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Get Started Free →Create subtle editorial word-by-word text reveal animations where each word fades and rises into place once it enters the viewport. Use for premium portfolio headlines, hero copy, section intros, and short marketing text that needs a cinematic staggered reveal with IntersectionObserver or in-view detection.
.claude/skills/mengto-staggered-word-reveal/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 11% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 31% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 82% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 5% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 14% | 0% |
opacity: 0, transform: translateY(20px).opacity: 1, transform: translateY(0).0.8s.cubic-bezier(0.16, 1, 0.3, 1).0.06s to 0.08s per word. Default to 0.07s.20% visible, with a slight lower viewport bias.html<h1 class="word-reveal" data-word-reveal> Build interfaces that feel calm, cinematic, and alive. </h1>
Keep no-JS content visible. Hide only after JavaScript is active and before the text has been split.
css.word-reveal { visibility: visible; } html.js .word-reveal[data-word-reveal]:not(.is-ready) { opacity: 0; } .word-reveal__word { display: inline-block; opacity: 0; transform: translate3d(0, 20px, 0); transition: opacity 0.8s cubic-bezier(0.16, 1, 0.3, 1), transform 0.8s cubic-bezier(0.16, 1, 0.3, 1); transition-delay: calc(var(--word-index) * 0.07s); will-change: opacity, transform; } .word-reveal.is-visible .word-reveal__word { opacity: 1; transform: translate3d(0, 0, 0); } @media (prefers-reduced-motion: reduce) { html.js .word-reveal[data-word-reveal]:not(.is-ready), .word-reveal__word { opacity: 1; transform: none; transition: none; } }
This splitter preserves spaces, avoids innerHTML, exposes the original sentence to screen readers, and unobserves after the first reveal.
jsdocument.documentElement.classList.add("js"); function splitWordReveal(element) { if (element.dataset.wordRevealReady === "true") return; const text = element.textContent || ""; const parts = text.split(/(\s+)/); let wordIndex = 0; element.textContent = ""; element.setAttribute("aria-label", text.trim()); parts.forEach((part) => { if (!part.trim()) { element.appendChild(document.createTextNode(part)); return; } const word = document.createElement("span"); word.className = "word-reveal__word"; word.setAttribute("aria-hidden", "true"); word.style.setProperty("--word-index", wordIndex); word.textContent = part; element.appendChild(word); wordIndex += 1; }); element.dataset.wordRevealReady = "true"; element.classList.add("is-ready"); } function initWordReveals(selector = "[data-word-reveal]") { const elements = Array.from(document.querySelectorAll(selector)); const reduceMotion = window.matchMedia("(prefers-reduced-motion: reduce)").matches; if (reduceMotion || !("IntersectionObserver" in window)) { elements.forEach((element) => { element.classList.add("is-ready", "is-visible"); }); return; } const observer = new IntersectionObserver( (entries, io) => { entries.forEach((entry) => { if (!entry.isIntersecting) return; entry.target.classList.add("is-visible"); io.unobserve(entry.target); }); }, { threshold: 0.2, rootMargin: "0px 0px -10% 0px", } ); elements.forEach((element) => { splitWordReveal(element); observer.observe(element); }); } document.addEventListener("DOMContentLoaded", () => { initWordReveals(); });
y: 20, opacity: 0, duration 0.8, ease [0.16, 1, 0.3, 1], stagger 0.06 to 0.08, once: true.fromTo(words, { y: 20, opacity: 0 }, { y: 0, opacity: 1, duration: 0.8, ease: "expo.out", stagger: 0.07 }).transform and opacity only.translateY(20px) and opacity: 0.0.06s to 0.08s delay.| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 17,900 | 11,847 | -34% | 1 | 1 | 0% | 3,803 | 4,234 | +11% | 0 | 0 | — |
case-02 | fail→pass | 14,044 | 10,817 | -23% | 1 | 1 | 0% | 2,939 | 3,848 | +31% | 0 | 0 | — |
case-03 | fail→fail | 13,583 | 8,609 | -37% | 1 | 1 | 0% | 2,895 | 3,387 | +17% | 0 | 0 | — |
case-04 | pass→pass | 16,969 | 9,188 | -46% | 1 | 1 | 0% | 3,080 | 3,276 | +6% | 0 | 0 | — |
case-05 | pass→pass | 13,480 | 8,833 | -34% | 1 | 1 | 0% | 2,634 | 3,097 | +18% | 0 | 0 | — |
case-06 | fail→pass | 11,207 | 13,107 | +17% | 1 | 1 | 0% | 2,195 | 3,988 | +82% | 0 | 0 | — |
case-07 | fail→pass | 11,588 | 4,108 | -65% | 1 | 1 | 0% | 2,149 | 2,263 | +5% | 0 | 0 | — |
case-08 | fail→pass | 14,035 | 9,717 | -31% | 1 | 1 | 0% | 2,890 | 3,308 | +14% | 0 | 0 | — |
case-09 | fail→pass | 13,120 | 5,774 | -56% | 1 | 1 | 0% | 2,471 | 2,664 | +8% | 0 | 0 | — |
case-10 | fail→pass | 9,774 | 5,243 | -46% | 1 | 1 | 0% | 2,042 | 2,534 | +24% | 0 | 0 | — |
case-11 | pass→pass | 12,045 | 5,351 | -56% | 1 | 1 | 0% | 2,204 | 2,470 | +12% | 0 | 0 | — |
case-12 | pass→pass | 9,808 | 6,510 | -34% | 1 | 1 | 0% | 1,764 | 2,602 | +48% | 0 | 0 | — |
case-13 | pass→pass | 11,250 | 6,454 | -43% | 1 | 1 | 0% | 1,896 | 2,613 | +38% | 0 | 0 | — |
case-14 | pass→pass | 8,238 | 5,602 | -32% | 1 | 1 | 0% | 1,521 | 2,385 | +57% | 0 | 0 | — |
case-15 | pass→pass | 15,043 | 4,657 | -69% | 1 | 1 | 0% | 2,957 | 2,238 | -24% | 0 | 0 | — |
case-16 | pass→pass | 8,852 | 3,522 | -60% | 1 | 1 | 0% | 1,529 | 1,979 | +29% | 0 | 0 | — |
case-17 | fail→pass | 11,745 | 2,747 | -77% | 1 | 1 | 0% | 2,234 | 1,911 | -14% | 0 | 0 | — |
case-18 | pass→pass | 11,142 | 2,982 | -73% | 1 | 1 | 0% | 2,180 | 2,052 | -6% | 0 | 0 | — |
case-19 | pass→pass | 11,251 | 6,667 | -41% | 1 | 1 | 0% | 2,229 | 2,862 | +28% | 0 | 0 | — |
case-20 | pass→pass | 20,760 | 21,206 | +2% | 1 | 1 | 0% | 4,600 | 6,215 | +35% | 0 | 0 | — |
case-21 | pass→pass | 11,646 | 14,408 | +24% | 1 | 1 | 0% | 2,292 | 4,416 | +93% | 0 | 0 | — |
case-22 | pass→pass | 11,035 | 11,452 | +4% | 1 | 1 | 0% | 2,180 | 3,723 | +71% | 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. 22 cases were attempted. The headline lift of +36 percentage points is the difference between those two pass rates over the 22 comparable cases.
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.