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Get Started Free →Scientific method scaffolding — hypothesis → experiment → evidence → conclusion. Use when you need rigorous causal reasoning rather than vibes-based debugging.
.claude/skills/evolution-foundation-dev-sciomc/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | -25% | 0% |
| case-04 | ✗→✓ | ▲ Improved | -5% | 0% |
| case-05 | ✗→✓ | ▲ Improved | -14% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -18% | 0% |
| case-10 | ✗→✓ | ▲ Improved | -24% | 0% |
Derived from oh-my-claudecode (MIT, Yeachan Heo). Adapted for the EvoNexus Engineering Layer.
Scientific method discipline applied to engineering investigations. Forces explicit hypothesis statement, experimental design, evidence collection, and provisional conclusions.
@hawk-debugger@scout-explorer@prism-scientist)Saved to workspace/development/research/[C]sciomc-{topic}-{date}.md:
markdown## Scientific Investigation — {topic} ### Hypothesis {Falsifiable claim} ### Experimental Design - Dependent variable: {what we measure} - Independent variables: {what we vary} - Controls: {what we hold constant} - Sample size: {N} ### Method {Step-by-step protocol} ### Results {Raw data summary} ### Statistical Analysis [delegated to @prism-scientist] ### Conclusion {Provisional conclusion + limitations} ### Follow-ups - {next experiment}
@prism-scientist (for statistical analysis)@trail-tracer (when investigation is causal)@apex-architect (when conclusion implies architecture change)| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 34,721 | 23,492 | -32% | 1 | 1 | 0% | 6,223 | 4,511 | -28% | 0 | 0 | — |
case-02 | fail→pass | 28,885 | 17,827 | -38% | 1 | 1 | 0% | 4,843 | 3,652 | -25% | 0 | 0 | — |
case-03 | fail→fail | 34,785 | 27,500 | -21% | 1 | 1 | 0% | 6,218 | 5,557 | -11% | 0 | 0 | — |
case-04 | fail→pass | 9,910 | 7,590 | -23% | 1 | 1 | 0% | 1,741 | 1,655 | -5% | 0 | 0 | — |
case-05 | fail→pass | 17,512 | 11,846 | -32% | 1 | 1 | 0% | 2,622 | 2,256 | -14% | 0 | 0 | — |
case-06 | pass→pass | 6,493 | 5,198 | -20% | 1 | 1 | 0% | 1,057 | 1,312 | +24% | 0 | 0 | — |
case-07 | pass→pass | 13,099 | 7,846 | -40% | 1 | 1 | 0% | 2,219 | 1,770 | -20% | 0 | 0 | — |
case-08 | fail→pass | 19,862 | 13,456 | -32% | 1 | 1 | 0% | 3,162 | 2,583 | -18% | 0 | 0 | — |
case-09 | pass→pass | 14,838 | 9,679 | -35% | 1 | 1 | 0% | 2,315 | 1,940 | -16% | 0 | 0 | — |
case-10 | fail→pass | 21,822 | 13,457 | -38% | 1 | 1 | 0% | 4,009 | 3,050 | -24% | 0 | 0 | — |
case-11 | fail→pass | 9,843 | 5,434 | -45% | 1 | 1 | 0% | 1,525 | 1,283 | -16% | 0 | 0 | — |
case-12 | fail→pass | 10,028 | 2,986 | -70% | 1 | 1 | 0% | 1,494 | 972 | -35% | 0 | 0 | — |
case-13 | fail→pass | 10,840 | 3,271 | -70% | 1 | 1 | 0% | 1,691 | 1,011 | -40% | 0 | 0 | — |
case-14 | fail→pass | 9,843 | 1,890 | -81% | 1 | 1 | 0% | 1,507 | 762 | -49% | 0 | 0 | — |
case-15 | pass→pass | 13,620 | 7,801 | -43% | 1 | 1 | 0% | 2,283 | 1,787 | -22% | 0 | 0 | — |
case-16 | fail→fail | 22,744 | 14,271 | -37% | 1 | 1 | 0% | 3,263 | 2,750 | -16% | 0 | 0 | — |
case-17 | fail→pass | 18,065 | 14,260 | -21% | 1 | 1 | 0% | 2,733 | 2,735 | +0% | 0 | 0 | — |
case-18 | pass→pass | 15,096 | 8,715 | -42% | 1 | 1 | 0% | 2,503 | 2,031 | -19% | 0 | 0 | — |
case-19 | pass→pass | 10,301 | 7,499 | -27% | 1 | 1 | 0% | 1,461 | 1,530 | +5% | 0 | 0 | — |
case-20 | fail→pass | 17,996 | 19,984 | +11% | 1 | 1 | 0% | 2,866 | 3,467 | +21% | 0 | 0 | — |
case-21 | pass→pass | 16,856 | 9,826 | -42% | 1 | 1 | 0% | 2,513 | 2,077 | -17% | 0 | 0 | — |
case-22 | fail→pass | 9,465 | 2,413 | -75% | 1 | 1 | 0% | 1,460 | 843 | -42% | 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 +55 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.