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Get Started Free →Use when auditing slow page loads, heavy assets, or rendering delays related to Optimize largest contentful paint. Verify the actual bottleneck in DevTools, Lighthouse, or field data before recommending changes.
.claude/skills/thedaviddias-largest-contentful-paint/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-08 | ✗→✓ | ▲ Improved | 1% | 0% |
| case-13 | ✓→✗ | ▼ Worse | 3% | 0% |
| case-02 | ✓→✓ | ＝ Same ✓ | -22% | 0% |
| case-03 | ✓→✓ | ＝ Same ✓ | 12% | 0% |
| case-04 | ✓→✓ | ＝ Same ✓ | -39% | 0% |
LCP is the most important Core Web Vital for perceived load speed—it measures when the main content becomes visible, directly impacting user perception and SEO rankings.
Measure LCP using Lighthouse or PageSpeed Insights. Verify the largest visible element renders within 2.5 seconds.
Optimize LCP by preloading critical resources, optimizing images, using CDN, and reducing server response time.
Explain how LCP measures perceived load speed by tracking when the largest visible content element becomes visible.
Review the routes, assets, and loading behavior that affect Optimize largest contentful paint. 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/largest-contentful-paint
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 19,240 | 19,997 | +4% | 1 | 1 | 0% | 3,216 | 3,554 | +11% | 0 | 0 | — |
case-02 | pass→pass | 19,376 | 16,630 | -14% | 1 | 1 | 0% | 1,892 | 1,481 | -22% | 0 | 0 | — |
case-03 | pass→pass | 12,504 | 17,542 | +40% | 1 | 1 | 0% | 2,023 | 2,274 | +12% | 0 | 0 | — |
case-04 | pass→pass | 15,999 | 7,115 | -56% | 1 | 1 | 0% | 2,420 | 1,488 | -39% | 0 | 0 | — |
case-05 | pass→pass | 14,716 | 8,031 | -45% | 1 | 1 | 0% | 2,448 | 1,610 | -34% | 0 | 0 | — |
case-06 | pass→pass | 14,305 | 8,725 | -39% | 1 | 1 | 0% | 2,285 | 1,873 | -18% | 0 | 0 | — |
case-07 | pass→pass | 11,600 | 8,542 | -26% | 1 | 1 | 0% | 1,763 | 1,514 | -14% | 0 | 0 | — |
case-08 | fail→pass | 21,654 | 19,205 | -11% | 1 | 1 | 0% | 3,379 | 3,414 | +1% | 0 | 0 | — |
case-09 | pass→pass | 11,909 | 7,540 | -37% | 1 | 1 | 0% | 1,573 | 1,357 | -14% | 0 | 0 | — |
case-10 | pass→pass | 16,613 | 11,854 | -29% | 1 | 1 | 0% | 2,498 | 2,098 | -16% | 0 | 0 | — |
case-11 | pass→pass | 12,812 | 9,832 | -23% | 1 | 1 | 0% | 1,909 | 1,805 | -5% | 0 | 0 | — |
case-12 | pass→pass | 7,619 | 5,899 | -23% | 1 | 1 | 0% | 1,178 | 1,152 | -2% | 0 | 0 | — |
case-13 | pass→fail | 16,797 | 15,069 | -10% | 1 | 1 | 0% | 2,488 | 2,570 | +3% | 0 | 0 | — |
case-14 | pass→pass | 13,440 | 7,820 | -42% | 1 | 1 | 0% | 1,991 | 1,450 | -27% | 0 | 0 | — |
case-15 | pass→pass | 11,900 | 6,790 | -43% | 1 | 1 | 0% | 1,747 | 1,264 | -28% | 0 | 0 | — |
case-16 | pass→pass | 17,312 | 14,717 | -15% | 1 | 1 | 0% | 2,690 | 2,775 | +3% | 0 | 0 | — |
case-17 | pass→pass | 17,418 | 13,744 | -21% | 1 | 1 | 0% | 2,323 | 2,219 | -4% | 0 | 0 | — |
case-18 | pass→pass | 6,999 | 5,108 | -27% | 1 | 1 | 0% | 945 | 1,031 | +9% | 0 | 0 | — |
case-19 | pass→pass | 16,440 | 9,177 | -44% | 1 | 1 | 0% | 1,855 | 1,533 | -17% | 0 | 0 | — |
case-20 | pass→pass | 16,262 | 12,615 | -22% | 1 | 1 | 0% | 2,329 | 1,814 | -22% | 0 | 0 | — |
case-21 | pass→pass | 5,301 | 4,866 | -8% | 1 | 1 | 0% | 753 | 987 | +31% | 0 | 0 | — |
case-22 | pass→pass | 4,579 | 2,924 | -36% | 1 | 1 | 0% | 611 | 802 | +31% | 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 0 percentage points is the difference between those two pass rates over the 22 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.