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Get Started Free →Identify and classify sources of between-study heterogeneity (clinical, methodological, statistical)
.claude/skills/yogsoth-ai-heterogeneity-source-analysis/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-09 | ✗→✓ | ▲ Improved | -34% | 0% |
| case-12 | ✗→✓ | ▲ Improved | -15% | 0% |
| case-17 | ✗→✓ | ▲ Improved | -14% | 0% |
| case-21 | ✗→✓ | ▲ Improved | 6% | 0% |
| case-22 | ✗→✓ | ▲ Improved | -33% | 0% |
Systematically identify, classify, and prioritize potential sources of between-study heterogeneity for investigation via subgroup analysis and meta-regression.
study_characteristics: Study-level characteristics and covariateseffect_sizes: Extracted effect sizes showing variationmoderator_candidates: Pre-specified candidate moderator variablesClassified sources of heterogeneity with investigation priority and statistical analysis plan.
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Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | spawn-agent | Spawn a customized CC subagent with full MCP tool access. Used by SOPs that declare execution: subagent. |
<!-- 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 | pass→pass | 9,440 | 15,217 | +61% | 1 | 1 | 0% | 1,597 | 1,994 | +25% | 0 | 0 | — |
case-02 | pass→pass | 7,289 | 11,114 | +52% | 1 | 1 | 0% | 1,108 | 1,887 | +70% | 0 | 0 | — |
case-03 | fail→fail | 12,797 | 12,202 | -5% | 1 | 1 | 0% | 2,009 | 2,102 | +5% | 0 | 0 | — |
case-04 | pass→pass | 13,444 | 16,801 | +25% | 1 | 1 | 0% | 2,097 | 2,849 | +36% | 0 | 0 | — |
case-05 | pass→pass | 12,098 | 8,920 | -26% | 1 | 1 | 0% | 1,912 | 1,452 | -24% | 0 | 0 | — |
case-06 | pass→pass | 15,286 | 8,082 | -47% | 1 | 1 | 0% | 2,206 | 1,355 | -39% | 0 | 0 | — |
case-07 | pass→pass | 18,991 | 4,040 | -79% | 1 | 1 | 0% | 1,549 | 799 | -48% | 0 | 0 | — |
case-08 | fail→fail | 12,484 | 11,331 | -9% | 1 | 1 | 0% | 1,758 | 1,947 | +11% | 0 | 0 | — |
case-09 | fail→pass | 15,070 | 8,320 | -45% | 1 | 1 | 0% | 2,158 | 1,420 | -34% | 0 | 0 | — |
case-10 | pass→pass | 14,137 | 10,769 | -24% | 1 | 1 | 0% | 2,078 | 1,862 | -10% | 0 | 0 | — |
case-11 | pass→pass | 12,732 | 8,525 | -33% | 1 | 1 | 0% | 1,885 | 1,443 | -23% | 0 | 0 | — |
case-12 | fail→pass | 13,313 | 10,224 | -23% | 1 | 1 | 0% | 1,962 | 1,667 | -15% | 0 | 0 | — |
case-13 | fail→fail | 9,661 | 9,899 | +2% | 1 | 1 | 0% | 1,553 | 1,689 | +9% | 0 | 0 | — |
case-14 | pass→pass | 14,024 | 14,308 | +2% | 1 | 1 | 0% | 2,230 | 2,347 | +5% | 0 | 0 | — |
case-15 | pass→pass | 9,120 | 5,093 | -44% | 1 | 1 | 0% | 1,437 | 995 | -31% | 0 | 0 | — |
case-16 | pass→pass | 13,790 | 5,666 | -59% | 1 | 1 | 0% | 2,133 | 1,070 | -50% | 0 | 0 | — |
case-17 | fail→pass | 13,117 | 10,550 | -20% | 1 | 1 | 0% | 2,030 | 1,751 | -14% | 0 | 0 | — |
case-18 | pass→pass | 14,301 | 10,364 | -28% | 1 | 1 | 0% | 2,122 | 1,740 | -18% | 0 | 0 | — |
case-19 | pass→pass | 12,871 | 11,415 | -11% | 1 | 1 | 0% | 2,126 | 2,151 | +1% | 0 | 0 | — |
case-20 | pass→pass | 9,979 | 5,589 | -44% | 1 | 1 | 0% | 1,418 | 999 | -30% | 0 | 0 | — |
case-21 | fail→pass | 11,381 | 11,072 | -3% | 1 | 1 | 0% | 1,831 | 1,933 | +6% | 0 | 0 | — |
case-22 | fail→pass | 14,297 | 7,888 | -45% | 1 | 1 | 0% | 2,063 | 1,383 | -33% | 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.
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.