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Get Started Free →Apply life's principles as design constraints. Orchestrates ecosystem-pattern-extraction → evolution-mechanism-transfer → abstraction-to-design.
.claude/skills/yogsoth-ai-life-principles-application/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | -1% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 62% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 58% | 0% |
| case-09 | ✗→✓ | ▲ Improved | -7% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 24% | 0% |
Apply life's principles (adapt to changing conditions, integrate development with growth, be locally attuned and responsive, use life-friendly chemistry, be resource-efficient) as design constraints.
Extract organization patterns from relevant ecosystems using ecosystem-pattern-extraction SOP. Identify symbiosis, emergence, cycling, and resilience patterns.
Map evolution mechanisms to design operations using evolution-mechanism-transfer SOP. Translate selection pressure, variation generation, and fitness landscape concepts.
Abstract extracted patterns and mechanisms into actionable design constraints using abstraction-to-design SOP. Frame as "design must..." statements.
| Metric | Floor | |--------|-------| | Ecosystem patterns extracted | ≥3 | | Evolution mechanisms mapped | ≥2 | | Life principles applied as design constraints | ≥2 | | Design constraint statements | ≥4 |
| SOP | Role | |-----|------| | ecosystem-pattern-extraction | Stage 1 — extract ecosystem organization patterns | | evolution-mechanism-transfer | Stage 2 — map evolution→design operations | | abstraction-to-design | Stage 3 — abstract to design constraints | | emulation-generation | Post — generate solutions within constraints | | web-search | Support — search for life principles literature | | paper-overview | Support — find biomimicry principles research |
<!-- BEGIN available-tables (generated) -->
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | abstraction-to-design | Abstract biological principle to design principle. Bridge from biology to engineering. | | ecosystem-pattern-extraction | Extract ecosystem-level organization patterns (symbiosis, emergence, cycles, resilience). | | evolution-mechanism-transfer | Map evolution mechanisms to design operations. Translate selection, mutation, drift, radiation into design operators. |
<!-- 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-02 | fail→pass | 43,664 | 37,546 | -14% | 1 | 1 | 0% | 6,437 | 6,369 | -1% | 0 | 0 | — |
case-01 | fail→fail | 60,784 | 58,365 | -4% | 1 | 1 | 0% | 7,172 | 4,966 | -31% | 0 | 0 | — |
case-03 | fail→pass | 30,578 | 48,538 | +59% | 1 | 1 | 0% | 4,691 | 7,582 | +62% | 0 | 0 | — |
case-04 | pass→fail | 18,824 | 61,149 | +225% | 1 | 1 | 0% | 3,454 | 6,754 | +96% | 0 | 0 | — |
case-05 | pass→fail | 26,648 | 40,363 | +51% | 1 | 1 | 0% | 5,230 | 8,255 | +58% | 0 | 0 | — |
case-06 | pass→fail | 28,881 | 45,925 | +59% | 1 | 1 | 0% | 3,147 | 7,942 | +152% | 0 | 0 | — |
case-07 | pass→pass | 26,756 | 38,175 | +43% | 1 | 1 | 0% | 5,133 | 8,681 | +69% | 0 | 0 | — |
case-08 | fail→pass | 20,893 | 34,592 | +66% | 1 | 1 | 0% | 3,655 | 5,777 | +58% | 0 | 0 | — |
case-09 | fail→pass | 47,155 | 65,415 | +39% | 1 | 1 | 0% | 7,581 | 7,047 | -7% | 0 | 0 | — |
case-10 | fail→fail | 22,811 | 36,441 | +60% | 1 | 1 | 0% | 3,922 | 7,284 | +86% | 0 | 0 | — |
case-11 | fail→fail | 23,812 | 36,051 | +51% | 1 | 1 | 0% | 3,543 | 5,569 | +57% | 0 | 0 | — |
case-12 | fail→pass | 31,449 | 32,414 | +3% | 1 | 1 | 0% | 4,807 | 5,974 | +24% | 0 | 0 | — |
case-13 | fail→pass | 27,322 | 32,359 | +18% | 1 | 1 | 0% | 3,921 | 5,843 | +49% | 0 | 0 | — |
case-14 | fail→fail | 23,049 | 33,000 | +43% | 1 | 1 | 0% | 3,732 | 5,170 | +39% | 0 | 0 | — |
case-15 | fail→pass | 21,525 | 34,438 | +60% | 1 | 1 | 0% | 3,189 | 6,611 | +107% | 0 | 0 | — |
case-16 | fail→pass | 44,443 | 35,768 | -20% | 1 | 1 | 0% | 7,528 | 6,554 | -13% | 0 | 0 | — |
case-17 | fail→pass | 26,232 | 36,970 | +41% | 1 | 1 | 0% | 4,565 | 7,445 | +63% | 0 | 0 | — |
case-18 | fail→pass | 35,500 | 51,853 | +46% | 1 | 1 | 0% | 4,969 | 4,945 | -0% | 0 | 0 | — |
case-19 | fail→pass | 23,776 | 28,498 | +20% | 1 | 1 | 0% | 3,828 | 5,102 | +33% | 0 | 0 | — |
case-20 | fail→pass | 23,889 | 32,471 | +36% | 1 | 1 | 0% | 3,861 | 5,544 | +44% | 0 | 0 | — |
case-21 | fail→pass | 23,378 | 37,078 | +59% | 1 | 1 | 0% | 3,500 | 6,754 | +93% | 0 | 0 | — |
case-22 | fail→pass | 27,928 | 51,161 | +83% | 1 | 1 | 0% | 4,609 | 7,894 | +71% | 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 +50 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.