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Get Started Free →SOP for documenting an intervention — what happens when a causal variable is manipulated.
.claude/skills/yogsoth-ai-intervention-page-creation/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-06 | ✗→✓ | ▲ Improved | 108% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 149% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 227% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 11% | 0% |
| case-22 | ✗→✓ | ▲ Improved | 1050% | 0% |
Document an intervention: what variable is manipulated, how, and what the predicted/observed effects are.
CC file write + vault_add_edge
claims/<intervention-slug>.md with frontmatter (type: claim, confidence, tags: intervention])[[dir/slug]] pointing to the target (dir/slug = target path minus .md). Place inline at semantically relevant location. Skip if already present.<HARD-GATE> Intervention must specify: target variable, direction of manipulation, predicted downstream effects. </HARD-GATE>
Returns: { path: string, target_variable: string, predicted_effects: string[] }
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 62,040 | 201,426 | +225% | 1 | 1 | 0% | 2,025 | 858 | -58% | 0 | 0 | — |
case-02 | fail→fail | 14,155 | 36,801 | +160% | 1 | 1 | 0% | 1,705 | 935 | -45% | 0 | 0 | — |
case-03 | fail→fail | 18,022 | 35,795 | +99% | 1 | 1 | 0% | 1,558 | 666 | -57% | 0 | 0 | — |
case-04 | fail→fail | 22,262 | 18,272 | -18% | 1 | 1 | 0% | 2,249 | 598 | -73% | 0 | 0 | — |
case-05 | fail→fail | 29,268 | 31,774 | +9% | 1 | 1 | 0% | 2,958 | 624 | -79% | 0 | 0 | — |
case-06 | fail→pass | 28,210 | 43,131 | +53% | 1 | 1 | 0% | 1,951 | 4,058 | +108% | 0 | 0 | — |
case-07 | pass→pass | 9,412 | 11,561 | +23% | 1 | 1 | 0% | 1,517 | 2,608 | +72% | 0 | 0 | — |
case-08 | fail→pass | 10,448 | 28,754 | +175% | 1 | 1 | 0% | 972 | 2,420 | +149% | 0 | 0 | — |
case-09 | pass→pass | 15,289 | 14,190 | -7% | 1 | 1 | 0% | 1,597 | 952 | -40% | 0 | 0 | — |
case-10 | pass→fail | 15,066 | 39,212 | +160% | 1 | 1 | 0% | 1,723 | 559 | -68% | 0 | 0 | — |
case-11 | pass→pass | 27,768 | 30,845 | +11% | 1 | 1 | 0% | 4,617 | 2,738 | -41% | 0 | 0 | — |
case-12 | fail→pass | 4,527 | 16,575 | +266% | 1 | 1 | 0% | 696 | 2,278 | +227% | 0 | 0 | — |
case-13 | pass→pass | 15,795 | 45,212 | +186% | 1 | 1 | 0% | 1,701 | 3,596 | +111% | 0 | 0 | — |
case-14 | pass→fail | 18,792 | 36,353 | +93% | 1 | 1 | 0% | 2,598 | 556 | -79% | 0 | 0 | — |
case-15 | fail→pass | 26,751 | 35,452 | +33% | 1 | 1 | 0% | 4,300 | 4,779 | +11% | 0 | 0 | — |
case-16 | pass→pass | 6,055 | 9,978 | +65% | 1 | 1 | 0% | 975 | 578 | -41% | 0 | 0 | — |
case-17 | pass→pass | 20,520 | 23,730 | +16% | 1 | 1 | 0% | 2,265 | 2,292 | +1% | 0 | 0 | — |
case-18 | fail→fail | 6,815 | 7,616 | +12% | 1 | 1 | 0% | 1,416 | 476 | -66% | 0 | 0 | — |
case-19 | pass→pass | 18,721 | 4,882 | -74% | 1 | 1 | 0% | 622 | 1,161 | +87% | 0 | 0 | — |
case-20 | pass→pass | 10,657 | 14,127 | +33% | 1 | 1 | 0% | 2,222 | 2,376 | +7% | 0 | 0 | — |
case-21 | fail→fail | 11,932 | 14,116 | +18% | 1 | 1 | 0% | 1,944 | 525 | -73% | 0 | 0 | — |
case-22 | fail→pass | 19,669 | 18,213 | -7% | 1 | 1 | 0% | 278 | 3,196 | +1050% | 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 12 counted toward the lift figure. The other 10 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 +14 percentage points is the difference between those two pass rates over the 12 comparable cases. 5 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.