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Get Started Free →Optimize any form that is NOT signup or account registration — including lead capture, contact, demo request, application, survey, quote, and checkout forms. Use when the goal is to increase form completion rate, reduce friction, or improve lead quality without breaking compliance or downstream workflows.
.claude/skills/dokhacgiakhoa-form-cro/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 48% | 0% |
| case-11 | ✓→✗ | ▼ Worse | 43% | 0% |
| case-02 | ✓→✓ | ＝ Same ✓ | 51% | 0% |
| case-12 | ✓→✓ | ＝ Same ✓ | 50% | 0% |
| case-04 | ✓→✓ | ＝ Same ✓ | 96% | 0% |
You are an expert in form optimization and friction reduction. Your goal is to maximize form completion while preserving data usefulness.
You do not blindly reduce fields. You do not optimize forms in isolation from their business purpose. You do not assume more data equals better leads.
Before giving recommendations, calculate the Form Health & Friction Index.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 21,360 | 22,823 | +7% | 1 | 1 | 0% | 3,021 | 4,477 | +48% | 0 | 0 | — |
case-02 | pass→pass | 25,168 | 25,562 | +2% | 1 | 1 | 0% | 3,732 | 5,631 | +51% | 0 | 0 | — |
case-03 | fail→fail | 19,118 | 24,488 | +28% | 1 | 1 | 0% | 2,627 | 4,558 | +74% | 0 | 0 | — |
case-12 | pass→pass | 17,002 | 17,049 | +0% | 1 | 1 | 0% | 2,100 | 3,145 | +50% | 0 | 0 | — |
case-04 | pass→pass | 12,647 | 20,025 | +58% | 1 | 1 | 0% | 1,869 | 3,659 | +96% | 0 | 0 | — |
case-05 | pass→pass | 11,781 | 14,257 | +21% | 1 | 1 | 0% | 1,862 | 3,116 | +67% | 0 | 0 | — |
case-06 | pass→pass | 11,130 | 13,286 | +19% | 1 | 1 | 0% | 1,694 | 2,972 | +75% | 0 | 0 | — |
case-07 | pass→pass | 15,312 | 11,324 | -26% | 1 | 1 | 0% | 1,996 | 2,906 | +46% | 0 | 0 | — |
case-08 | pass→pass | 12,398 | 34,584 | +179% | 1 | 1 | 0% | 2,049 | 3,205 | +56% | 0 | 0 | — |
case-09 | pass→pass | 14,281 | 19,940 | +40% | 1 | 1 | 0% | 1,830 | 4,010 | +119% | 0 | 0 | — |
case-10 | pass→pass | 9,789 | 18,568 | +90% | 1 | 1 | 0% | 1,527 | 3,285 | +115% | 0 | 0 | — |
case-11 | pass→fail | 15,499 | 15,110 | -3% | 1 | 1 | 0% | 1,987 | 2,840 | +43% | 0 | 0 | — |
case-13 | pass→pass | 12,619 | 14,060 | +11% | 1 | 1 | 0% | 1,936 | 3,248 | +68% | 0 | 0 | — |
case-14 | pass→pass | 9,623 | 12,702 | +32% | 1 | 1 | 0% | 1,455 | 2,800 | +92% | 0 | 0 | — |
case-15 | pass→pass | 8,765 | 16,039 | +83% | 1 | 1 | 0% | 1,099 | 3,319 | +202% | 0 | 0 | — |
case-16 | pass→pass | 16,570 | 16,295 | -2% | 1 | 1 | 0% | 2,570 | 3,077 | +20% | 0 | 0 | — |
case-17 | pass→pass | 10,489 | 14,329 | +37% | 1 | 1 | 0% | 1,668 | 2,906 | +74% | 0 | 0 | — |
case-18 | pass→pass | 17,786 | 16,980 | -5% | 1 | 1 | 0% | 2,753 | 3,725 | +35% | 0 | 0 | — |
case-19 | pass→pass | 12,579 | 17,804 | +42% | 1 | 1 | 0% | 2,058 | 3,321 | +61% | 0 | 0 | — |
case-20 | fail→fail | 9,646 | 11,063 | +15% | 1 | 1 | 0% | 1,702 | 2,553 | +50% | 0 | 0 | — |
case-21 | pass→pass | 10,859 | 12,648 | +16% | 1 | 1 | 0% | 1,842 | 2,740 | +49% | 0 | 0 | — |
case-22 | pass→pass | 7,664 | 7,894 | +3% | 1 | 1 | 0% | 1,280 | 2,212 | +73% | 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.