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Get Started Free →Use when auditing slow page loads, heavy assets, or rendering delays related to Implement lazy loading for offscreen content. Verify the actual bottleneck in DevTools, Lighthouse, or field data before recommending changes. Absence of `loading='lazy'` is not enough by itself when fold position is unknown.
.claude/skills/thedaviddias-lazy-loading/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-12 | ✗→✓ | ▲ Improved | -50% | 0% |
| case-18 | ✗→✓ | ▲ Improved | -31% | 0% |
| case-01 | ✓→✓ | = Same ✓ | -15% | 0% |
| case-02 | ✓→✓ | = Same ✓ | 138% | 0% |
| case-03 | ✓→✓ | = Same ✓ | -40% | 0% |
Lazy loading defers non-critical resources until needed—this reduces initial page weight, speeds up first paint, and saves bandwidth for content users may never scroll to.
Verify that images and other heavy resources below the fold are lazy loaded. Only report this when the code or route context makes offscreen placement clear.
Implement lazy loading for images, videos, and iframes using native loading='lazy' or JavaScript solutions.
Explain how lazy loading defers resource loading until needed, improving initial page load performance.
Review the routes, assets, and loading behavior that affect Implement lazy loading for offscreen content. Flag exact files, requests, or rendering steps that add unnecessary network, CPU, or layout cost, and describe the measurement method used to confirm the issue.
For full implementation details, code examples, and framework-specific guidance, see references/rule.md.
Rule page: https://frontendchecklist.io/en/rules/performance/lazy-loading
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | pass→pass | 9,273 | 7,005 | -24% | 1 | 1 | 0% | 1,799 | 1,537 | -15% | 0 | 0 | — |
case-02 | pass→pass | 3,303 | 4,916 | +49% | 1 | 1 | 0% | 438 | 1,042 | +138% | 0 | 0 | — |
case-03 | pass→pass | 12,902 | 7,358 | -43% | 1 | 1 | 0% | 2,400 | 1,447 | -40% | 0 | 0 | — |
case-04 | pass→pass | 11,947 | 7,190 | -40% | 1 | 1 | 0% | 2,151 | 1,509 | -30% | 0 | 0 | — |
case-05 | pass→pass | 10,684 | 4,959 | -54% | 1 | 1 | 0% | 1,609 | 1,107 | -31% | 0 | 0 | — |
case-06 | pass→pass | 3,947 | 3,981 | +1% | 1 | 1 | 0% | 654 | 872 | +33% | 0 | 0 | — |
case-07 | pass→pass | 14,342 | 8,616 | -40% | 1 | 1 | 0% | 2,224 | 1,878 | -16% | 0 | 0 | — |
case-08 | pass→pass | 15,623 | 6,924 | -56% | 1 | 1 | 0% | 2,694 | 1,300 | -52% | 0 | 0 | — |
case-09 | pass→pass | 8,025 | 7,499 | -7% | 1 | 1 | 0% | 1,363 | 1,415 | +4% | 0 | 0 | — |
case-10 | pass→pass | 10,774 | 6,335 | -41% | 1 | 1 | 0% | 1,760 | 1,288 | -27% | 0 | 0 | — |
case-11 | fail→fail | 6,536 | 5,616 | -14% | 1 | 1 | 0% | 1,012 | 1,186 | +17% | 0 | 0 | — |
case-12 | fail→pass | 9,973 | 2,898 | -71% | 1 | 1 | 0% | 1,495 | 748 | -50% | 0 | 0 | — |
case-13 | pass→pass | 4,155 | 3,857 | -7% | 1 | 1 | 0% | 611 | 830 | +36% | 0 | 0 | — |
case-14 | pass→pass | 11,367 | 7,896 | -31% | 1 | 1 | 0% | 1,676 | 1,501 | -10% | 0 | 0 | — |
case-15 | pass→pass | 12,688 | 7,753 | -39% | 1 | 1 | 0% | 1,869 | 1,471 | -21% | 0 | 0 | — |
case-16 | pass→pass | 9,529 | 6,429 | -33% | 1 | 1 | 0% | 1,317 | 1,170 | -11% | 0 | 0 | — |
case-17 | pass→pass | 10,783 | 5,189 | -52% | 1 | 1 | 0% | 1,594 | 1,009 | -37% | 0 | 0 | — |
case-18 | fail→pass | 18,882 | 10,489 | -44% | 1 | 1 | 0% | 2,562 | 1,755 | -31% | 0 | 0 | — |
case-19 | pass→pass | 9,566 | 8,329 | -13% | 1 | 1 | 0% | 1,712 | 1,475 | -14% | 0 | 0 | — |
case-20 | pass→pass | 10,248 | 8,264 | -19% | 1 | 1 | 0% | 1,673 | 1,492 | -11% | 0 | 0 | — |
case-21 | pass→pass | 9,542 | 5,418 | -43% | 1 | 1 | 0% | 1,414 | 1,075 | -24% | 0 | 0 | — |
case-22 | pass→pass | 22,363 | 14,222 | -36% | 1 | 1 | 0% | 2,860 | 2,219 | -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. 22 cases were attempted. The headline lift of +9 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.