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Get Started Free →Strategy: Systematic factor removal — remove factors one at a time and observe whether the conclusion remains stable, identifying which factors are load-bearing.
.claude/skills/yogsoth-ai-factor-removal/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -55% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -47% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 27% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 4% | 0% |
| case-11 | ✗→✓ | ▲ Improved | -17% | 0% |
Ablation study approach: systematically remove each factor and observe conclusion stability.
| Parameter | S | M | L | |---|---|---|---| | Factors removed | 5 | 10 | 20 | | Removal iterations | 1 | 2 | 3 | | Combination removals | 0 | 3 | 8 |
factor-enumeration → [rank by suspected importance]
→ [for each factor]:
single-factor-removal
→ counterfactual-scenario-construction
→ fragility-measurement
→ [if budget allows, test combinations]:
single-factor-removal (multiple factors)
→ counterfactual-scenario-construction
→ load-bearing-identification (final ranking)<!-- BEGIN available-tables (generated) -->
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use | | --- | --- | | minimal-change-search | Tactic: Generate candidate changes, detect flip-points where conclusion reverses, measure fragility as distance to nearest flip. | | systematic-factor-ablation | Tactic: List all factors, remove one at a time, assess conclusion stability, rank factors by load-bearing importance. |
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | counterfactual-scenario-construction | Construct precise, internally consistent counterfactual scenarios where specified factors are altered, then reason about the resulting conclusion. | | factor-enumeration | List all key factors, conditions, and assumptions that support or enable the artifact's conclusion. | | flip-point-detection | Find the minimal change magnitude along a dimension that causes the conclusion to flip from true to false. | | fragility-measurement | Compute a fragility index from flip-point distances and degradation scores, summarizing how robust the conclusion is. | | load-bearing-identification | Identify which factors are "load-bearing walls" — factors whose removal would collapse the conclusion. | | single-factor-removal | Remove one specified factor from the artifact's support structure and reason about how the conclusion changes. |
<!-- 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-09 | pass→pass | 11,525 | 6,011 | -48% | 1 | 1 | 0% | 1,757 | 1,555 | -11% | 0 | 0 | — |
case-01 | fail→pass | 14,231 | 7,261 | -49% | 1 | 1 | 0% | 2,446 | 1,102 | -55% | 0 | 0 | — |
case-02 | pass→fail | 8,481 | 2,847 | -66% | 1 | 1 | 0% | 1,341 | 1,089 | -19% | 0 | 0 | — |
case-03 | fail→pass | 26,054 | 2,208 | -92% | 1 | 1 | 0% | 1,979 | 1,043 | -47% | 0 | 0 | — |
case-04 | pass→pass | 5,363 | 3,480 | -35% | 1 | 1 | 0% | 864 | 1,339 | +55% | 0 | 0 | — |
case-05 | fail→pass | 7,780 | 5,093 | -35% | 1 | 1 | 0% | 1,209 | 1,540 | +27% | 0 | 0 | — |
case-06 | pass→pass | 10,991 | 5,027 | -54% | 1 | 1 | 0% | 1,714 | 1,491 | -13% | 0 | 0 | — |
case-07 | pass→pass | 10,332 | 3,533 | -66% | 1 | 1 | 0% | 1,667 | 1,302 | -22% | 0 | 0 | — |
case-08 | fail→pass | 9,126 | 4,671 | -49% | 1 | 1 | 0% | 1,386 | 1,446 | +4% | 0 | 0 | — |
case-10 | pass→pass | 11,707 | 3,723 | -68% | 1 | 1 | 0% | 1,723 | 1,356 | -21% | 0 | 0 | — |
case-11 | fail→pass | 21,261 | 2,817 | -87% | 1 | 1 | 0% | 1,361 | 1,127 | -17% | 0 | 0 | — |
case-12 | fail→pass | 21,056 | 1,662 | -92% | 1 | 1 | 0% | 1,309 | 903 | -31% | 0 | 0 | — |
case-13 | fail→pass | 10,173 | 4,126 | -59% | 1 | 1 | 0% | 1,555 | 1,359 | -13% | 0 | 0 | — |
case-14 | pass→fail | 6,634 | 2,321 | -65% | 1 | 1 | 0% | 1,127 | 1,006 | -11% | 0 | 0 | — |
case-15 | pass→pass | 12,152 | 3,870 | -68% | 1 | 1 | 0% | 1,813 | 1,424 | -21% | 0 | 0 | — |
case-16 | pass→pass | 5,605 | 1,959 | -65% | 1 | 1 | 0% | 862 | 1,033 | +20% | 0 | 0 | — |
case-17 | fail→pass | 9,719 | 6,652 | -32% | 1 | 1 | 0% | 1,450 | 1,694 | +17% | 0 | 0 | — |
case-18 | fail→pass | 11,771 | 2,970 | -75% | 1 | 1 | 0% | 639 | 1,125 | +76% | 0 | 0 | — |
case-19 | fail→pass | 5,878 | 3,043 | -48% | 1 | 1 | 0% | 962 | 1,124 | +17% | 0 | 0 | — |
case-20 | pass→pass | 13,090 | 5,939 | -55% | 1 | 1 | 0% | 1,856 | 1,627 | -12% | 0 | 0 | — |
case-21 | pass→pass | 8,380 | 6,478 | -23% | 1 | 1 | 0% | 1,346 | 1,653 | +23% | 0 | 0 | — |
case-22 | fail→pass | 13,882 | 2,309 | -83% | 1 | 1 | 0% | 2,069 | 1,075 | -48% | 0 | 0 | — |
case-23 | fail→pass | 7,991 | 1,775 | -78% | 1 | 1 | 0% | 1,265 | 965 | -24% | 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. 23 cases were attempted, and 19 counted toward the lift figure. The other 4 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 +43 percentage points is the difference between those two pass rates over the 19 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.