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Get Started Free →Use after framing a question and before designing an analysis, or when choosing a method, judging whether a result is novel, or needing a prior effect size for a power calculation
.claude/skills/k-dense-ai-surveying-prior-work/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | -5% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -20% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 25% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 75% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 17% | 0% |
Before designing an analysis, ground the question and your chosen methods in what is already known. Most questions have established methods, known confounds, and prior effect sizes. Reinventing a method badly — or rediscovering a known artifact and reporting it as a finding — wastes effort and erodes credibility.
Core principle: Find out what is already known before you generate new claims.
This is the science analog of reading the existing codebase before writing new code. It is a flexible skill — adapt depth to the stakes of the investigation.
framing-research-questions, before designing-the-analysisSurvey four things:
science-superpowers:dispatching-parallel-investigations for the workflow. This keeps your own context clean.After grounding, invoke science-superpowers:designing-the-analysis. Bring forward the adopted methods, the confound list, and the prior effect size for powering the design.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-16 | pass→pass | 17,679 | 13,406 | -24% | 1 | 1 | 0% | 2,406 | 2,517 | +5% | 0 | 0 | — |
case-01 | fail→fail | 35,275 | 26,333 | -25% | 1 | 1 | 0% | 6,217 | 4,230 | -32% | 0 | 0 | — |
case-02 | fail→pass | 36,996 | 32,044 | -13% | 1 | 1 | 0% | 6,223 | 5,883 | -5% | 0 | 0 | — |
case-03 | pass→pass | 16,919 | 17,018 | +1% | 1 | 1 | 0% | 2,692 | 3,212 | +19% | 0 | 0 | — |
case-04 | pass→pass | 10,875 | 9,976 | -8% | 1 | 1 | 0% | 2,101 | 2,614 | +24% | 0 | 0 | — |
case-05 | pass→pass | 10,126 | 9,745 | -4% | 1 | 1 | 0% | 1,691 | 2,210 | +31% | 0 | 0 | — |
case-06 | fail→fail | 26,680 | 28,696 | +8% | 1 | 1 | 0% | 3,743 | 5,124 | +37% | 0 | 0 | — |
case-07 | fail→pass | 15,462 | 7,802 | -50% | 1 | 1 | 0% | 2,315 | 1,863 | -20% | 0 | 0 | — |
case-08 | pass→pass | 13,881 | 11,428 | -18% | 1 | 1 | 0% | 2,016 | 2,438 | +21% | 0 | 0 | — |
case-09 | fail→pass | 19,085 | 19,347 | +1% | 1 | 1 | 0% | 2,889 | 3,613 | +25% | 0 | 0 | — |
case-10 | fail→pass | 12,619 | 18,074 | +43% | 1 | 1 | 0% | 2,140 | 3,747 | +75% | 0 | 0 | — |
case-11 | pass→pass | 12,808 | 7,708 | -40% | 1 | 1 | 0% | 1,812 | 1,804 | -0% | 0 | 0 | — |
case-12 | fail→pass | 18,815 | 18,072 | -4% | 1 | 1 | 0% | 2,972 | 3,481 | +17% | 0 | 0 | — |
case-13 | pass→pass | 14,294 | 12,649 | -12% | 1 | 1 | 0% | 2,004 | 2,482 | +24% | 0 | 0 | — |
case-14 | pass→pass | 13,946 | 11,868 | -15% | 1 | 1 | 0% | 1,993 | 2,406 | +21% | 0 | 0 | — |
case-15 | fail→pass | 15,784 | 21,311 | +35% | 1 | 1 | 0% | 2,672 | 4,351 | +63% | 0 | 0 | — |
case-17 | pass→pass | 32,654 | 13,440 | -59% | 1 | 1 | 0% | 2,620 | 2,731 | +4% | 0 | 0 | — |
case-18 | pass→pass | 10,121 | 8,016 | -21% | 1 | 1 | 0% | 1,645 | 1,938 | +18% | 0 | 0 | — |
case-19 | fail→pass | 13,872 | 9,499 | -32% | 1 | 1 | 0% | 2,079 | 2,138 | +3% | 0 | 0 | — |
case-20 | pass→pass | 15,580 | 13,542 | -13% | 1 | 1 | 0% | 2,113 | 2,652 | +26% | 0 | 0 | — |
case-21 | fail→pass | 14,207 | 8,294 | -42% | 1 | 1 | 0% | 1,903 | 1,889 | -1% | 0 | 0 | — |
case-22 | pass→pass | 11,916 | 4,645 | -61% | 1 | 1 | 0% | 1,718 | 1,408 | -18% | 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 +36 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.