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.claude/skills/asymmetric-al-playwright-ci/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 18% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 11% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 27% | 0% |
| case-14 | ✗→✓ | ▲ Improved | 7% | 0% |
| case-07 | ✓→✗ | ▼ Worse | 16% | 0% |
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retries: 2 in CI only — surface flakiness in pipelines, not locallytraces: 'on-first-retry' — capture rich debugging artifacts without slowing every run--shard=N/M splits tests evenly; scale horizontally, not vertically~/.cache/ms-playwright keyed on Playwright versionmcr.microsoft.com/playwright:v* has all OS deps pre-installedglobalSetup, reuse storageState across workers| Provider | Guide | |---|---| | GitHub Actions | ci-github-actions.md | | GitLab CI | ci-gitlab.md | | CircleCI / Azure DevOps / Jenkins | ci-other.md |
| Topic | Guide | |---|---| | Parallel execution & sharding | parallel-and-sharding.md | | Docker & containers | docker-and-containers.md | | Multi-project config | projects-and-dependencies.md |
| Topic | Guide | |---|---| | Reports & artifacts | reporting-and-artifacts.md | | Code coverage | test-coverage.md | | Global setup/teardown | global-setup-teardown.md |
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-03 | pass→pass | 13,628 | 7,124 | -48% | 1 | 1 | 0% | 2,208 | 1,694 | -23% | 0 | 0 | — |
case-01 | fail→pass | 13,491 | 13,057 | -3% | 1 | 1 | 0% | 2,520 | 2,982 | +18% | 0 | 0 | — |
case-02 | fail→pass | 20,901 | 18,612 | -11% | 1 | 1 | 0% | 3,380 | 3,744 | +11% | 0 | 0 | — |
case-04 | pass→pass | 4,875 | 4,372 | -10% | 1 | 1 | 0% | 884 | 1,337 | +51% | 0 | 0 | — |
case-05 | pass→pass | 9,703 | 7,657 | -21% | 1 | 1 | 0% | 1,597 | 1,788 | +12% | 0 | 0 | — |
case-06 | pass→pass | 8,516 | 3,943 | -54% | 1 | 1 | 0% | 1,458 | 1,159 | -21% | 0 | 0 | — |
case-07 | pass→fail | 16,369 | 13,492 | -18% | 1 | 1 | 0% | 2,520 | 2,934 | +16% | 0 | 0 | — |
case-08 | pass→pass | 15,551 | 12,949 | -17% | 1 | 1 | 0% | 2,477 | 2,666 | +8% | 0 | 0 | — |
case-09 | pass→pass | 11,939 | 13,097 | +10% | 1 | 1 | 0% | 1,991 | 2,583 | +30% | 0 | 0 | — |
case-10 | pass→pass | 14,671 | 12,988 | -11% | 1 | 1 | 0% | 2,384 | 2,484 | +4% | 0 | 0 | — |
case-11 | fail→pass | 11,339 | 10,688 | -6% | 1 | 1 | 0% | 1,937 | 2,467 | +27% | 0 | 0 | — |
case-12 | pass→pass | 12,076 | 12,504 | +4% | 1 | 1 | 0% | 2,061 | 2,916 | +41% | 0 | 0 | — |
case-13 | pass→pass | 13,174 | 10,556 | -20% | 1 | 1 | 0% | 2,009 | 2,053 | +2% | 0 | 0 | — |
case-14 | fail→pass | 17,014 | 12,525 | -26% | 1 | 1 | 0% | 2,442 | 2,607 | +7% | 0 | 0 | — |
case-15 | pass→fail | 7,509 | 6,635 | -12% | 1 | 1 | 0% | 1,412 | 1,708 | +21% | 0 | 0 | — |
case-16 | pass→pass | 11,374 | 9,297 | -18% | 1 | 1 | 0% | 1,960 | 2,170 | +11% | 0 | 0 | — |
case-17 | pass→pass | 9,154 | 8,814 | -4% | 1 | 1 | 0% | 1,645 | 2,236 | +36% | 0 | 0 | — |
case-18 | pass→pass | 15,616 | 12,989 | -17% | 1 | 1 | 0% | 2,661 | 2,858 | +7% | 0 | 0 | — |
case-19 | pass→pass | 17,299 | 16,531 | -4% | 1 | 1 | 0% | 2,479 | 3,120 | +26% | 0 | 0 | — |
case-20 | pass→pass | 6,222 | 6,171 | -1% | 1 | 1 | 0% | 1,089 | 1,621 | +49% | 0 | 0 | — |
case-21 | pass→pass | 5,134 | 4,126 | -20% | 1 | 1 | 0% | 879 | 1,208 | +37% | 0 | 0 | — |
case-22 | pass→pass | 8,281 | 3,622 | -56% | 1 | 1 | 0% | 1,491 | 1,111 | -25% | 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. 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.