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Get Started Free →LLM prompt blocks for per-paper and corpus literature synthesis after a search pipeline. USE WHEN synthesizing arXiv/Crossref results, writing per-paper notes, thematic clusters, gap analysis from a literature corpus, or wiring template_search_project synthesis — even without naming infrastructure.llm templates.
.claude/skills/docxology-template-literature-synthesis/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 31% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 3% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 20% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 35% | 0% |
| case-10 | ✗→✓ | ▲ Improved | -63% | 0% |
Prompt blocks for infrastructure.llm or project synthesis.py after literature search.
Each paper renders as:
text### {citation_key} — {title} ({year}) **Authors:** {authors} **DOI / URL:** {doi or url} **Abstract:** {abstract}
Structured note: CONTRIBUTION (one sentence), METHOD (2–3 bullets), EVIDENCE (2–3 bullets), LIMITATION (one bullet), TAGS (3–7 lowercase). Cite as [{citation_key}].
Group into 3–7 thematic clusters; summarise dominant approach; agreements/disagreements; 3 open questions. Cite only corpus keys in square brackets.
Given GOAL + corpus: per paper COVERS/LACKS sub-claims; propose 3 follow-up experiments. Cite by key.
temperature=0.0, seed=42 for replay.infrastructure.llm.validation.validate_complete.output/llm/ or project convention; keys match references.bib.projects/templates/template_search_project/ for wired implementation.infrastructure/search/ and project search scriptsFull prompt text and programmatic example: references/prompt-blocks.md
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 28,838 | 27,929 | -3% | 1 | 1 | 0% | 5,831 | 6,689 | +15% | 0 | 0 | — |
case-02 | fail→pass | 15,708 | 19,514 | +24% | 1 | 1 | 0% | 2,876 | 3,764 | +31% | 0 | 0 | — |
case-03 | fail→pass | 26,303 | 18,098 | -31% | 1 | 1 | 0% | 3,900 | 4,028 | +3% | 0 | 0 | — |
case-04 | fail→pass | 21,504 | 22,098 | +3% | 1 | 1 | 0% | 3,436 | 4,129 | +20% | 0 | 0 | — |
case-05 | fail→pass | 5,224 | 5,372 | +3% | 1 | 1 | 0% | 1,244 | 1,679 | +35% | 0 | 0 | — |
case-06 | fail→fail | 17,149 | 11,813 | -31% | 1 | 1 | 0% | 3,208 | 2,602 | -19% | 0 | 0 | — |
case-07 | fail→fail | 16,500 | 11,032 | -33% | 1 | 1 | 0% | 2,949 | 2,601 | -12% | 0 | 0 | — |
case-08 | pass→pass | 7,441 | 2,027 | -73% | 1 | 1 | 0% | 1,433 | 828 | -42% | 0 | 0 | — |
case-09 | pass→pass | 5,876 | 3,878 | -34% | 1 | 1 | 0% | 1,285 | 1,276 | -1% | 0 | 0 | — |
case-10 | fail→pass | 13,019 | 2,141 | -84% | 1 | 1 | 0% | 2,173 | 803 | -63% | 0 | 0 | — |
case-11 | fail→pass | 15,359 | 3,536 | -77% | 1 | 1 | 0% | 2,265 | 939 | -59% | 0 | 0 | — |
case-12 | fail→fail | 1,570 | 23,456 | +1394% | 1 | 1 | 0% | 230 | 4,484 | +1850% | 0 | 0 | — |
case-13 | pass→pass | 15,401 | 4,462 | -71% | 1 | 1 | 0% | 1,901 | 1,147 | -40% | 0 | 0 | — |
case-14 | fail→pass | 13,121 | 7,747 | -41% | 1 | 1 | 0% | 2,510 | 1,799 | -28% | 0 | 0 | — |
case-15 | pass→pass | 11,028 | 7,486 | -32% | 1 | 1 | 0% | 1,821 | 1,559 | -14% | 0 | 0 | — |
case-16 | fail→pass | 3,473 | 1,671 | -52% | 1 | 1 | 0% | 525 | 682 | +30% | 0 | 0 | — |
case-17 | fail→pass | 15,082 | 8,694 | -42% | 1 | 1 | 0% | 2,420 | 1,874 | -23% | 0 | 0 | — |
case-18 | pass→pass | 15,105 | 6,538 | -57% | 1 | 1 | 0% | 2,402 | 1,462 | -39% | 0 | 0 | — |
case-19 | pass→pass | 11,808 | 4,903 | -58% | 1 | 1 | 0% | 1,780 | 1,225 | -31% | 0 | 0 | — |
case-20 | fail→pass | 23,660 | 7,441 | -69% | 1 | 1 | 0% | 4,890 | 1,935 | -60% | 0 | 0 | — |
case-21 | fail→fail | 91,165 | 13,597 | -85% | 1 | 1 | 0% | 6,164 | 2,935 | -52% | 0 | 0 | — |
case-22 | fail→fail | 12,911 | 10,888 | -16% | 1 | 1 | 0% | 2,825 | 2,722 | -4% | 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 +45 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.