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Get Started Free →Extract ecosystem-level organization patterns (symbiosis, emergence, cycles, resilience).
.claude/skills/yogsoth-ai-ecosystem-pattern-extraction/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-20 | ✗→✓ | ▲ Improved | 405% | 0% |
| case-22 | ✓→✓ | ＝ Same ✓ | 0% | 0% |
| case-21 | ✓→✓ | ＝ Same ✓ | 89% | 0% |
| case-23 | ✓→✓ | ＝ Same ✓ | 14% | 0% |
| case-03 | ✗→✗ | ＝ Same ✗ | 360% | 0% |
Extract ecosystem-level organization patterns.
Subagent — spawned via subagent-spawning/spawn-agent skill.
Ecosystem pattern extraction requires understanding complex multi-species interactions, emergent properties, and system-level dynamics. Benefits from dedicated systems-thinking attention.
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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. |
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| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-03 | fail→fail | 7,770 | 39,004 | +402% | 1 | 1 | 0% | 1,201 | 5,519 | +360% | 0 | 0 | — |
case-01 | fail→fail | 8,864 | 56,321 | +535% | 1 | 1 | 0% | 1,310 | 4,340 | +231% | 0 | 0 | — |
case-02 | fail→fail | 18,455 | 16,213 | -12% | 1 | 1 | 0% | 2,901 | 2,442 | -16% | 0 | 0 | — |
case-04 | fail→fail | 11,288 | 12,010 | +6% | 1 | 1 | 0% | 1,734 | 1,957 | +13% | 0 | 0 | — |
case-05 | fail→fail | 14,628 | 49,048 | +235% | 1 | 1 | 0% | 2,233 | 2,819 | +26% | 0 | 0 | — |
case-06 | fail→fail | 21,982 | 27,066 | +23% | 1 | 1 | 0% | 3,432 | 4,309 | +26% | 0 | 0 | — |
case-07 | fail→fail | 16,580 | 29,088 | +75% | 1 | 1 | 0% | 3,090 | 4,396 | +42% | 0 | 0 | — |
case-08 | fail→fail | 42,596 | 34,664 | -19% | 1 | 1 | 0% | 8,222 | 5,875 | -29% | 0 | 0 | — |
case-09 | fail→fail | 23,370 | 27,415 | +17% | 1 | 1 | 0% | 3,161 | 4,058 | +28% | 0 | 0 | — |
case-10 | fail→fail | 18,742 | 34,767 | +86% | 1 | 1 | 0% | 2,389 | 5,320 | +123% | 0 | 0 | — |
case-11 | fail→fail | 19,762 | 26,031 | +32% | 1 | 1 | 0% | 3,227 | 4,053 | +26% | 0 | 0 | — |
case-12 | fail→fail | 11,310 | 24,448 | +116% | 1 | 1 | 0% | 1,702 | 3,519 | +107% | 0 | 0 | — |
case-13 | fail→fail | 19,605 | 36,835 | +88% | 1 | 1 | 0% | 3,380 | 5,736 | +70% | 0 | 0 | — |
case-14 | fail→fail | 17,408 | 12,243 | -30% | 1 | 1 | 0% | 2,806 | 1,785 | -36% | 0 | 0 | — |
case-15 | fail→fail | 17,853 | 30,769 | +72% | 1 | 1 | 0% | 2,693 | 5,015 | +86% | 0 | 0 | — |
case-16 | fail→fail | 31,572 | 48,027 | +52% | 1 | 1 | 0% | 4,338 | 7,957 | +83% | 0 | 0 | — |
case-22 | pass→pass | 15,128 | 15,281 | +1% | 1 | 1 | 0% | 2,904 | 2,913 | +0% | 0 | 0 | — |
case-17 | fail→fail | 18,605 | 19,586 | +5% | 1 | 1 | 0% | 3,006 | 3,123 | +4% | 0 | 0 | — |
case-18 | fail→fail | 14,481 | 5,404 | -63% | 1 | 1 | 0% | 2,239 | 327 | -85% | 0 | 0 | — |
case-19 | fail→fail | 23,344 | 5,764 | -75% | 1 | 1 | 0% | 3,523 | 494 | -86% | 0 | 0 | — |
case-20 | fail→pass | 8,375 | 32,447 | +287% | 1 | 1 | 0% | 1,221 | 6,168 | +405% | 0 | 0 | — |
case-21 | pass→pass | 8,347 | 14,885 | +78% | 1 | 1 | 0% | 1,433 | 2,704 | +89% | 0 | 0 | — |
case-23 | pass→pass | 6,440 | 6,456 | +0% | 1 | 1 | 0% | 1,265 | 1,439 | +14% | 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. 23 cases were attempted, and 20 counted toward the lift figure. The other 3 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +4 percentage points is the difference between those two pass rates over the 20 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.