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Get Started Free →Map parameter space, generate extreme values, test at boundaries, detect breakpoints, synthesize validity envelope.
.claude/skills/yogsoth-ai-boundary-probing/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-03 | ✗→✓ | ▲ Improved | 12% | 0% |
| case-06 | ✗→✓ | ▲ Improved | -35% | 0% |
| case-11 | ✗→✓ | ▲ Improved | -9% | 0% |
| case-12 | ✗→✓ | ▲ Improved | -43% | 0% |
| case-14 | ✗→✓ | ▲ Improved | -50% | 0% |
parameter-space-mapping to identify all dimensionsextreme-value-generation:breakpoint-detection at each extreme valuevalidity-envelope-construction to synthesize boundaries<!-- BEGIN available-tables (generated) -->
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | breakpoint-detection | Test a claim at extreme parameter values and detect the precise point where it breaks down. | | extreme-value-generation | Generate boundary and extreme test values for a given parameter dimension to stress-test claims. | | parameter-space-mapping | Identify all parameter dimensions along which a claim's validity might vary. | | stress-test-validity-envelope-construction | Synthesize breakpoints across dimensions into a coherent validity envelope for a claim. |
<!-- END available-tables (generated) -->
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 24,389 | 23,555 | -3% | 1 | 1 | 0% | 3,698 | 4,198 | +14% | 0 | 0 | — |
case-02 | fail→fail | 47,267 | 6,478 | -86% | 1 | 1 | 0% | 1,428 | 795 | -44% | 0 | 0 | — |
case-03 | fail→pass | 21,136 | 19,240 | -9% | 1 | 1 | 0% | 3,371 | 3,775 | +12% | 0 | 0 | — |
case-04 | fail→fail | 9,944 | 2,239 | -77% | 1 | 1 | 0% | 1,475 | 768 | -48% | 0 | 0 | — |
case-05 | pass→pass | 16,300 | 5,150 | -68% | 1 | 1 | 0% | 2,412 | 1,148 | -52% | 0 | 0 | — |
case-06 | fail→pass | 19,182 | 11,371 | -41% | 1 | 1 | 0% | 2,877 | 1,857 | -35% | 0 | 0 | — |
case-07 | pass→pass | 11,944 | 3,925 | -67% | 1 | 1 | 0% | 1,731 | 906 | -48% | 0 | 0 | — |
case-08 | pass→pass | 10,670 | 4,322 | -59% | 1 | 1 | 0% | 1,543 | 1,076 | -30% | 0 | 0 | — |
case-09 | pass→pass | 16,699 | 9,929 | -41% | 1 | 1 | 0% | 2,536 | 1,887 | -26% | 0 | 0 | — |
case-10 | fail→fail | 7,377 | 2,242 | -70% | 1 | 1 | 0% | 984 | 704 | -28% | 0 | 0 | — |
case-11 | fail→pass | 13,405 | 3,256 | -76% | 1 | 1 | 0% | 966 | 881 | -9% | 0 | 0 | — |
case-12 | fail→pass | 9,849 | 3,027 | -69% | 1 | 1 | 0% | 1,400 | 798 | -43% | 0 | 0 | — |
case-13 | pass→pass | 12,450 | 2,503 | -80% | 1 | 1 | 0% | 1,800 | 672 | -63% | 0 | 0 | — |
case-14 | fail→pass | 9,501 | 2,256 | -76% | 1 | 1 | 0% | 1,370 | 690 | -50% | 0 | 0 | — |
case-15 | pass→pass | 11,643 | 14,542 | +25% | 1 | 1 | 0% | 2,133 | 3,008 | +41% | 0 | 0 | — |
case-16 | pass→pass | 10,414 | 6,725 | -35% | 1 | 1 | 0% | 1,747 | 1,405 | -20% | 0 | 0 | — |
case-17 | pass→pass | 4,601 | 4,671 | +2% | 1 | 1 | 0% | 876 | 1,157 | +32% | 0 | 0 | — |
case-18 | fail→pass | 8,988 | 1,805 | -80% | 1 | 1 | 0% | 1,326 | 645 | -51% | 0 | 0 | — |
case-19 | pass→pass | 16,632 | 10,036 | -40% | 1 | 1 | 0% | 2,625 | 1,946 | -26% | 0 | 0 | — |
case-20 | pass→pass | 9,169 | 3,633 | -60% | 1 | 1 | 0% | 1,413 | 918 | -35% | 0 | 0 | — |
case-21 | pass→pass | 7,744 | 4,876 | -37% | 1 | 1 | 0% | 1,062 | 994 | -6% | 0 | 0 | — |
case-22 | pass→pass | 14,799 | 3,316 | -78% | 1 | 1 | 0% | 2,087 | 884 | -58% | 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 +27 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.