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Get Started Free →Use when the user wants to find related work, survey a research area, identify literature gaps, or discover open-source implementations. Triggers on phrases like "find papers on", "related work", "literature review", "what papers exist", "open source implementation", or "papers with code".
.claude/skills/fcakyon-literature-research/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 10% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 4% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 46% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 5% | 0% |
| case-15 | ✗→✓ | ▲ Improved | -9% | 0% |
You are helping a researcher conduct systematic literature research. Follow this methodology to ensure thorough, accurate coverage.
Before searching:
Use multiple search strategies in order:
From seed papers or initial results:
Organize found papers into a structured taxonomy:
| Paper | Year | Venue | Approach | Key Result | Code? | Relevance |
|-------|------|-------|----------|-----------|-------|-----------|Group by methodology or approach type, not chronologically.
Map what exists vs. what's missing:
For each identified gap, verify it's real:
Rate confidence: HIGH (extensively searched, clearly missing), MEDIUM (searched but might have missed niche work), LOW (limited search, gap may exist elsewhere).
Produce a structured research landscape:
Every paper mentioned must have verified metadata:
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 30,055 | 27,540 | -8% | 1 | 1 | 0% | 5,270 | 5,821 | +10% | 0 | 0 | — |
case-02 | fail→fail | 37,538 | 28,890 | -23% | 1 | 1 | 0% | 6,214 | 5,921 | -5% | 0 | 0 | — |
case-03 | fail→fail | 37,877 | 25,207 | -33% | 1 | 1 | 0% | 6,215 | 5,261 | -15% | 0 | 0 | — |
case-04 | pass→fail | 14,327 | 17,698 | +24% | 1 | 1 | 0% | 2,308 | 3,640 | +58% | 0 | 0 | — |
case-05 | pass→pass | 13,725 | 20,499 | +49% | 1 | 1 | 0% | 2,338 | 4,251 | +82% | 0 | 0 | — |
case-06 | pass→pass | 38,463 | 27,319 | -29% | 1 | 1 | 0% | 6,179 | 5,757 | -7% | 0 | 0 | — |
case-07 | pass→pass | 20,489 | 8,539 | -58% | 1 | 1 | 0% | 1,945 | 2,259 | +16% | 0 | 0 | — |
case-08 | pass→pass | 16,528 | 11,198 | -32% | 1 | 1 | 0% | 2,265 | 2,705 | +19% | 0 | 0 | — |
case-09 | fail→pass | 18,491 | 13,876 | -25% | 1 | 1 | 0% | 2,810 | 2,911 | +4% | 0 | 0 | — |
case-10 | fail→pass | 13,465 | 12,883 | -4% | 1 | 1 | 0% | 1,953 | 2,846 | +46% | 0 | 0 | — |
case-11 | pass→pass | 17,545 | 14,668 | -16% | 1 | 1 | 0% | 2,647 | 3,122 | +18% | 0 | 0 | — |
case-12 | fail→pass | 16,568 | 12,200 | -26% | 1 | 1 | 0% | 2,740 | 2,868 | +5% | 0 | 0 | — |
case-13 | fail→fail | 18,462 | 21,356 | +16% | 1 | 1 | 0% | 2,890 | 3,950 | +37% | 0 | 0 | — |
case-14 | fail→fail | 10,083 | 3,211 | -68% | 1 | 1 | 0% | 1,675 | 1,251 | -25% | 0 | 0 | — |
case-15 | fail→pass | 13,822 | 7,946 | -43% | 1 | 1 | 0% | 2,204 | 1,995 | -9% | 0 | 0 | — |
case-16 | fail→pass | 12,525 | 5,573 | -56% | 1 | 1 | 0% | 1,944 | 1,580 | -19% | 0 | 0 | — |
case-17 | fail→pass | 10,379 | 8,841 | -15% | 1 | 1 | 0% | 1,838 | 2,224 | +21% | 0 | 0 | — |
case-18 | pass→pass | 8,742 | 5,746 | -34% | 1 | 1 | 0% | 1,314 | 1,717 | +31% | 0 | 0 | — |
case-19 | pass→pass | 11,880 | 8,790 | -26% | 1 | 1 | 0% | 1,905 | 2,194 | +15% | 0 | 0 | — |
case-20 | pass→pass | 11,642 | 8,089 | -31% | 1 | 1 | 0% | 1,783 | 2,004 | +12% | 0 | 0 | — |
case-21 | fail→pass | 11,481 | 6,033 | -47% | 1 | 1 | 0% | 1,747 | 1,807 | +3% | 0 | 0 | — |
case-22 | pass→pass | 13,588 | 9,012 | -34% | 1 | 1 | 0% | 2,060 | 2,115 | +3% | 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 +32 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.