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Get Started Free →Percy visual testing platform integration for visual regression detection
.claude/skills/a5c-ai-percy-visual-testing/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 7% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -34% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 22% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -11% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 28% | 0% |
This skill provides expert-level capabilities for Percy-based visual testing, enabling snapshot capture, visual diff analysis, and seamless CI/CD integration.
visual-regression.js - Visual regression testinge2e-test-suite.js - E2E with visual validationcross-browser-testing.js - Cross-browser visual testing@percy/cli - Percy CLI@percy/playwright / @percy/cypress - Framework SDKsjavascript{ kind: 'skill', skill: { name: 'percy-visual', context: { action: 'capture-snapshots', testSuite: 'e2e', widths: [375, 768, 1280], branch: 'feature/new-design' } } }
The skill requires a Percy token and can be configured to work with various testing frameworks.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 16,579 | 25,047 | +51% | 1 | 1 | 0% | 3,165 | 3,374 | +7% | 0 | 0 | — |
case-02 | pass→pass | 16,017 | 9,952 | -38% | 1 | 1 | 0% | 2,848 | 2,304 | -19% | 0 | 0 | — |
case-03 | fail→pass | 28,109 | 13,349 | -53% | 1 | 1 | 0% | 4,712 | 3,120 | -34% | 0 | 0 | — |
case-04 | fail→fail | 11,485 | 6,611 | -42% | 1 | 1 | 0% | 2,034 | 1,639 | -19% | 0 | 0 | — |
case-05 | pass→pass | 7,727 | 3,302 | -57% | 1 | 1 | 0% | 1,399 | 979 | -30% | 0 | 0 | — |
case-06 | pass→pass | 12,596 | 11,401 | -9% | 1 | 1 | 0% | 2,204 | 2,482 | +13% | 0 | 0 | — |
case-07 | fail→pass | 12,747 | 12,513 | -2% | 1 | 1 | 0% | 2,336 | 2,839 | +22% | 0 | 0 | — |
case-08 | fail→pass | 11,183 | 6,712 | -40% | 1 | 1 | 0% | 2,069 | 1,846 | -11% | 0 | 0 | — |
case-09 | pass→pass | 16,226 | 14,698 | -9% | 1 | 1 | 0% | 2,716 | 2,631 | -3% | 0 | 0 | — |
case-10 | fail→pass | 14,804 | 16,918 | +14% | 1 | 1 | 0% | 2,369 | 3,036 | +28% | 0 | 0 | — |
case-11 | pass→pass | 9,640 | 9,536 | -1% | 1 | 1 | 0% | 1,686 | 2,101 | +25% | 0 | 0 | — |
case-12 | fail→pass | 12,800 | 14,282 | +12% | 1 | 1 | 0% | 2,282 | 2,933 | +29% | 0 | 0 | — |
case-13 | fail→pass | 14,308 | 12,684 | -11% | 1 | 1 | 0% | 2,604 | 2,335 | -10% | 0 | 0 | — |
case-14 | pass→pass | 16,711 | 14,719 | -12% | 1 | 1 | 0% | 2,796 | 3,126 | +12% | 0 | 0 | — |
case-15 | fail→pass | 16,971 | 15,551 | -8% | 1 | 1 | 0% | 3,474 | 2,839 | -18% | 0 | 0 | — |
case-16 | fail→pass | 12,522 | 12,605 | +1% | 1 | 1 | 0% | 2,431 | 2,340 | -4% | 0 | 0 | — |
case-17 | pass→pass | 6,411 | 2,883 | -55% | 1 | 1 | 0% | 944 | 828 | -12% | 0 | 0 | — |
case-18 | fail→pass | 12,684 | 11,845 | -7% | 1 | 1 | 0% | 2,208 | 2,416 | +9% | 0 | 0 | — |
case-19 | fail→pass | 17,655 | 17,391 | -1% | 1 | 1 | 0% | 2,716 | 3,372 | +24% | 0 | 0 | — |
case-20 | pass→fail | 6,039 | 7,316 | +21% | 1 | 1 | 0% | 1,208 | 1,635 | +35% | 0 | 0 | — |
case-21 | pass→pass | 10,011 | 5,950 | -41% | 1 | 1 | 0% | 1,603 | 1,595 | -0% | 0 | 0 | — |
case-22 | pass→fail | 16,277 | 18,555 | +14% | 1 | 1 | 0% | 2,905 | 4,028 | +39% | 0 | 0 | — |
case-23 | pass→pass | 13,355 | 15,852 | +19% | 1 | 1 | 0% | 2,535 | 2,710 | +7% | 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 +39 percentage points is the difference between those two pass rates over the 23 comparable cases. 2 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.