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Get Started Free →Negate a claim, derive logical consequences step by step, detect whether a genuine contradiction or absurdity emerges.
.claude/skills/yogsoth-ai-contradiction-derivation/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-07 | ✗→✓ | ▲ Improved | -29% | 0% |
| case-09 | ✗→✓ | ▲ Improved | -59% | 0% |
| case-11 | ✓→✗ | ▼ Worse | -50% | 0% |
| case-20 | ✓→✗ | ▼ Worse | 36% | 0% |
| case-02 | ✓→✓ | ＝ Same ✓ | 8% | 0% |
claim-negation to produce ~Pdeductive-chain with ~P as premise, derive consequencescontradiction-detection to check for:<!-- BEGIN available-tables (generated) -->
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | claim-negation | Formally negate the core claim, producing the logical complement for reductio testing. | | contradiction-detection | Evaluate whether a derivation chain has reached a genuine contradiction, absurdity, or inconclusive state. | | deductive-chain | Derive logical consequences step by step from a given premise, building a traceable derivation chain. |
<!-- 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 | 17,158 | 15,567 | -9% | 1 | 1 | 0% | 3,045 | 3,272 | +7% | 0 | 0 | — |
case-02 | pass→pass | 5,539 | 4,553 | -18% | 1 | 1 | 0% | 1,042 | 1,122 | +8% | 0 | 0 | — |
case-03 | pass→pass | 17,220 | 5,159 | -70% | 1 | 1 | 0% | 2,868 | 1,201 | -58% | 0 | 0 | — |
case-04 | pass→pass | 11,162 | 5,126 | -54% | 1 | 1 | 0% | 1,805 | 1,081 | -40% | 0 | 0 | — |
case-05 | pass→pass | 12,424 | 7,012 | -44% | 1 | 1 | 0% | 1,925 | 1,472 | -24% | 0 | 0 | — |
case-06 | fail→fail | 11,349 | 9,916 | -13% | 1 | 1 | 0% | 1,779 | 1,834 | +3% | 0 | 0 | — |
case-07 | fail→pass | 9,751 | 4,605 | -53% | 1 | 1 | 0% | 1,430 | 1,015 | -29% | 0 | 0 | — |
case-08 | fail→fail | 7,633 | 1,574 | -79% | 1 | 1 | 0% | 1,157 | 508 | -56% | 0 | 0 | — |
case-09 | fail→pass | 12,747 | 3,098 | -76% | 1 | 1 | 0% | 2,052 | 840 | -59% | 0 | 0 | — |
case-10 | pass→pass | 11,105 | 4,123 | -63% | 1 | 1 | 0% | 1,688 | 1,030 | -39% | 0 | 0 | — |
case-11 | pass→fail | 13,153 | 3,997 | -70% | 1 | 1 | 0% | 1,907 | 945 | -50% | 0 | 0 | — |
case-12 | pass→pass | 8,746 | 2,419 | -72% | 1 | 1 | 0% | 1,511 | 709 | -53% | 0 | 0 | — |
case-13 | pass→pass | 12,679 | 2,021 | -84% | 1 | 1 | 0% | 2,431 | 621 | -74% | 0 | 0 | — |
case-14 | pass→pass | 8,683 | 3,252 | -63% | 1 | 1 | 0% | 1,246 | 860 | -31% | 0 | 0 | — |
case-15 | pass→pass | 13,594 | 5,257 | -61% | 1 | 1 | 0% | 2,061 | 1,122 | -46% | 0 | 0 | — |
case-16 | fail→fail | 12,956 | 2,485 | -81% | 1 | 1 | 0% | 1,868 | 680 | -64% | 0 | 0 | — |
case-17 | pass→pass | 6,325 | 4,151 | -34% | 1 | 1 | 0% | 1,119 | 1,121 | +0% | 0 | 0 | — |
case-18 | pass→pass | 12,814 | 4,309 | -66% | 1 | 1 | 0% | 1,855 | 952 | -49% | 0 | 0 | — |
case-19 | pass→pass | 12,849 | 7,073 | -45% | 1 | 1 | 0% | 2,335 | 1,613 | -31% | 0 | 0 | — |
case-20 | pass→fail | 11,670 | 15,432 | +32% | 1 | 1 | 0% | 2,378 | 3,232 | +36% | 0 | 0 | — |
case-21 | pass→pass | 9,940 | 8,336 | -16% | 1 | 1 | 0% | 2,297 | 2,014 | -12% | 0 | 0 | — |
case-22 | pass→pass | 10,911 | 6,672 | -39% | 1 | 1 | 0% | 2,138 | 1,568 | -27% | 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 0 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.