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Get Started Free →Scaffold and draft medical/AI literature reviews (narrative, scoping PRISMA-ScR, or systematic). Asks for the spine axis, builds a 7-part skeleton with a required Intro scope/non-overlap block, a summary-table stub, an evaluation-metrics critique subsection, and reporting-guideline wiring. Reuses the self-review RV1-RV9 narrative-review probes for QC. Does not invent citations.
.claude/skills/aperivue-review-paper/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 22% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 22% | 0% |
| case-05 | ✗→✓ | ▲ Improved | -53% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 38% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 30% | 0% |
Scaffold and draft a literature review — narrative, scoping (PRISMA-ScR), or systematic (PRISMA 2020) — for medical / medical-AI research. This skill builds the structure, the required scope/non-overlap framing, the summary-table stubs, and the reporting-guideline wiring, then hands off to the existing QC skills. It is the review-article counterpart to write-paper (which targets original research); for reviewing someone else's review article, use /peer-review or /self-review (the RV1-RV9 probes). The structure follows established review-writing conventions; it is not derived from, and does not reproduce, any specific published review.
_src/refs.bib (produced by /search-lit → /lit-sync → /verify-refs). If a claim needs a reference that is not yet in the library, leave a [NEEDS-REF: claim] marker and route it to /search-lit; do not fabricate a DOI, author, year, or citekey.
from sources the user supplies or that are verified; an unknown cell stays a placeholder.
the output maps the evidence — it does not issue clinical recommendations.
/self-review reports 0fatal findings and /verify-refs reports 0 FABRICATED / MISMATCH and no placeholder citations remain.
systematic (PRISMA 2020). This decides the reporting guideline and the registration path.
modality (e.g. 2D → 3D), by task (generation / QA / deployment), or by lifecycle stage. Every body section then follows this one axis; mixing axes is the most common structural failure.
pre-empts the reviewer's first question, "why another review on this?" (user-approval checkpoint: confirm the boundary with the user before scaffolding).
Load ${CLAUDE_SKILL_DIR}/references/macro_skeleton.md and instantiate:
(required field) → "this review…".
generated to match the type, Step 2).
required quality signal: how the field measures itself, and where those metrics mislead).
study | year | [spine-axis value] | method | key finding.The stub ships with column headers and one placeholder row; rows are filled only from verified sources (see Anti-Hallucination).
over-enforce it).
/check-reporting does not yet carry the chosen checklist (e.g. PRISMA-ScR), track amanual gap table and flag it for the user rather than silently skipping the item.
Run the standard manuscript QC chain, which this skill is designed to feed:
/self-review — the RV1-RV9 narrative-review probes auto-activate for a review article./check-reporting — the chosen guideline (SANRA / PRISMA-ScR / PRISMA 2020)./verify-refs — every citation resolves; 0 FABRICATED / MISMATCH./humanize — AI-pattern density below threshold./academic-aio — discoverability pass (optional).Convergence gate: self-review fatal = 0; verify-refs FABRICATED/MISMATCH = 0; no [NEEDS-REF] / [@NEW:]-style placeholder citations remain; humanize density < 2.0.
_src/refs.bib only; never invent citekeys (see Anti-Hallucination).has contributed to the area being reviewed (per intellectual-coi).
macro_skeleton.md — the 7-part template and the table/figure plan per review type.| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-20 | pass→pass | 16,187 | 9,073 | -44% | 1 | 1 | 0% | 2,802 | 2,859 | +2% | 0 | 0 | — |
case-08 | pass→pass | 13,597 | 8,610 | -37% | 1 | 1 | 0% | 2,050 | 2,622 | +28% | 0 | 0 | — |
case-01 | fail→fail | 38,449 | 38,762 | +1% | 1 | 1 | 0% | 6,224 | 7,572 | +22% | 0 | 0 | — |
case-02 | fail→pass | 36,905 | 37,876 | +3% | 1 | 1 | 0% | 6,215 | 7,563 | +22% | 0 | 0 | — |
case-03 | fail→pass | 37,342 | 38,643 | +3% | 1 | 1 | 0% | 6,205 | 7,549 | +22% | 0 | 0 | — |
case-04 | fail→fail | 7,664 | 9,598 | +25% | 1 | 1 | 0% | 1,276 | 2,786 | +118% | 0 | 0 | — |
case-05 | fail→pass | 33,596 | 9,718 | -71% | 1 | 1 | 0% | 6,192 | 2,935 | -53% | 0 | 0 | — |
case-06 | pass→fail | 16,741 | 30,276 | +81% | 1 | 1 | 0% | 3,300 | 7,523 | +128% | 0 | 0 | — |
case-07 | pass→pass | 15,032 | 9,400 | -37% | 1 | 1 | 0% | 2,225 | 2,860 | +29% | 0 | 0 | — |
case-09 | pass→pass | 4,711 | 7,752 | +65% | 1 | 1 | 0% | 803 | 2,595 | +223% | 0 | 0 | — |
case-10 | fail→pass | 8,147 | 4,241 | -48% | 1 | 1 | 0% | 1,538 | 2,119 | +38% | 0 | 0 | — |
case-11 | fail→pass | 9,996 | 3,585 | -64% | 1 | 1 | 0% | 1,449 | 1,886 | +30% | 0 | 0 | — |
case-12 | pass→pass | 11,365 | 9,028 | -21% | 1 | 1 | 0% | 1,822 | 2,822 | +55% | 0 | 0 | — |
case-13 | pass→pass | 11,877 | 6,972 | -41% | 1 | 1 | 0% | 1,972 | 2,528 | +28% | 0 | 0 | — |
case-14 | pass→pass | 13,749 | 3,572 | -74% | 1 | 1 | 0% | 2,423 | 1,946 | -20% | 0 | 0 | — |
case-15 | fail→pass | 6,808 | 3,975 | -42% | 1 | 1 | 0% | 1,085 | 1,927 | +78% | 0 | 0 | — |
case-16 | pass→pass | 16,182 | 11,755 | -27% | 1 | 1 | 0% | 2,627 | 3,255 | +24% | 0 | 0 | — |
case-17 | fail→pass | 15,771 | 7,709 | -51% | 1 | 1 | 0% | 2,454 | 2,621 | +7% | 0 | 0 | — |
case-18 | pass→pass | 9,044 | 6,472 | -28% | 1 | 1 | 0% | 1,489 | 2,338 | +57% | 0 | 0 | — |
case-19 | pass→pass | 7,485 | 2,658 | -64% | 1 | 1 | 0% | 1,222 | 1,795 | +47% | 0 | 0 | — |
case-21 | pass→pass | 5,345 | 1,759 | -67% | 1 | 1 | 0% | 740 | 1,603 | +117% | 0 | 0 | — |
case-22 | pass→pass | 12,701 | 9,808 | -23% | 1 | 1 | 0% | 1,924 | 2,768 | +44% | 0 | 0 | — |
case-23 | fail→pass | 18,250 | 1,593 | -91% | 1 | 1 | 0% | 1,024 | 1,600 | +56% | 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. 23 cases were attempted, and 22 counted toward the lift figure. The other 1 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +30 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.