Loading skill
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Get Started Free →Design loading, skeleton, and progressive content reveal patterns.
.claude/skills/owl-listener-loading-states/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -4% | 0% |
| case-05 | ✗→✓ | ▲ Improved | -21% | 0% |
| case-02 | ✓→✓ | ＝ Same ✓ | -14% | 0% |
| case-03 | ✓→✓ | ＝ Same ✓ | -5% | 0% |
| case-04 | ✓→✓ | ＝ Same ✓ | -5% | 0% |
You are an expert in designing loading experiences that maintain user confidence and perceived performance.
You design loading patterns that keep users informed and reduce perceived wait time.
Show the layout shape before content loads. Use for known content structure. Animate with subtle shimmer.
Indeterminate spinner for unknown duration. Determinate progress bar when progress is measurable. Keep spinners small and unobtrusive.
Load critical content first, enhance progressively. Lazy-load below-fold content. Blur-up images (low-res placeholder to full).
Show the expected result immediately. Reconcile with server response. Roll back if the action fails.
Show placeholder text/images while loading. Use realistic proportions. Transition smoothly to real content.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 17,666 | 15,676 | -11% | 1 | 1 | 0% | 3,066 | 2,953 | -4% | 0 | 0 | — |
case-02 | pass→pass | 11,970 | 7,387 | -38% | 1 | 1 | 0% | 1,909 | 1,647 | -14% | 0 | 0 | — |
case-03 | pass→pass | 13,190 | 10,452 | -21% | 1 | 1 | 0% | 2,097 | 1,986 | -5% | 0 | 0 | — |
case-04 | pass→pass | 13,607 | 11,982 | -12% | 1 | 1 | 0% | 2,506 | 2,386 | -5% | 0 | 0 | — |
case-05 | fail→pass | 7,913 | 4,946 | -37% | 1 | 1 | 0% | 1,504 | 1,189 | -21% | 0 | 0 | — |
case-06 | pass→pass | 11,514 | 9,253 | -20% | 1 | 1 | 0% | 2,069 | 1,989 | -4% | 0 | 0 | — |
case-07 | pass→pass | 6,352 | 4,433 | -30% | 1 | 1 | 0% | 1,180 | 1,113 | -6% | 0 | 0 | — |
case-08 | pass→pass | 10,242 | 6,622 | -35% | 1 | 1 | 0% | 1,682 | 1,460 | -13% | 0 | 0 | — |
case-09 | pass→pass | 10,621 | 12,326 | +16% | 1 | 1 | 0% | 1,934 | 2,295 | +19% | 0 | 0 | — |
case-10 | pass→pass | 12,436 | 8,383 | -33% | 1 | 1 | 0% | 2,352 | 1,816 | -23% | 0 | 0 | — |
case-11 | pass→pass | 11,590 | 9,104 | -21% | 1 | 1 | 0% | 1,881 | 1,883 | +0% | 0 | 0 | — |
case-12 | pass→pass | 13,786 | 13,079 | -5% | 1 | 1 | 0% | 2,291 | 2,487 | +9% | 0 | 0 | — |
case-13 | pass→pass | 8,569 | 6,906 | -19% | 1 | 1 | 0% | 1,449 | 1,501 | +4% | 0 | 0 | — |
case-14 | pass→pass | 8,853 | 7,825 | -12% | 1 | 1 | 0% | 1,552 | 1,763 | +14% | 0 | 0 | — |
case-15 | pass→pass | 7,388 | 4,186 | -43% | 1 | 1 | 0% | 1,289 | 1,027 | -20% | 0 | 0 | — |
case-16 | pass→pass | 11,949 | 9,121 | -24% | 1 | 1 | 0% | 1,801 | 1,855 | +3% | 0 | 0 | — |
case-17 | pass→pass | 11,512 | 10,449 | -9% | 1 | 1 | 0% | 2,070 | 2,147 | +4% | 0 | 0 | — |
case-18 | pass→pass | 13,376 | 13,632 | +2% | 1 | 1 | 0% | 2,316 | 2,624 | +13% | 0 | 0 | — |
case-19 | pass→pass | 10,602 | 5,643 | -47% | 1 | 1 | 0% | 1,743 | 1,355 | -22% | 0 | 0 | — |
case-20 | pass→pass | 11,874 | 9,049 | -24% | 1 | 1 | 0% | 1,966 | 2,068 | +5% | 0 | 0 | — |
case-21 | pass→pass | 10,456 | 11,591 | +11% | 1 | 1 | 0% | 1,951 | 2,440 | +25% | 0 | 0 | — |
case-22 | pass→pass | 5,670 | 4,442 | -22% | 1 | 1 | 0% | 1,140 | 1,305 | +14% | 0 | 0 | — |
case-23 | pass→pass | 15,802 | 9,951 | -37% | 1 | 1 | 0% | 3,189 | 2,472 | -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. 23 cases were attempted. The headline lift of +9 percentage points is the difference between those two pass rates over the 23 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.