> 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.Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section. Transforms your writing process from solo effort to collaborative partnership.
Identifies high-quality leads for your product or service by analyzing your business, searching for target companies, and providing actionable contact strategies. Perfect for sales, business development, and marketing professionals.
Use this skill to query your Google NotebookLM notebooks directly from Claude Code for source-grounded, citation-backed answers from Gemini. Browser automation, library management, persistent auth. Drastically reduced hallucinations through document-only responses.
Efficient database search tool for bioRxiv preprint server. Use this skill when searching for life sciences preprints by keywords, authors, date ranges, or categories, retrieving paper metadata, downloading PDFs, or conducting literature reviews.
Query and analyze scholarly literature using the OpenAlex database. This skill should be used when searching for academic papers, analyzing research trends, finding works by authors or institutions, tracking citations, discovering open access publications, or conducting bibliometric analysis across 240M+ scholarly works. Use for literature searches, research output analysis, citation analysis, and academic database queries.
Access USPTO APIs for patent/trademark searches, examination history (PEDS), assignments, citations, office actions, TSDR, for IP analysis and prior art searches.
Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.
Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use hypothesis-generation; for creative ideation use scientific-brainstorming.
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.