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Get Started Free →Inject artificial constraints to force creative divergence. Generates and applies constraints (resource, time, material, audience, scale) to existing ideas to produce variants.
.claude/skills/yogsoth-ai-constraint-injection/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-16 | ✗→✓ | ▲ Improved | -63% | 0% |
| case-18 | ✗→✓ | ▲ Improved | -38% | 0% |
| case-22 | ✗→✓ | ▲ Improved | 42% | 0% |
| case-20 | ✓→✗ | ▼ Worse | 182% | 0% |
| case-17 | ✓→✓ | ＝ Same ✓ | 31% | 0% |
Inject artificial constraints to force creative divergence.
Subagent — spawned via subagent-spawning/spawn-agent skill.
Constraint injection requires generating appropriate constraints AND then reasoning about how to satisfy the problem under those constraints. The two-phase process benefits from dedicated attention.
<!-- 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 | 19,971 | 37,490 | +88% | 1 | 1 | 0% | 2,706 | 4,879 | +80% | 0 | 0 | — |
case-02 | fail→fail | 25,804 | 38,195 | +48% | 1 | 1 | 0% | 3,270 | 5,132 | +57% | 0 | 0 | — |
case-03 | fail→fail | 21,149 | 32,492 | +54% | 1 | 1 | 0% | 2,512 | 4,630 | +84% | 0 | 0 | — |
case-04 | fail→fail | 17,739 | 17,640 | -1% | 1 | 1 | 0% | 2,527 | 2,518 | -0% | 0 | 0 | — |
case-05 | fail→fail | 21,681 | 23,744 | +10% | 1 | 1 | 0% | 2,632 | 749 | -72% | 0 | 0 | — |
case-06 | fail→fail | 21,878 | 47,122 | +115% | 1 | 1 | 0% | 3,205 | 6,804 | +112% | 0 | 0 | — |
case-07 | fail→fail | 19,750 | 46,687 | +136% | 1 | 1 | 0% | 2,526 | 4,923 | +95% | 0 | 0 | — |
case-08 | fail→fail | 18,975 | 25,602 | +35% | 1 | 1 | 0% | 2,709 | 3,916 | +45% | 0 | 0 | — |
case-09 | fail→fail | 18,065 | 39,255 | +117% | 1 | 1 | 0% | 2,212 | 5,678 | +157% | 0 | 0 | — |
case-10 | fail→fail | 30,935 | 37,898 | +23% | 1 | 1 | 0% | 4,040 | 4,818 | +19% | 0 | 0 | — |
case-11 | fail→fail | 16,976 | 31,028 | +83% | 1 | 1 | 0% | 2,521 | 3,259 | +29% | 0 | 0 | — |
case-12 | fail→fail | 18,341 | 16,725 | -9% | 1 | 1 | 0% | 2,627 | 869 | -67% | 0 | 0 | — |
case-13 | fail→fail | 22,449 | 20,713 | -8% | 1 | 1 | 0% | 3,010 | 3,165 | +5% | 0 | 0 | — |
case-14 | fail→fail | 24,035 | 33,756 | +40% | 1 | 1 | 0% | 3,410 | 5,394 | +58% | 0 | 0 | — |
case-15 | fail→fail | 19,206 | 24,609 | +28% | 1 | 1 | 0% | 2,525 | 3,193 | +26% | 0 | 0 | — |
case-16 | fail→pass | 10,240 | 2,526 | -75% | 1 | 1 | 0% | 1,258 | 464 | -63% | 0 | 0 | — |
case-17 | pass→pass | 9,111 | 9,090 | -0% | 1 | 1 | 0% | 1,339 | 1,757 | +31% | 0 | 0 | — |
case-18 | fail→pass | 30,370 | 4,908 | -84% | 1 | 1 | 0% | 1,232 | 763 | -38% | 0 | 0 | — |
case-19 | pass→pass | 18,213 | 2,368 | -87% | 1 | 1 | 0% | 1,844 | 405 | -78% | 0 | 0 | — |
case-20 | pass→fail | 19,402 | 49,114 | +153% | 1 | 1 | 0% | 2,526 | 7,133 | +182% | 0 | 0 | — |
case-21 | pass→pass | 20,437 | 35,081 | +72% | 1 | 1 | 0% | 3,422 | 5,584 | +63% | 0 | 0 | — |
case-22 | fail→pass | 22,133 | 17,499 | -21% | 1 | 1 | 0% | 1,880 | 2,664 | +42% | 0 | 0 | — |
case-23 | pass→pass | 7,108 | 13,491 | +90% | 1 | 1 | 0% | 1,157 | 2,780 | +140% | 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 21 counted toward the lift figure. The other 2 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 21 comparable cases. 1 case got worse with the skill loaded, and it is 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.