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Get Started Free →Answer per-domain signalling questions (5-value scale: Yes/Probably yes/Probably no/No/No information) for RoB2, ROBINS-I, or QUADAS-2, per whichever variant study-design-tool-gate dispatched to. Use this after study-design-tool-gate has dispatched to one of these three tools; this SOP produces only the raw signalling answers, not any domain-level or overall roll-up — that happens in domain-level-judgment next.
.claude/skills/yogsoth-ai-signalling-question-answering/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-06 | ✗→✓ | ▲ Improved | 16% | 0% |
| case-05 | ✓→✓ | = Same ✓ | 40% | 0% |
| case-19 | ✓→✓ | = Same ✓ | -48% | 0% |
| case-20 | ✓→✓ | = Same ✓ | -23% | 0% |
| case-21 | ✓→✓ | = Same ✓ | 49% | 0% |
Raw per-domain 5-value signalling answers for RoB2/ROBINS-I/QUADAS-2. First of two algorithmic levels these tools require — see domain-level-judgment for the second.
Subagent — spawned via spawn-agent skill.
NOS's star-awarding and AMSTAR-2's checklist items are NOT this SOP's concern — an earlier graph draft conflated "answer a domain question" with "award a star" and "check a checklist item," but these are three structurally different actions (5-value signalling judgment vs. binary star-or-not vs. checklist Yes/No/Partial). Keep this SOP scoped to exactly RoB2/ROBINS-I/QUADAS-2's signalling questions.
<!-- BEGIN available-tables (generated) -->
| SOP | When to use | | --- | --- | | spawn-agent | Spawn a customized CC subagent with full MCP tool access. |
<!-- 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 | 13,416 | 32,054 | +139% | 1 | 1 | 0% | 1,369 | 2,190 | +60% | 0 | 0 | — |
case-02 | fail→fail | 17,726 | 30,119 | +70% | 1 | 1 | 0% | 2,006 | 4,783 | +138% | 0 | 0 | — |
case-03 | fail→fail | 13,018 | 11,800 | -9% | 1 | 1 | 0% | 1,519 | 1,344 | -12% | 0 | 0 | — |
case-04 | fail→fail | 18,954 | 17,998 | -5% | 1 | 1 | 0% | 2,067 | 2,429 | +18% | 0 | 0 | — |
case-05 | pass→pass | 14,494 | 17,509 | +21% | 1 | 1 | 0% | 1,660 | 2,323 | +40% | 0 | 0 | — |
case-06 | fail→pass | 20,769 | 21,120 | +2% | 1 | 1 | 0% | 2,536 | 2,943 | +16% | 0 | 0 | — |
case-07 | fail→fail | 16,142 | 8,283 | -49% | 1 | 1 | 0% | 2,095 | 817 | -61% | 0 | 0 | — |
case-08 | fail→fail | 19,908 | 14,770 | -26% | 1 | 1 | 0% | 2,543 | 2,133 | -16% | 0 | 0 | — |
case-09 | fail→fail | 14,925 | 38,705 | +159% | 1 | 1 | 0% | 1,781 | 4,437 | +149% | 0 | 0 | — |
case-10 | fail→fail | 13,812 | 24,031 | +74% | 1 | 1 | 0% | 1,637 | 3,277 | +100% | 0 | 0 | — |
case-11 | fail→fail | 22,741 | 23,815 | +5% | 1 | 1 | 0% | 2,807 | 3,587 | +28% | 0 | 0 | — |
case-12 | fail→fail | 18,501 | 18,021 | -3% | 1 | 1 | 0% | 2,323 | 2,701 | +16% | 0 | 0 | — |
case-13 | fail→fail | 17,278 | 15,596 | -10% | 1 | 1 | 0% | 2,138 | 2,273 | +6% | 0 | 0 | — |
case-14 | fail→fail | 21,118 | 23,232 | +10% | 1 | 1 | 0% | 2,680 | 3,241 | +21% | 0 | 0 | — |
case-15 | fail→fail | 21,406 | 18,223 | -15% | 1 | 1 | 0% | 2,708 | 2,494 | -8% | 0 | 0 | — |
case-16 | fail→fail | 20,183 | 23,459 | +16% | 1 | 1 | 0% | 2,566 | 3,350 | +31% | 0 | 0 | — |
case-17 | fail→fail | 24,870 | 20,867 | -16% | 1 | 1 | 0% | 3,682 | 3,045 | -17% | 0 | 0 | — |
case-18 | fail→fail | 14,194 | 15,144 | +7% | 1 | 1 | 0% | 1,478 | 1,875 | +27% | 0 | 0 | — |
case-19 | pass→pass | 15,246 | 9,042 | -41% | 1 | 1 | 0% | 1,798 | 929 | -48% | 0 | 0 | — |
case-20 | pass→pass | 14,902 | 11,489 | -23% | 1 | 1 | 0% | 1,674 | 1,293 | -23% | 0 | 0 | — |
case-21 | pass→pass | 9,763 | 10,564 | +8% | 1 | 1 | 0% | 812 | 1,211 | +49% | 0 | 0 | — |
case-22 | fail→fail | 22,250 | 15,952 | -28% | 1 | 1 | 0% | 2,843 | 2,251 | -21% | 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 +5 percentage points is the difference between those two pass rates over the 22 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.