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Get Started Free →Import SOP: Quick paper scan, returns abstract and metadata (from literature-engine)
.claude/skills/yogsoth-ai-hypothesis-formation-paper-overview/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -67% | 0% |
| case-02 | ✗→✓ | ▲ Improved | -28% | 0% |
| case-04 | ✗→✓ | ▲ Improved | -77% | 0% |
| case-06 | ✗→✓ | ▲ Improved | -60% | 0% |
| case-10 | ✗→✓ | ▲ Improved | -85% | 0% |
Quick paper scan capability imported from the literature-engine skill library.
Directly call the paper-overview skill in literature-engine. This skill provides:
<!-- BEGIN available-tables (generated) -->
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | literature-overview | Quick landscape scan — discover papers on a topic without full-text reading |
<!-- 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→pass | 12,925 | 12,276 | -5% | 1 | 1 | 0% | 1,819 | 598 | -67% | 0 | 0 | — |
case-02 | fail→pass | 6,822 | 2,914 | -57% | 1 | 1 | 0% | 894 | 647 | -28% | 0 | 0 | — |
case-03 | pass→pass | 6,416 | 2,230 | -65% | 1 | 1 | 0% | 935 | 508 | -46% | 0 | 0 | — |
case-04 | fail→pass | 14,945 | 1,936 | -87% | 1 | 1 | 0% | 1,956 | 455 | -77% | 0 | 0 | — |
case-05 | pass→pass | 4,565 | 2,159 | -53% | 1 | 1 | 0% | 669 | 491 | -27% | 0 | 0 | — |
case-06 | fail→pass | 8,525 | 1,880 | -78% | 1 | 1 | 0% | 1,236 | 498 | -60% | 0 | 0 | — |
case-07 | fail→fail | 11,175 | 4,495 | -60% | 1 | 1 | 0% | 1,507 | 848 | -44% | 0 | 0 | — |
case-08 | fail→fail | 16,214 | 8,478 | -48% | 1 | 1 | 0% | 2,268 | 1,454 | -36% | 0 | 0 | — |
case-09 | pass→pass | 9,488 | 1,991 | -79% | 1 | 1 | 0% | 1,215 | 516 | -58% | 0 | 0 | — |
case-10 | fail→pass | 19,304 | 1,963 | -90% | 1 | 1 | 0% | 2,938 | 435 | -85% | 0 | 0 | — |
case-11 | fail→pass | 11,738 | 3,143 | -73% | 1 | 1 | 0% | 1,654 | 673 | -59% | 0 | 0 | — |
case-12 | fail→fail | 11,112 | 4,645 | -58% | 1 | 1 | 0% | 1,522 | 848 | -44% | 0 | 0 | — |
case-13 | fail→pass | 8,734 | 2,929 | -66% | 1 | 1 | 0% | 1,208 | 648 | -46% | 0 | 0 | — |
case-14 | fail→pass | 12,277 | 1,705 | -86% | 1 | 1 | 0% | 1,825 | 490 | -73% | 0 | 0 | — |
case-15 | pass→pass | 7,720 | 2,041 | -74% | 1 | 1 | 0% | 1,163 | 477 | -59% | 0 | 0 | — |
case-16 | pass→pass | 8,275 | 2,013 | -76% | 1 | 1 | 0% | 1,206 | 512 | -58% | 0 | 0 | — |
case-17 | fail→pass | 13,091 | 10,473 | -20% | 1 | 1 | 0% | 2,091 | 1,713 | -18% | 0 | 0 | — |
case-18 | fail→pass | 9,913 | 2,344 | -76% | 1 | 1 | 0% | 1,351 | 512 | -62% | 0 | 0 | — |
case-19 | pass→fail | 8,875 | 2,471 | -72% | 1 | 1 | 0% | 1,261 | 521 | -59% | 0 | 0 | — |
case-20 | fail→fail | 9,930 | 6,601 | -34% | 1 | 1 | 0% | 1,445 | 1,104 | -24% | 0 | 0 | — |
case-21 | pass→pass | 2,926 | 2,933 | +0% | 1 | 1 | 0% | 476 | 682 | +43% | 0 | 0 | — |
case-22 | pass→pass | 9,675 | 9,321 | -4% | 1 | 1 | 0% | 1,662 | 1,775 | +7% | 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 +41 percentage points is the difference between those two pass rates over the 22 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.