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Get Started Free →Use when reviewing any process that spans multiple screens, steps, or modal stages. Follow the real user journey and compare fields across steps instead of reviewing screens in isolation.
.claude/skills/thedaviddias-redundant-entry/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 17% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 15% | 0% |
| case-02 | ✓→✓ | ＝ Same ✓ | 5% | 0% |
| case-03 | ✓→✓ | ＝ Same ✓ | 21% | 0% |
| case-04 | ✓→✓ | ＝ Same ✓ | -4% | 0% |
Re-entering the same information increases cognitive load, slows task completion, and introduces avoidable typing errors. Users with cognitive, motor, or speech-input limitations are affected first, but everyone benefits from fewer repeated fields.
Review this multi-step flow for information that is requested more than once in the same process. Flag fields that should be auto-populated or selectable from previously entered data.
Reuse previously entered information within the same process. Auto-populate repeat fields, add "same as" toggles where appropriate, and preserve data between steps so users do not need to retype it.
Explain WCAG 2.2 Redundant Entry, what counts as the same process, and when security or validity exceptions allow re-entry.
Review multi-step forms, checkout, onboarding, support, and account flows related to Avoid redundant entry in the same process. Flag exact steps where previously entered information is required again without auto-population or a selection mechanism.
For full implementation details, code examples, and framework-specific guidance, see references/rule.md.
Rule page: https://frontendchecklist.io/en/rules/accessibility/redundant-entry
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 12,050 | 11,195 | -7% | 1 | 1 | 0% | 2,219 | 2,588 | +17% | 0 | 0 | — |
case-02 | pass→pass | 9,489 | 7,792 | -18% | 1 | 1 | 0% | 1,646 | 1,725 | +5% | 0 | 0 | — |
case-03 | pass→pass | 13,602 | 11,882 | -13% | 1 | 1 | 0% | 2,148 | 2,601 | +21% | 0 | 0 | — |
case-04 | pass→pass | 10,045 | 6,828 | -32% | 1 | 1 | 0% | 1,659 | 1,592 | -4% | 0 | 0 | — |
case-05 | pass→pass | 10,173 | 7,335 | -28% | 1 | 1 | 0% | 1,753 | 1,595 | -9% | 0 | 0 | — |
case-06 | pass→pass | 9,848 | 7,382 | -25% | 1 | 1 | 0% | 1,759 | 1,702 | -3% | 0 | 0 | — |
case-11 | pass→pass | 13,790 | 8,891 | -36% | 1 | 1 | 0% | 2,385 | 2,197 | -8% | 0 | 0 | — |
case-07 | pass→pass | 7,922 | 5,786 | -27% | 1 | 1 | 0% | 1,363 | 1,330 | -2% | 0 | 0 | — |
case-08 | pass→pass | 8,589 | 7,619 | -11% | 1 | 1 | 0% | 1,538 | 1,948 | +27% | 0 | 0 | — |
case-09 | pass→pass | 11,349 | 9,073 | -20% | 1 | 1 | 0% | 1,976 | 1,828 | -7% | 0 | 0 | — |
case-10 | pass→pass | 9,492 | 6,159 | -35% | 1 | 1 | 0% | 1,660 | 1,449 | -13% | 0 | 0 | — |
case-12 | pass→pass | 10,538 | 8,044 | -24% | 1 | 1 | 0% | 1,860 | 1,828 | -2% | 0 | 0 | — |
case-13 | fail→pass | 9,737 | 8,967 | -8% | 1 | 1 | 0% | 1,715 | 1,975 | +15% | 0 | 0 | — |
case-14 | pass→pass | 12,623 | 10,352 | -18% | 1 | 1 | 0% | 2,151 | 2,232 | +4% | 0 | 0 | — |
case-15 | pass→pass | 13,628 | 11,762 | -14% | 1 | 1 | 0% | 2,199 | 2,215 | +1% | 0 | 0 | — |
case-16 | pass→pass | 12,552 | 11,024 | -12% | 1 | 1 | 0% | 2,392 | 2,624 | +10% | 0 | 0 | — |
case-17 | fail→fail | 13,875 | 9,059 | -35% | 1 | 1 | 0% | 2,492 | 1,881 | -25% | 0 | 0 | — |
case-18 | pass→pass | 12,565 | 9,128 | -27% | 1 | 1 | 0% | 2,097 | 2,019 | -4% | 0 | 0 | — |
case-19 | pass→pass | 12,158 | 7,254 | -40% | 1 | 1 | 0% | 1,988 | 1,737 | -13% | 0 | 0 | — |
case-20 | pass→pass | 7,210 | 5,854 | -19% | 1 | 1 | 0% | 1,449 | 1,538 | +6% | 0 | 0 | — |
case-21 | pass→pass | 15,449 | 12,605 | -18% | 1 | 1 | 0% | 2,788 | 2,840 | +2% | 0 | 0 | — |
case-22 | pass→pass | 11,058 | 11,661 | +5% | 1 | 1 | 0% | 2,110 | 2,401 | +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 +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.