Loading skill
Install any skill in seconds. Free to start, no credit card required.
Get Started Free →Identify unexplored viable regions in the morphological matrix where no existing methods operate.
.claude/skills/yogsoth-ai-white-space-identification/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-14 | ✗→✓ | ▲ Improved | 11% | 0% |
| case-01 | ✗→✓ | ▲ Improved | 9% | 0% |
| case-02 | ✗→✓ | ▲ Improved | -44% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 151% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 0% | 0% |
Identify matrix regions not covered by existing methods or solutions, then evaluate those regions for viability and novelty.
Map existing solutions onto the morphological matrix and identify uncovered regions using white-space-detection SOP. Annotate each gap with reason for non-coverage (overlooked, infeasible, or unexplored).
Evaluate identified white-space combinations for feasibility and novelty using combination-evaluation SOP. Score each on technical feasibility, market novelty, and implementation difficulty.
| Metric | Floor | |--------|-------| | Unexplored viable regions identified | ≥3 | | Regions evaluated for feasibility | all identified | | Novel viable combinations surfaced | ≥2 |
| SOP | Role | |-----|------| | white-space-detection | Stage 1 — detect uncovered matrix regions | | combination-evaluation | Stage 2 — evaluate feasibility and novelty |
<!-- BEGIN available-tables (generated) -->
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | combination-evaluation | Evaluate new combinations for feasibility and novelty | | white-space-detection | Identify matrix regions not covered by existing methods |
<!-- 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-14 | fail→pass | 23,025 | 27,669 | +20% | 1 | 1 | 0% | 3,697 | 4,109 | +11% | 0 | 0 | — |
case-01 | fail→pass | 27,448 | 30,282 | +10% | 1 | 1 | 0% | 4,504 | 4,927 | +9% | 0 | 0 | — |
case-07 | fail→fail | 27,748 | 25,879 | -7% | 1 | 1 | 0% | 3,318 | 3,875 | +17% | 0 | 0 | — |
case-02 | fail→pass | 54,398 | 32,002 | -41% | 1 | 1 | 0% | 8,245 | 4,645 | -44% | 0 | 0 | — |
case-03 | pass→fail | 37,076 | 29,921 | -19% | 1 | 1 | 0% | 3,245 | 4,952 | +53% | 0 | 0 | — |
case-04 | pass→fail | 24,002 | 30,077 | +25% | 1 | 1 | 0% | 3,700 | 4,909 | +33% | 0 | 0 | — |
case-05 | pass→fail | 22,523 | 38,674 | +72% | 1 | 1 | 0% | 2,794 | 6,293 | +125% | 0 | 0 | — |
case-06 | fail→pass | 9,734 | 26,936 | +177% | 1 | 1 | 0% | 1,606 | 4,024 | +151% | 0 | 0 | — |
case-08 | fail→fail | 18,418 | 31,886 | +73% | 1 | 1 | 0% | 2,099 | 5,228 | +149% | 0 | 0 | — |
case-09 | fail→fail | 22,093 | 23,928 | +8% | 1 | 1 | 0% | 2,648 | 3,617 | +37% | 0 | 0 | — |
case-10 | fail→fail | 25,341 | 33,789 | +33% | 1 | 1 | 0% | 3,366 | 5,400 | +60% | 0 | 0 | — |
case-11 | pass→fail | 22,242 | 13,329 | -40% | 1 | 1 | 0% | 3,980 | 2,788 | -30% | 0 | 0 | — |
case-12 | fail→pass | 23,449 | 19,677 | -16% | 1 | 1 | 0% | 3,733 | 3,729 | -0% | 0 | 0 | — |
case-13 | fail→fail | 28,095 | 21,531 | -23% | 1 | 1 | 0% | 3,026 | 3,080 | +2% | 0 | 0 | — |
case-15 | fail→fail | 25,066 | 27,467 | +10% | 1 | 1 | 0% | 3,197 | 5,256 | +64% | 0 | 0 | — |
case-16 | fail→fail | 14,698 | 17,238 | +17% | 1 | 1 | 0% | 2,405 | 3,420 | +42% | 0 | 0 | — |
case-17 | fail→fail | 20,506 | 22,221 | +8% | 1 | 1 | 0% | 3,426 | 3,333 | -3% | 0 | 0 | — |
case-18 | fail→pass | 23,147 | 23,451 | +1% | 1 | 1 | 0% | 3,174 | 3,860 | +22% | 0 | 0 | — |
case-19 | fail→fail | 23,500 | 23,509 | +0% | 1 | 1 | 0% | 3,168 | 3,774 | +19% | 0 | 0 | — |
case-20 | fail→fail | 30,920 | 22,102 | -29% | 1 | 1 | 0% | 3,150 | 3,151 | +0% | 0 | 0 | — |
case-21 | fail→fail | 30,531 | 27,014 | -12% | 1 | 1 | 0% | 3,932 | 3,866 | -2% | 0 | 0 | — |
case-22 | fail→fail | 20,060 | 22,412 | +12% | 1 | 1 | 0% | 2,434 | 3,098 | +27% | 0 | 0 | — |
case-23 | pass→pass | 43,916 | 32,579 | -26% | 1 | 1 | 0% | 6,542 | 4,821 | -26% | 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. The headline lift of +9 percentage points is the difference between those two pass rates over the 23 comparable cases. 4 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.