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Get Started Free →Use when validating or debugging a workflow in the embedded browser and you need a reproducible, evidence-first loop.
.claude/skills/holaboss-ai-browser-qa/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-08 | ✗→✓ | ▲ Improved | -36% | 0% |
| case-18 | ✗→✓ | ▲ Improved | -13% | 0% |
| case-21 | ✗→✓ | ▲ Improved | -45% | 0% |
| case-22 | ✗→✓ | ▲ Improved | -12% | 0% |
| case-04 | ✓→✗ | ▼ Worse | -58% | 0% |
Use this skill when the task is browser validation, repro, regression checking, or investigation in the workspace-embedded browser.
browser_act with wait_for over separate click then wait loops.browser_find when the target is known but compact state did not include it.browser_get_state detail=compact for orientation and detail=standard only when compact state is insufficient.data-* attributes, hrefs, or hydration data with a narrow read-only browser_evaluate before treating the fact as unavailable.browser_get_console, browser_get_errors, and browser_list_requests only after the cheaper orientation/action path failed or the issue is clearly runtime/network-related.browser_get_request only for one suspect request after browser_list_requests narrowed the field.browser_storage_get and browser_cookies_get to inspect auth or state flags before rerunning a long login flow.browser_storage_set and browser_cookies_set only for controlled state repair, not as a default shortcut.download_started and download_completed waits plus browser_list_downloads.| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 13,520 | 11,834 | -12% | 1 | 1 | 0% | 2,301 | 922 | -60% | 0 | 0 | — |
case-02 | fail→fail | 7,065 | 6,654 | -6% | 1 | 1 | 0% | 299 | 967 | +223% | 0 | 0 | — |
case-03 | fail→fail | 6,474 | 6,224 | -4% | 1 | 1 | 0% | 225 | 854 | +280% | 0 | 0 | — |
case-04 | pass→fail | 14,854 | 8,781 | -41% | 1 | 1 | 0% | 2,753 | 1,143 | -58% | 0 | 0 | — |
case-05 | pass→fail | 16,026 | 6,206 | -61% | 1 | 1 | 0% | 2,849 | 891 | -69% | 0 | 0 | — |
case-06 | pass→pass | 9,854 | 7,854 | -20% | 1 | 1 | 0% | 1,795 | 2,040 | +14% | 0 | 0 | — |
case-07 | pass→pass | 11,220 | 3,979 | -65% | 1 | 1 | 0% | 1,904 | 1,262 | -34% | 0 | 0 | — |
case-08 | fail→pass | 14,929 | 3,927 | -74% | 1 | 1 | 0% | 1,986 | 1,274 | -36% | 0 | 0 | — |
case-09 | pass→pass | 10,773 | 5,094 | -53% | 1 | 1 | 0% | 1,451 | 1,524 | +5% | 0 | 0 | — |
case-10 | pass→pass | 11,006 | 7,618 | -31% | 1 | 1 | 0% | 1,819 | 1,787 | -2% | 0 | 0 | — |
case-11 | pass→pass | 11,994 | 5,998 | -50% | 1 | 1 | 0% | 1,776 | 1,564 | -12% | 0 | 0 | — |
case-12 | pass→pass | 8,658 | 3,285 | -62% | 1 | 1 | 0% | 1,340 | 1,149 | -14% | 0 | 0 | — |
case-13 | pass→pass | 10,304 | 4,630 | -55% | 1 | 1 | 0% | 1,595 | 1,411 | -12% | 0 | 0 | — |
case-14 | fail→fail | 19,153 | 22,374 | +17% | 1 | 1 | 0% | 1,614 | 2,308 | +43% | 0 | 0 | — |
case-15 | fail→fail | 10,750 | 6,636 | -38% | 1 | 1 | 0% | 1,399 | 1,440 | +3% | 0 | 0 | — |
case-16 | pass→pass | 9,718 | 4,943 | -49% | 1 | 1 | 0% | 1,357 | 1,222 | -10% | 0 | 0 | — |
case-17 | pass→pass | 9,432 | 4,119 | -56% | 1 | 1 | 0% | 1,563 | 1,144 | -27% | 0 | 0 | — |
case-18 | fail→pass | 10,068 | 6,373 | -37% | 1 | 1 | 0% | 1,676 | 1,459 | -13% | 0 | 0 | — |
case-19 | pass→pass | 10,317 | 7,901 | -23% | 1 | 1 | 0% | 1,612 | 1,608 | -0% | 0 | 0 | — |
case-20 | pass→pass | 8,012 | 4,698 | -41% | 1 | 1 | 0% | 1,403 | 1,235 | -12% | 0 | 0 | — |
case-21 | fail→pass | 12,762 | 3,318 | -74% | 1 | 1 | 0% | 2,110 | 1,165 | -45% | 0 | 0 | — |
case-22 | fail→pass | 10,841 | 7,865 | -27% | 1 | 1 | 0% | 1,863 | 1,637 | -12% | 0 | 0 | — |
case-23 | fail→fail | 13,312 | 5,265 | -60% | 1 | 1 | 0% | 2,261 | 1,338 | -41% | 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, and 18 counted toward the lift figure. The other 5 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +9 percentage points is the difference between those two pass rates over the 18 comparable cases. 3 cases got worse with the skill loaded, and they are 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.