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Get Started Free →Use when evaluating test quality on modules containing business logic, calculation utilities, or state machines to determine whether the tests provide genuine defect detection.
.claude/skills/thedaviddias-mutation-testing/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-08 | ✗→✓ | ▲ Improved | -48% | 0% |
| case-18 | ✗→✓ | ▲ Improved | -44% | 0% |
| case-09 | ✓→✓ | = Same ✓ | 27% | 0% |
| case-07 | ✓→✓ | = Same ✓ | 14% | 0% |
| case-01 | ✓→✓ | = Same ✓ | 19% | 0% |
A codebase can have 90% line coverage and still ship critical bugs if tests only execute code without asserting meaningful outcomes. Mutation testing reveals these gaps by proving that your tests can distinguish correct code from subtly broken code — the standard that actually matters in production.
Assess whether this module has mutation testing configured and whether the mutation score on critical paths meets the project's quality threshold.
Set up Stryker for this module, run an initial mutation report, and improve tests to kill surviving mutants in the critical business logic paths.
Explain what mutation testing is, how Stryker works, and why mutation score is a more meaningful quality signal than code coverage alone.
Review the Stryker configuration and mutation report to identify surviving mutants in critical code paths, and suggest test improvements to kill them.
For full implementation details, code examples, and framework-specific guidance, see references/rule.md.
Rule page: https://frontendchecklist.io/en/rules/testing/mutation-testing
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-09 | pass→pass | 11,029 | 11,565 | +5% | 1 | 1 | 0% | 1,820 | 2,311 | +27% | 0 | 0 | — |
case-07 | pass→pass | 8,631 | 8,053 | -7% | 1 | 1 | 0% | 1,539 | 1,758 | +14% | 0 | 0 | — |
case-01 | pass→pass | 9,318 | 7,952 | -15% | 1 | 1 | 0% | 1,532 | 1,816 | +19% | 0 | 0 | — |
case-08 | fail→pass | 11,389 | 4,324 | -62% | 1 | 1 | 0% | 1,887 | 980 | -48% | 0 | 0 | — |
case-02 | pass→pass | 13,822 | 7,385 | -47% | 1 | 1 | 0% | 2,275 | 1,652 | -27% | 0 | 0 | — |
case-03 | pass→pass | 15,667 | 11,237 | -28% | 1 | 1 | 0% | 2,405 | 2,280 | -5% | 0 | 0 | — |
case-04 | pass→pass | 9,092 | 7,433 | -18% | 1 | 1 | 0% | 1,666 | 1,595 | -4% | 0 | 0 | — |
case-05 | pass→pass | 8,028 | 7,077 | -12% | 1 | 1 | 0% | 1,352 | 1,585 | +17% | 0 | 0 | — |
case-06 | pass→pass | 12,693 | 6,776 | -47% | 1 | 1 | 0% | 2,220 | 1,612 | -27% | 0 | 0 | — |
case-10 | pass→pass | 11,843 | 10,123 | -15% | 1 | 1 | 0% | 2,065 | 2,097 | +2% | 0 | 0 | — |
case-11 | fail→fail | 9,425 | 3,951 | -58% | 1 | 1 | 0% | 1,459 | 939 | -36% | 0 | 0 | — |
case-12 | pass→pass | 7,554 | 5,046 | -33% | 1 | 1 | 0% | 1,350 | 1,225 | -9% | 0 | 0 | — |
case-13 | pass→pass | 10,407 | 6,502 | -38% | 1 | 1 | 0% | 1,734 | 1,367 | -21% | 0 | 0 | — |
case-14 | pass→pass | 14,610 | 10,099 | -31% | 1 | 1 | 0% | 2,428 | 1,929 | -21% | 0 | 0 | — |
case-15 | pass→pass | 6,383 | 2,977 | -53% | 1 | 1 | 0% | 1,180 | 763 | -35% | 0 | 0 | — |
case-16 | pass→pass | 12,410 | 6,732 | -46% | 1 | 1 | 0% | 2,045 | 1,422 | -30% | 0 | 0 | — |
case-17 | pass→pass | 10,903 | 7,954 | -27% | 1 | 1 | 0% | 2,003 | 1,801 | -10% | 0 | 0 | — |
case-18 | fail→pass | 9,005 | 3,431 | -62% | 1 | 1 | 0% | 1,608 | 901 | -44% | 0 | 0 | — |
case-19 | pass→pass | 12,870 | 8,992 | -30% | 1 | 1 | 0% | 2,540 | 1,947 | -23% | 0 | 0 | — |
case-20 | pass→pass | 12,034 | 10,773 | -10% | 1 | 1 | 0% | 2,534 | 2,383 | -6% | 0 | 0 | — |
case-21 | pass→pass | 8,596 | 5,944 | -31% | 1 | 1 | 0% | 1,630 | 1,514 | -7% | 0 | 0 | — |
case-22 | pass→pass | 9,071 | 8,644 | -5% | 1 | 1 | 0% | 1,621 | 1,764 | +9% | 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.
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.