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Get Started Free →Select random paper facet as creative stimulus. Uses genuine randomness in paper selection to break domain fixation.
.claude/skills/yogsoth-ai-random-paper-entry/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-16 | ✗→✓ | ▲ Improved | -76% | 0% |
| case-20 | ✗→✓ | ▲ Improved | -17% | 0% |
| case-04 | ✓→✓ | ＝ Same ✓ | 74% | 0% |
| case-05 | ✓→✓ | ＝ Same ✓ | 53% | 0% |
| case-06 | ✓→✓ | ＝ Same ✓ | 95% | 0% |
Select random paper facet as creative stimulus.
Subagent — spawned via subagent-spawning/spawn-agent skill.
Random paper selection requires genuine exploration without goal-directed filtering. The subagent must resist the temptation to select "relevant" papers and instead embrace true randomness, then force associations from whatever is found.
<!-- BEGIN available-tables (generated) -->
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 | fail→fail | 14,778 | 19,819 | +34% | 1 | 1 | 0% | 1,334 | 740 | -45% | 0 | 0 | — |
case-02 | fail→fail | 14,592 | 18,904 | +30% | 1 | 1 | 0% | 1,280 | 496 | -61% | 0 | 0 | — |
case-03 | fail→fail | 13,259 | 29,296 | +121% | 1 | 1 | 0% | 1,264 | 1,444 | +14% | 0 | 0 | — |
case-04 | pass→pass | 12,798 | 20,261 | +58% | 1 | 1 | 0% | 1,506 | 2,617 | +74% | 0 | 0 | — |
case-05 | pass→pass | 17,534 | 22,786 | +30% | 1 | 1 | 0% | 2,103 | 3,222 | +53% | 0 | 0 | — |
case-06 | pass→pass | 7,867 | 9,067 | +15% | 1 | 1 | 0% | 401 | 782 | +95% | 0 | 0 | — |
case-07 | pass→pass | 19,198 | 18,690 | -3% | 1 | 1 | 0% | 2,356 | 2,437 | +3% | 0 | 0 | — |
case-08 | fail→fail | 22,362 | 22,698 | +2% | 1 | 1 | 0% | 2,418 | 1,085 | -55% | 0 | 0 | — |
case-09 | fail→fail | 13,153 | 42,388 | +222% | 1 | 1 | 0% | 1,204 | 1,062 | -12% | 0 | 0 | — |
case-10 | fail→fail | 18,345 | 28,018 | +53% | 1 | 1 | 0% | 564 | 3,648 | +547% | 0 | 0 | — |
case-11 | fail→fail | 21,067 | 15,770 | -25% | 1 | 1 | 0% | 3,238 | 778 | -76% | 0 | 0 | — |
case-12 | fail→fail | 16,002 | 32,634 | +104% | 1 | 1 | 0% | 2,526 | 4,356 | +72% | 0 | 0 | — |
case-13 | fail→fail | 7,534 | 26,941 | +258% | 1 | 1 | 0% | 1,072 | 2,238 | +109% | 0 | 0 | — |
case-14 | fail→fail | 18,640 | 19,585 | +5% | 1 | 1 | 0% | 2,661 | 760 | -71% | 0 | 0 | — |
case-15 | fail→fail | 18,666 | 15,750 | -16% | 1 | 1 | 0% | 2,052 | 2,532 | +23% | 0 | 0 | — |
case-16 | fail→pass | 15,935 | 2,394 | -85% | 1 | 1 | 0% | 2,377 | 570 | -76% | 0 | 0 | — |
case-17 | fail→fail | 23,752 | 28,677 | +21% | 1 | 1 | 0% | 2,593 | 4,231 | +63% | 0 | 0 | — |
case-18 | fail→fail | 19,744 | 16,457 | -17% | 1 | 1 | 0% | 2,638 | 738 | -72% | 0 | 0 | — |
case-19 | fail→fail | 20,129 | 12,568 | -38% | 1 | 1 | 0% | 2,093 | 666 | -68% | 0 | 0 | — |
case-20 | fail→pass | 18,662 | 9,866 | -47% | 1 | 1 | 0% | 1,964 | 1,633 | -17% | 0 | 0 | — |
case-21 | fail→fail | 20,636 | 16,592 | -20% | 1 | 1 | 0% | 2,173 | 530 | -76% | 0 | 0 | — |
case-22 | fail→fail | 15,630 | 32,335 | +107% | 1 | 1 | 0% | 228 | 3,142 | +1278% | 0 | 0 | — |
case-23 | fail→fail | 22,374 | 34,073 | +52% | 1 | 1 | 0% | 2,387 | 996 | -58% | 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 10 counted toward the lift figure. The other 13 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 +9 percentage points is the difference between those two pass rates over the 10 comparable cases. 2 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.