aperivue/review-paper
> 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.
npx skills add https://github.com/Aperivue/medsci-skills --skill review-paper
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.Take aperivue/review-paper from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.