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Get Started Free →Use when auditing slow page loads, heavy assets, or rendering delays related to Keep page weight under 1500KB. Verify the actual bottleneck in DevTools, Lighthouse, or field data before recommending changes.
.claude/skills/thedaviddias-page-weight/SKILL.md| Model | Eval pass | Runs |
|---|---|---|
| gemini-3.6-flash | 100% | 2 |
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 72% | 0% |
| case-06 | ✗→✓ | ▲ Improved | -18% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -43% | 0% |
| case-09 | ✗→✓ | ▲ Improved | -43% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 26% | 0% |
Page weight directly correlates with load time—a 1.5MB page takes 3-5 seconds on 4G mobile. Every 100KB reduction improves user experience, especially on slower networks.
Analyze the total page weight including all resources. Check if it's under 1500KB (ideally under 500KB).
Optimize this page to reduce its total weight through image optimization, code minification, and removing unnecessary resources.
Explain how page weight impacts loading time, especially on mobile networks, and affects user experience.
Review the routes, assets, and loading behavior that affect Keep page weight under 1500KB. 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/page-weight
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 12,514 | 17,423 | +39% | 1 | 1 | 0% | 2,102 | 3,625 | +72% | 0 | 0 | — |
case-02 | fail→fail | 13,920 | 1,541 | -89% | 1 | 1 | 0% | 2,857 | 534 | -81% | 0 | 0 | — |
case-03 | pass→pass | 15,109 | 11,125 | -26% | 1 | 1 | 0% | 2,667 | 2,393 | -10% | 0 | 0 | — |
case-04 | pass→pass | 18,516 | 15,578 | -16% | 1 | 1 | 0% | 3,322 | 3,338 | +0% | 0 | 0 | — |
case-05 | pass→pass | 13,785 | 11,353 | -18% | 1 | 1 | 0% | 2,549 | 2,253 | -12% | 0 | 0 | — |
case-06 | fail→pass | 13,856 | 8,943 | -35% | 1 | 1 | 0% | 2,399 | 1,976 | -18% | 0 | 0 | — |
case-07 | fail→pass | 8,929 | 4,107 | -54% | 1 | 1 | 0% | 1,626 | 931 | -43% | 0 | 0 | — |
case-08 | pass→pass | 5,598 | 2,826 | -50% | 1 | 1 | 0% | 941 | 755 | -20% | 0 | 0 | — |
case-09 | fail→pass | 11,057 | 5,010 | -55% | 1 | 1 | 0% | 2,029 | 1,155 | -43% | 0 | 0 | — |
case-10 | pass→pass | 15,359 | 13,759 | -10% | 1 | 1 | 0% | 2,586 | 2,932 | +13% | 0 | 0 | — |
case-11 | pass→pass | 12,871 | 14,233 | +11% | 1 | 1 | 0% | 2,171 | 2,910 | +34% | 0 | 0 | — |
case-12 | fail→pass | 6,552 | 6,053 | -8% | 1 | 1 | 0% | 1,068 | 1,348 | +26% | 0 | 0 | — |
case-13 | fail→pass | 12,200 | 8,769 | -28% | 1 | 1 | 0% | 2,142 | 1,836 | -14% | 0 | 0 | — |
case-14 | fail→pass | 14,063 | 12,396 | -12% | 1 | 1 | 0% | 2,398 | 2,439 | +2% | 0 | 0 | — |
case-15 | fail→pass | 5,440 | 2,569 | -53% | 1 | 1 | 0% | 967 | 645 | -33% | 0 | 0 | — |
case-16 | fail→pass | 13,962 | 8,681 | -38% | 1 | 1 | 0% | 2,370 | 1,950 | -18% | 0 | 0 | — |
case-17 | fail→pass | 12,861 | 10,374 | -19% | 1 | 1 | 0% | 2,194 | 2,001 | -9% | 0 | 0 | — |
case-18 | fail→fail | 10,414 | 9,143 | -12% | 1 | 1 | 0% | 1,793 | 1,970 | +10% | 0 | 0 | — |
case-19 | pass→pass | 14,175 | 14,166 | -0% | 1 | 1 | 0% | 2,182 | 2,587 | +19% | 0 | 0 | — |
case-20 | fail→pass | 16,187 | 14,239 | -12% | 1 | 1 | 0% | 2,873 | 3,014 | +5% | 0 | 0 | — |
case-21 | pass→pass | 13,048 | 14,629 | +12% | 1 | 1 | 0% | 2,218 | 2,816 | +27% | 0 | 0 | — |
case-22 | pass→pass | 12,856 | 9,370 | -27% | 1 | 1 | 0% | 2,197 | 1,889 | -14% | 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 +50 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.