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Get Started Free →Use when auditing slow page loads, heavy assets, or rendering delays related to Optimize interaction to next paint. Verify the actual bottleneck in DevTools, Lighthouse, or field data before recommending changes.
.claude/skills/thedaviddias-interaction-to-next-paint/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-09 | ✗→✓ | ▲ Improved | 13% | 0% |
| case-15 | ✗→✓ | ▲ Improved | -15% | 0% |
| case-03 | ✓→✓ | ＝ Same ✓ | 4% | 0% |
| case-04 | ✓→✓ | ＝ Same ✓ | -24% | 0% |
| case-05 | ✓→✓ | ＝ Same ✓ | 16% | 0% |
INP replaced FID as a Core Web Vital in 2024—it measures how quickly your page responds to user interactions throughout the session, not just the first interaction.
Measure INP using Chrome DevTools or PageSpeed Insights. Verify interaction responses occur within 200ms.
Optimize event handlers, break up long tasks, use web workers for heavy computation, and reduce main thread blocking.
Explain how INP measures overall page responsiveness by tracking the latency of all user interactions during a page visit.
Review the routes, assets, and loading behavior that affect Optimize interaction to next 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/interaction-to-next-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 | 19,356 | 17,985 | -7% | 1 | 1 | 0% | 4,045 | 4,498 | +11% | 0 | 0 | — |
case-02 | fail→fail | 15,829 | 9,819 | -38% | 1 | 1 | 0% | 2,929 | 2,282 | -22% | 0 | 0 | — |
case-03 | pass→pass | 10,472 | 9,563 | -9% | 1 | 1 | 0% | 2,117 | 2,203 | +4% | 0 | 0 | — |
case-04 | pass→pass | 7,926 | 5,623 | -29% | 1 | 1 | 0% | 1,460 | 1,112 | -24% | 0 | 0 | — |
case-05 | pass→pass | 9,787 | 7,938 | -19% | 1 | 1 | 0% | 1,641 | 1,907 | +16% | 0 | 0 | — |
case-06 | pass→pass | 9,319 | 8,518 | -9% | 1 | 1 | 0% | 2,081 | 1,885 | -9% | 0 | 0 | — |
case-07 | fail→fail | 15,295 | 10,557 | -31% | 1 | 1 | 0% | 2,821 | 2,321 | -18% | 0 | 0 | — |
case-08 | pass→pass | 18,844 | 14,689 | -22% | 1 | 1 | 0% | 1,901 | 1,701 | -11% | 0 | 0 | — |
case-09 | fail→pass | 18,413 | 19,182 | +4% | 1 | 1 | 0% | 2,594 | 2,934 | +13% | 0 | 0 | — |
case-10 | pass→pass | 16,181 | 14,403 | -11% | 1 | 1 | 0% | 2,349 | 1,807 | -23% | 0 | 0 | — |
case-11 | pass→pass | 14,754 | 16,114 | +9% | 1 | 1 | 0% | 2,678 | 2,912 | +9% | 0 | 0 | — |
case-12 | pass→pass | 3,731 | 2,965 | -21% | 1 | 1 | 0% | 634 | 694 | +9% | 0 | 0 | — |
case-13 | pass→pass | 10,621 | 7,921 | -25% | 1 | 1 | 0% | 1,957 | 1,649 | -16% | 0 | 0 | — |
case-14 | pass→pass | 19,834 | 17,523 | -12% | 1 | 1 | 0% | 2,749 | 3,219 | +17% | 0 | 0 | — |
case-15 | fail→pass | 11,955 | 7,561 | -37% | 1 | 1 | 0% | 1,772 | 1,503 | -15% | 0 | 0 | — |
case-16 | pass→pass | 10,418 | 7,809 | -25% | 1 | 1 | 0% | 1,599 | 1,597 | -0% | 0 | 0 | — |
case-17 | pass→pass | 17,846 | 12,287 | -31% | 1 | 1 | 0% | 2,370 | 2,176 | -8% | 0 | 0 | — |
case-18 | pass→pass | 16,881 | 11,355 | -33% | 1 | 1 | 0% | 2,474 | 1,979 | -20% | 0 | 0 | — |
case-19 | fail→fail | 20,132 | 14,272 | -29% | 1 | 1 | 0% | 2,694 | 2,410 | -11% | 0 | 0 | — |
case-20 | pass→pass | 22,152 | 14,557 | -34% | 1 | 1 | 0% | 2,832 | 2,628 | -7% | 0 | 0 | — |
case-21 | pass→pass | 17,825 | 13,587 | -24% | 1 | 1 | 0% | 2,522 | 2,215 | -12% | 0 | 0 | — |
case-22 | pass→pass | 15,801 | 11,815 | -25% | 1 | 1 | 0% | 2,530 | 1,802 | -29% | 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.