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Get Started Free →Create masked staggered word reveals on scroll with GSAP ScrollTrigger. Use when headings, hero copy, section titles, or editorial text should reveal word-by-word through an overflow mask as they enter the viewport.
.claude/skills/mengto-masked-reveal/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 4% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 14% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 17% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 17% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 48% | 0% |
82% of the viewport.0.7s to 0.9s.0.025s to 0.045s per word.yPercent: 110 to 0.power3.out or expo.out.html<h1 class="masked-reveal" data-masked-reveal> Design systems that feel alive from the first scroll. </h1>
css.masked-reveal { visibility: visible; } html.js .masked-reveal[data-masked-reveal] { visibility: hidden; } html.js .masked-reveal.is-split { visibility: visible; } .masked-reveal .word-mask { display: inline-block; overflow: hidden; vertical-align: top; } .masked-reveal .word { display: inline-block; transform: translateY(110%); will-change: transform; } @media (prefers-reduced-motion: reduce) { html.js .masked-reveal[data-masked-reveal] { visibility: visible; } .masked-reveal .word { transform: none; } }
This helper avoids the paid SplitText plugin and keeps spaces intact.
jsdocument.documentElement.classList.add("js"); gsap.registerPlugin(ScrollTrigger); function escapeHTML(value) { return value .replace(/&/g, "&") .replace(/</g, "<") .replace(/>/g, ">") .replace(/"/g, """) .replace(/'/g, "'"); } function splitMaskedReveal(element) { if (element.dataset.maskedRevealReady === "true") return; const text = element.textContent.trim(); element.setAttribute("aria-label", text); element.innerHTML = text .split(/(\s+)/) .map((part) => { if (!part.trim()) return part; return `<span class="word-mask" aria-hidden="true"><span class="word">${escapeHTML(part)}</span></span>`; }) .join(""); element.dataset.maskedRevealReady = "true"; element.classList.add("is-split"); } function initMaskedReveals(selector = "[data-masked-reveal]") { if (window.matchMedia("(prefers-reduced-motion: reduce)").matches) return; document.querySelectorAll(selector).forEach((element) => { splitMaskedReveal(element); const words = element.querySelectorAll(".word"); gsap.set(element, { autoAlpha: 1 }); gsap.fromTo( words, { yPercent: 110 }, { yPercent: 0, duration: 0.8, ease: "power3.out", stagger: 0.035, scrollTrigger: { trigger: element, start: "top 82%", once: true, }, } ); }); } initMaskedReveals();
jsuseLayoutEffect(() => { const ctx = gsap.context(() => { initMaskedReveals("[data-masked-reveal]"); }, rootRef); return () => ctx.revert(); }, []);
ScrollTrigger.refresh() after late-loading images or layout shifts.autoAlpha: 1.aria-label.| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 15,522 | 10,161 | -35% | 1 | 1 | 0% | 3,308 | 3,434 | +4% | 0 | 0 | — |
case-02 | fail→pass | 14,955 | 10,292 | -31% | 1 | 1 | 0% | 3,079 | 3,508 | +14% | 0 | 0 | — |
case-03 | fail→pass | 15,236 | 10,976 | -28% | 1 | 1 | 0% | 2,999 | 3,498 | +17% | 0 | 0 | — |
case-04 | fail→fail | 13,851 | 12,369 | -11% | 1 | 1 | 0% | 2,818 | 3,450 | +22% | 0 | 0 | — |
case-05 | fail→fail | 14,123 | 15,811 | +12% | 1 | 1 | 0% | 2,995 | 4,361 | +46% | 0 | 0 | — |
case-06 | fail→fail | 14,873 | 13,962 | -6% | 1 | 1 | 0% | 3,076 | 4,277 | +39% | 0 | 0 | — |
case-07 | fail→pass | 15,529 | 11,789 | -24% | 1 | 1 | 0% | 3,031 | 3,553 | +17% | 0 | 0 | — |
case-08 | fail→pass | 13,227 | 12,496 | -6% | 1 | 1 | 0% | 2,404 | 3,567 | +48% | 0 | 0 | — |
case-17 | fail→pass | 10,052 | 1,609 | -84% | 1 | 1 | 0% | 1,713 | 1,366 | -20% | 0 | 0 | — |
case-09 | fail→pass | 11,235 | 7,576 | -33% | 1 | 1 | 0% | 1,923 | 2,572 | +34% | 0 | 0 | — |
case-10 | fail→pass | 6,685 | 2,901 | -57% | 1 | 1 | 0% | 1,245 | 1,697 | +36% | 0 | 0 | — |
case-11 | pass→fail | 13,437 | 15,181 | +13% | 1 | 1 | 0% | 2,799 | 4,134 | +48% | 0 | 0 | — |
case-12 | pass→pass | 13,818 | 11,845 | -14% | 1 | 1 | 0% | 2,974 | 3,417 | +15% | 0 | 0 | — |
case-23 | pass→pass | 11,346 | 8,047 | -29% | 1 | 1 | 0% | 2,244 | 2,561 | +14% | 0 | 0 | — |
case-13 | pass→pass | 5,887 | 4,165 | -29% | 1 | 1 | 0% | 1,033 | 1,917 | +86% | 0 | 0 | — |
case-14 | pass→pass | 10,527 | 6,372 | -39% | 1 | 1 | 0% | 2,059 | 2,293 | +11% | 0 | 0 | — |
case-15 | pass→pass | 13,324 | 7,339 | -45% | 1 | 1 | 0% | 2,621 | 2,599 | -1% | 0 | 0 | — |
case-16 | pass→pass | 7,709 | 3,310 | -57% | 1 | 1 | 0% | 1,446 | 1,685 | +17% | 0 | 0 | — |
case-18 | pass→pass | 9,537 | 2,837 | -70% | 1 | 1 | 0% | 1,852 | 1,655 | -11% | 0 | 0 | — |
case-19 | pass→pass | 11,706 | 4,352 | -63% | 1 | 1 | 0% | 2,180 | 1,946 | -11% | 0 | 0 | — |
case-20 | fail→pass | 7,883 | 3,982 | -49% | 1 | 1 | 0% | 1,352 | 1,793 | +33% | 0 | 0 | — |
case-21 | pass→pass | 8,208 | 4,341 | -47% | 1 | 1 | 0% | 1,663 | 1,957 | +18% | 0 | 0 | — |
case-22 | fail→fail | 16,297 | 12,569 | -23% | 1 | 1 | 0% | 2,477 | 3,243 | +31% | 0 | 0 | — |
case-24 | pass→pass | 4,499 | 2,949 | -34% | 1 | 1 | 0% | 723 | 1,553 | +115% | 0 | 0 | — |
case-25 | pass→pass | 10,429 | 1,887 | -82% | 1 | 1 | 0% | 1,913 | 1,491 | -22% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 25 cases were attempted. The headline lift of +32 percentage points is the difference between those two pass rates over the 25 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.