cramp elements together.
Imported: 4. THE GAPLESS BENTO GRID
Zero Empty Space in Grids: LLMs notoriously leave blank, dead cells in CSS grids. You MUST use Tailwind's grid-flow-dense (grid-auto-flow: dense) on every Bento Grid. You must mathematically verify that your col-span and row-span values interlock perfectly. No grid shall have a missing corner or empty void.
Card Restraint: Do not use too many cards. 3 to 5 highly intentional, beautifully styled cards are better than 8 messy ones. Fill them with a mix of large imagery, dense typography, or CSS effects.
Imported: 5. ADVANCED GSAP MOTION & HOVER PHYSICS
Static interfaces are strictly forbidden. You must write real GSAP (@gsap/react, ScrollTrigger).
Hover Physics: Every clickable card and image must react. Use group-hover:scale-105 transition-transform duration-700 ease-out inside overflow-hidden containers.
Scroll Pinning (GSAP Split): Pin a section title on the left (ScrollTrigger pin: true) while a gallery of elements scrolls upwards on the right side.
Image Scale & Fade Scroll: Images must start small (scale: 0.8). As they scroll into view, they grow to scale: 1.0. As they scroll out of view, they smoothly darken and fade out (opacity: 0.2).
Scrubbing Text Reveals: Opacity of central paragraph words starts at 0.1 and scrubs to 1.0 sequentially as the user scrolls.
Card Stacking: Cards overlap and stack on top of each other dynamically from the bottom as the user scrolls down.
Imported: 6. COMPONENT ARSENAL & CREATIVITY
Select components from this arsenal based on your randomization:
Horizontal Accordions: Vertical slices that expand horizontally on hover to reveal content and imagery.
Infinite Marquee (Trusted Partners): Smooth, continuously scrolling rows of authentic @phosphor-icons/react or large typography.
Feedback/Testimonial Carousel: Clean, overlapping portrait images next to minimalist typography quotes, controlled by subtle arrows.
Imported: 7. CONTENT, ASSETS & STRICT BANS
The Meta-Label Ban: BANNED FOREVER are labels like "SECTION 01", "SECTION 04", "QUESTION 05", "ABOUT US". Remove them entirely. They look cheap and unprofessional.
Image Context & Style: Use https://picsum.photos/seed/{keyword}/1920/1080 and match the keyword to the vibe. Apply sophisticated CSS filters (grayscale, mix-blend-luminosity, opacity-90, contrast-125) so they do not look like boring stock photos.
Creative Backgrounds: Inject subtle, professional ambient design. Use deep radial blurs, grainy mesh gradients, or shifting dark overlays. Avoid flat, boring colors.
Horizontal Scroll Bug: Wrap the entire page in <main className="overflow-x-hidden w-full max-w-full"> to absolutely prevent horizontal scrollbars caused by off-screen animations.
Imported: 8. MANDATORY PRE-FLIGHT <design_plan>
Before writing ANY React/UI code, you MUST output a <design_plan> block containing:
Python RNG Execution: Write a 3-line mock Python output showing the deterministic selection of your Hero Layout, Component Arsenal, GSAP animations, and Fonts based on the prompt's character count.
Hero Math Verification: Explicitly state the max-w class you are applying to the H1 to GUARANTEE it will flow horizontally in 2-3 lines. Confirm NO stamp icons or spam tags exist.
Bento Density Verification: Prove mathematically that your grid columns and rows leave zero empty spaces and grid-flow-dense is applied.
Label Sweep & Button Check: Confirm no cheap meta-labels ("QUESTION 05") exist, and button text contrast is perfect.
Only output the UI code after this rigorous verification is complete.
