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Get Started Free →Expert Jest testing framework for JavaScript/TypeScript unit and integration testing
.claude/skills/a5c-ai-jest-testing/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 1101% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 43% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -15% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 6% | 0% |
| case-13 | ✗→✓ | ▲ Improved | -23% | 0% |
This skill provides expert-level capabilities for Jest-based testing, enabling comprehensive unit testing, snapshot testing, and integration with React Testing Library.
automation-framework.js - Test framework setupmutation-testing.js - Test quality assessmentcontinuous-testing.js - CI/CD integrationshift-left-testing.js - Early testing integrationjest - Test runner@testing-library/react - React testing utilitiests-jest - TypeScript support (optional)javascript{ kind: 'skill', skill: { name: 'jest-testing', context: { action: 'execute-tests', testPath: 'src/**/*.test.ts', coverage: true, coverageThreshold: { global: { branches: 80, functions: 80, lines: 80 } } } } }
The skill respects jest.config.js or jest.config.ts in the project root and can override settings as needed.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 3,800 | 29,462 | +675% | 1 | 1 | 0% | 184 | 2,209 | +1101% | 0 | 0 | — |
case-02 | fail→pass | 28,454 | 8,175 | -71% | 1 | 1 | 0% | 1,555 | 2,219 | +43% | 0 | 0 | — |
case-03 | fail→pass | 15,575 | 11,125 | -29% | 1 | 1 | 0% | 2,789 | 2,380 | -15% | 0 | 0 | — |
case-04 | pass→pass | 13,322 | 12,410 | -7% | 1 | 1 | 0% | 2,606 | 2,846 | +9% | 0 | 0 | — |
case-05 | fail→pass | 14,670 | 11,953 | -19% | 1 | 1 | 0% | 2,636 | 2,796 | +6% | 0 | 0 | — |
case-06 | pass→pass | 8,526 | 4,822 | -43% | 1 | 1 | 0% | 1,455 | 1,288 | -11% | 0 | 0 | — |
case-07 | pass→pass | 16,287 | 12,130 | -26% | 1 | 1 | 0% | 2,432 | 2,688 | +11% | 0 | 0 | — |
case-08 | fail→fail | 6,225 | 4,303 | -31% | 1 | 1 | 0% | 1,028 | 1,143 | +11% | 0 | 0 | — |
case-09 | pass→pass | 15,463 | 12,393 | -20% | 1 | 1 | 0% | 2,555 | 2,544 | -0% | 0 | 0 | — |
case-10 | pass→pass | 12,153 | 7,916 | -35% | 1 | 1 | 0% | 2,407 | 2,033 | -16% | 0 | 0 | — |
case-11 | pass→pass | 8,402 | 5,933 | -29% | 1 | 1 | 0% | 1,444 | 1,423 | -1% | 0 | 0 | — |
case-12 | pass→pass | 5,874 | 6,533 | +11% | 1 | 1 | 0% | 1,144 | 1,530 | +34% | 0 | 0 | — |
case-13 | fail→pass | 6,652 | 2,795 | -58% | 1 | 1 | 0% | 1,070 | 826 | -23% | 0 | 0 | — |
case-14 | fail→pass | 12,399 | 2,538 | -80% | 1 | 1 | 0% | 1,780 | 818 | -54% | 0 | 0 | — |
case-15 | fail→pass | 7,096 | 2,515 | -65% | 1 | 1 | 0% | 1,009 | 759 | -25% | 0 | 0 | — |
case-16 | pass→pass | 20,116 | 10,577 | -47% | 1 | 1 | 0% | 3,414 | 2,486 | -27% | 0 | 0 | — |
case-17 | pass→pass | 10,071 | 7,253 | -28% | 1 | 1 | 0% | 2,049 | 1,488 | -27% | 0 | 0 | — |
case-18 | pass→pass | 8,434 | 8,665 | +3% | 1 | 1 | 0% | 1,629 | 2,026 | +24% | 0 | 0 | — |
case-19 | pass→pass | 10,706 | 3,714 | -65% | 1 | 1 | 0% | 2,064 | 891 | -57% | 0 | 0 | — |
case-20 | pass→pass | 15,459 | 14,048 | -9% | 1 | 1 | 0% | 2,431 | 2,580 | +6% | 0 | 0 | — |
case-21 | pass→pass | 15,253 | 13,305 | -13% | 1 | 1 | 0% | 2,705 | 2,447 | -10% | 0 | 0 | — |
case-22 | pass→pass | 15,245 | 19,015 | +25% | 1 | 1 | 0% | 3,034 | 3,526 | +16% | 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, and 20 counted toward the lift figure. The other 2 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 +32 percentage points is the difference between those two pass rates over the 20 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.