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Get Started Free →Generate CI test matrix for Windows, macOS, and Linux combinations
.claude/skills/a5c-ai-cross-platform-test-matrix/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | -24% | 0% |
| case-13 | ✗→✓ | ▲ Improved | -28% | 0% |
| case-15 | ✗→✓ | ▲ Improved | -52% | 0% |
| case-07 | ✓→✗ | ▼ Worse | -17% | 0% |
| case-11 | ✓→✗ | ▼ Worse | -31% | 0% |
Generate CI/CD test matrix configurations for testing desktop applications across Windows, macOS, and Linux.
json{ "type": "object", "properties": { "projectPath": { "type": "string" }, "ciPlatform": { "enum": ["github-actions", "azure-devops", "gitlab-ci"] }, "platforms": { "type": "array" }, "architectures": { "type": "array" } }, "required": ["projectPath"] }
yamlname: Test Matrix on: [push, pull_request] jobs: test: strategy: fail-fast: false matrix: os: [ubuntu-latest, windows-latest, macos-latest] arch: [x64] include: - os: macos-latest arch: arm64 runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@v4 - name: Setup Node.js uses: actions/setup-node@v4 with: node-version: '20' architecture: ${{ matrix.arch }} - name: Install dependencies run: npm ci - name: Run tests run: npm test - name: Upload test results uses: actions/upload-artifact@v4 if: always() with: name: test-results-${{ matrix.os }}-${{ matrix.arch }} path: test-results/
playwright-electron-configdesktop-ci-architect agent| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 13,109 | 7,823 | -40% | 1 | 1 | 0% | 2,565 | 2,055 | -20% | 0 | 0 | — |
case-02 | fail→pass | 15,175 | 9,156 | -40% | 1 | 1 | 0% | 3,191 | 2,426 | -24% | 0 | 0 | — |
case-03 | pass→pass | 14,446 | 10,134 | -30% | 1 | 1 | 0% | 2,787 | 2,044 | -27% | 0 | 0 | — |
case-04 | pass→pass | 13,674 | 7,375 | -46% | 1 | 1 | 0% | 3,036 | 1,923 | -37% | 0 | 0 | — |
case-05 | pass→pass | 10,325 | 7,819 | -24% | 1 | 1 | 0% | 2,051 | 2,123 | +4% | 0 | 0 | — |
case-06 | pass→pass | 8,998 | 6,631 | -26% | 1 | 1 | 0% | 1,398 | 1,805 | +29% | 0 | 0 | — |
case-07 | pass→fail | 11,095 | 6,591 | -41% | 1 | 1 | 0% | 2,188 | 1,820 | -17% | 0 | 0 | — |
case-08 | fail→fail | 10,125 | 5,624 | -44% | 1 | 1 | 0% | 2,047 | 1,679 | -18% | 0 | 0 | — |
case-09 | pass→pass | 8,836 | 4,713 | -47% | 1 | 1 | 0% | 1,645 | 1,392 | -15% | 0 | 0 | — |
case-10 | pass→pass | 4,608 | 7,437 | +61% | 1 | 1 | 0% | 879 | 1,864 | +112% | 0 | 0 | — |
case-11 | pass→fail | 8,753 | 3,797 | -57% | 1 | 1 | 0% | 1,653 | 1,144 | -31% | 0 | 0 | — |
case-12 | fail→fail | 9,853 | 5,181 | -47% | 1 | 1 | 0% | 1,985 | 1,534 | -23% | 0 | 0 | — |
case-13 | fail→pass | 5,826 | 2,074 | -64% | 1 | 1 | 0% | 1,140 | 822 | -28% | 0 | 0 | — |
case-14 | pass→pass | 4,816 | 2,099 | -56% | 1 | 1 | 0% | 976 | 877 | -10% | 0 | 0 | — |
case-15 | fail→pass | 8,751 | 2,096 | -76% | 1 | 1 | 0% | 1,758 | 848 | -52% | 0 | 0 | — |
case-16 | pass→pass | 13,745 | 8,783 | -36% | 1 | 1 | 0% | 2,182 | 2,387 | +9% | 0 | 0 | — |
case-17 | pass→pass | 4,687 | 3,495 | -25% | 1 | 1 | 0% | 895 | 1,063 | +19% | 0 | 0 | — |
case-18 | pass→pass | 5,800 | 6,867 | +18% | 1 | 1 | 0% | 1,074 | 1,498 | +39% | 0 | 0 | — |
case-19 | pass→pass | 7,575 | 5,794 | -24% | 1 | 1 | 0% | 1,351 | 1,523 | +13% | 0 | 0 | — |
case-20 | pass→pass | 10,309 | 5,357 | -48% | 1 | 1 | 0% | 1,817 | 1,359 | -25% | 0 | 0 | — |
case-21 | pass→pass | 6,853 | 4,274 | -38% | 1 | 1 | 0% | 1,276 | 1,206 | -5% | 0 | 0 | — |
case-22 | pass→pass | 6,559 | 3,270 | -50% | 1 | 1 | 0% | 1,037 | 989 | -5% | 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 +5 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.