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Get Started Free →Challenge the evaluation criteria themselves using Assumption-based Planning, Critical Systems Heuristics, and Boundary Critique to ensure the framework is sound.
.claude/skills/yogsoth-ai-criteria-interrogation/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 37% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 12% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -25% | 0% |
| case-19 | ✗→✓ | ▲ Improved | 70% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 34% | 0% |
Purpose: Question whether the evaluation framework used during convergence was itself valid — exposing hidden biases in criteria selection, weighting, and boundary definitions that may have predetermined the outcome.
When to use:
| Metric | Minimum | |--------|---------| | Criteria challenged | All criteria used in convergence | | Alternative framings | >= 2 alternative criteria sets | | Boundary questions | >= 5 (who benefits, who is excluded) |
yamlcriteria_under_review: [] challenges_raised: {} alternative_framings: [] sensitivity_to_criteria_change: {} verdict: null # CRITERIA_SOUND | CRITERIA_BIASED | REWEIGHT
| Tactic | When to Deploy | |--------|---------------| | assumption-excavation | Default — treat criteria as assumptions to challenge | | multi-perspective-attack | When criteria privilege certain stakeholders |
yamlstrategy: criteria-interrogation criteria_reviewed: <count> challenges: - criterion: <name> challenge: <argument> severity: HIGH | MEDIUM | LOW alternative: <proposed change> sensitivity_result: ROBUST | FRAGILE verdict: CRITERIA_SOUND | CRITERIA_BIASED | REWEIGHT recommended_changes: []
<!-- BEGIN available-tables (generated) -->
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use | | --- | --- | | assumption-excavation | Systematic extraction, challenge, and sensitivity analysis of assumptions underlying a decision to identify load-bearing beliefs. | | multi-perspective-attack | Assign distinct perspectives to attack a decision from multiple angles, then synthesize findings into a unified assessment. |
<!-- 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-18 | pass→pass | 15,947 | 14,564 | -9% | 1 | 1 | 0% | 1,825 | 3,145 | +72% | 0 | 0 | — |
case-01 | fail→pass | 14,224 | 18,070 | +27% | 1 | 1 | 0% | 2,423 | 3,321 | +37% | 0 | 0 | — |
case-02 | fail→pass | 20,115 | 19,134 | -5% | 1 | 1 | 0% | 3,418 | 3,830 | +12% | 0 | 0 | — |
case-03 | fail→pass | 20,037 | 12,734 | -36% | 1 | 1 | 0% | 3,532 | 2,639 | -25% | 0 | 0 | — |
case-04 | pass→pass | 6,186 | 17,874 | +189% | 1 | 1 | 0% | 1,332 | 4,023 | +202% | 0 | 0 | — |
case-19 | fail→pass | 14,691 | 18,969 | +29% | 1 | 1 | 0% | 2,364 | 4,012 | +70% | 0 | 0 | — |
case-05 | pass→pass | 9,948 | 13,427 | +35% | 1 | 1 | 0% | 1,585 | 2,599 | +64% | 0 | 0 | — |
case-06 | pass→pass | 11,302 | 17,754 | +57% | 1 | 1 | 0% | 2,076 | 3,441 | +66% | 0 | 0 | — |
case-07 | fail→pass | 15,953 | 18,552 | +16% | 1 | 1 | 0% | 2,807 | 3,763 | +34% | 0 | 0 | — |
case-08 | fail→pass | 15,048 | 17,082 | +14% | 1 | 1 | 0% | 2,687 | 3,398 | +26% | 0 | 0 | — |
case-09 | fail→pass | 12,708 | 10,733 | -16% | 1 | 1 | 0% | 2,051 | 2,408 | +17% | 0 | 0 | — |
case-20 | fail→pass | 14,947 | 13,544 | -9% | 1 | 1 | 0% | 2,443 | 2,774 | +14% | 0 | 0 | — |
case-10 | pass→pass | 16,221 | 12,704 | -22% | 1 | 1 | 0% | 2,547 | 2,457 | -4% | 0 | 0 | — |
case-11 | pass→pass | 16,163 | 19,702 | +22% | 1 | 1 | 0% | 2,750 | 3,957 | +44% | 0 | 0 | — |
case-12 | fail→pass | 10,005 | 17,412 | +74% | 1 | 1 | 0% | 1,672 | 3,615 | +116% | 0 | 0 | — |
case-13 | fail→fail | 9,270 | 19,346 | +109% | 1 | 1 | 0% | 1,852 | 3,983 | +115% | 0 | 0 | — |
case-14 | fail→pass | 15,169 | 18,896 | +25% | 1 | 1 | 0% | 2,538 | 3,811 | +50% | 0 | 0 | — |
case-15 | fail→pass | 14,643 | 19,011 | +30% | 1 | 1 | 0% | 2,396 | 3,758 | +57% | 0 | 0 | — |
case-16 | fail→pass | 17,363 | 21,058 | +21% | 1 | 1 | 0% | 2,765 | 3,994 | +44% | 0 | 0 | — |
case-17 | pass→pass | 14,182 | 16,036 | +13% | 1 | 1 | 0% | 2,207 | 3,078 | +39% | 0 | 0 | — |
case-21 | fail→fail | 11,531 | 3,101 | -73% | 1 | 1 | 0% | 1,693 | 1,050 | -38% | 0 | 0 | — |
case-22 | fail→pass | 8,328 | 2,995 | -64% | 1 | 1 | 0% | 1,374 | 1,021 | -26% | 0 | 0 | — |
case-23 | pass→pass | 9,164 | 4,492 | -51% | 1 | 1 | 0% | 1,366 | 1,242 | -9% | 0 | 0 | — |
case-24 | fail→pass | 14,795 | 14,739 | -0% | 1 | 1 | 0% | 2,742 | 3,186 | +16% | 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. 24 cases were attempted. The headline lift of +58 percentage points is the difference between those two pass rates over the 24 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.