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Get Started Free →SOP: Build a multi-dimensional comparison matrix of competing hypotheses
.claude/skills/yogsoth-ai-hypothesis-comparison-matrix/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -26% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -32% | 0% |
| case-04 | ✗→✓ | ▲ Improved | -38% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -29% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 37% | 0% |
Build a multi-dimensional comparison matrix of competing hypotheses, highlight key differences, and support informed selection.
<HARD-GATE> Preconditions (all must hold before starting):
Not met → stop and return error: at least 2 hypotheses are required to build a comparison matrix. </HARD-GATE>
json{ "dimensions": ["mechanism_type", "evidence_support", "testability", "parsimony", "scope", "theoretical_basis"], "matrix": [ { "hypothesis_id": "H1", "label": "Primary Hypothesis", "statement": "...", "mechanism_type": "...", "evidence_support": "strong | moderate | weak | none", "testability": "high | medium | low", "parsimony": "high | medium | low", "scope": "broad | moderate | narrow", "theoretical_basis": "Theory name(s)" } ], "key_differentiators": ["Dimension where hypotheses differ most"], "recommendation": "Which hypothesis to prioritize and why", "caveats": "Important limitations of the comparison" }
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 20,384 | 21,976 | +8% | 1 | 1 | 0% | 3,035 | 2,256 | -26% | 0 | 0 | — |
case-02 | pass→pass | 15,137 | 10,138 | -33% | 1 | 1 | 0% | 2,290 | 2,139 | -7% | 0 | 0 | — |
case-03 | fail→pass | 19,575 | 10,198 | -48% | 1 | 1 | 0% | 3,256 | 2,229 | -32% | 0 | 0 | — |
case-04 | fail→pass | 9,455 | 2,885 | -69% | 1 | 1 | 0% | 1,362 | 847 | -38% | 0 | 0 | — |
case-05 | fail→fail | 19,414 | 13,902 | -28% | 1 | 1 | 0% | 2,924 | 2,561 | -12% | 0 | 0 | — |
case-06 | pass→pass | 10,305 | 8,333 | -19% | 1 | 1 | 0% | 1,707 | 1,923 | +13% | 0 | 0 | — |
case-07 | fail→pass | 20,607 | 10,268 | -50% | 1 | 1 | 0% | 2,836 | 2,016 | -29% | 0 | 0 | — |
case-08 | fail→pass | 10,994 | 10,444 | -5% | 1 | 1 | 0% | 1,556 | 2,124 | +37% | 0 | 0 | — |
case-09 | fail→fail | 18,745 | 12,763 | -32% | 1 | 1 | 0% | 2,617 | 2,276 | -13% | 0 | 0 | — |
case-10 | fail→fail | 13,816 | 9,837 | -29% | 1 | 1 | 0% | 1,899 | 2,096 | +10% | 0 | 0 | — |
case-11 | fail→pass | 11,656 | 9,766 | -16% | 1 | 1 | 0% | 1,573 | 2,007 | +28% | 0 | 0 | — |
case-12 | fail→fail | 11,588 | 6,924 | -40% | 1 | 1 | 0% | 1,783 | 1,616 | -9% | 0 | 0 | — |
case-13 | fail→pass | 15,844 | 9,874 | -38% | 1 | 1 | 0% | 2,481 | 1,916 | -23% | 0 | 0 | — |
case-14 | pass→pass | 17,617 | 9,271 | -47% | 1 | 1 | 0% | 2,468 | 1,787 | -28% | 0 | 0 | — |
case-15 | pass→pass | 21,170 | 11,744 | -45% | 1 | 1 | 0% | 2,895 | 2,137 | -26% | 0 | 0 | — |
case-16 | fail→pass | 14,428 | 6,247 | -57% | 1 | 1 | 0% | 2,491 | 1,455 | -42% | 0 | 0 | — |
case-17 | pass→pass | 10,676 | 9,747 | -9% | 1 | 1 | 0% | 1,597 | 2,014 | +26% | 0 | 0 | — |
case-18 | pass→pass | 10,821 | 10,427 | -4% | 1 | 1 | 0% | 1,529 | 2,176 | +42% | 0 | 0 | — |
case-19 | pass→pass | 18,319 | 8,728 | -52% | 1 | 1 | 0% | 2,622 | 1,802 | -31% | 0 | 0 | — |
case-20 | pass→fail | 11,587 | 2,612 | -77% | 1 | 1 | 0% | 1,839 | 772 | -58% | 0 | 0 | — |
case-21 | pass→fail | 12,439 | 2,319 | -81% | 1 | 1 | 0% | 1,718 | 727 | -58% | 0 | 0 | — |
case-22 | pass→fail | 7,471 | 3,105 | -58% | 1 | 1 | 0% | 1,213 | 888 | -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 +23 percentage points is the difference between those two pass rates over the 22 comparable cases. 3 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.