academic-aio
Use when a medical AI paper should be found and cited by AI search engines and RAG tools. Optimizes the title, abstract, summary…
Use when writing a literature review article (narrative, scoping PRISMA-ScR or systematic). Scaffolds a 7-part skeleton with a scope and non-overlap statement, summary-table stubs and reporting-guideline wiring, without inventing citations. Original research is /write-paper.
$ npx -y skills add Aperivue/medsci-skills --skill review-paper --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/review-paperContext preview
The summary Claude sees to decide when to auto-load this skill.
Use when writing a literature review article (narrative, scoping PRISMA-ScR or systematic). Scaffolds a 7-part skeleton with a scope and non-overlap statement, summary-table stubs and reporting-guideline wiring, without inventing citations. Original research is /write-paper.
name: review-paper description: Use when writing a literature review article (narrative, scoping PRISMA-ScR or systematic). Scaffolds a 7-part skeleton with a scope and non-overlap statement, summary-table stubs and reporting-guideline wiring, without inventing citations. Original research is /write-paper. metadata: triggers: "review article, scoping review, narrative review, literature review, PRISMA-ScR, write a review"
The review-article counterpart to `write-paper` (original research). For *reviewing* someone else's review article, use `/peer-review` or `/self-review` (the RV1-RV9 probes) instead.
1. Confirm **format** with the user: narrative (SANRA) | scoping (PRISMA-ScR + JBI) | systematic (PRISMA 2020). This decides the reporting guideline and the registration path. 2. Choose the **spine axis** — the single most consequential decision: organize the body by **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. 3. Require a **scope statement + non-overlap boundary** against prior/adjacent reviews — this 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 write the section for the chosen format to `manuscript.md` in the manuscript directory, with the summary-table stubs (Step 2) and that section's figure/table legend plan. The structure depends on the format:
PRISMA 2020 item (the reference lists them by item number). Do not use the 7-part skeleton for these: it has no place for eligibility criteria, search, selection, charting/extraction, risk of bias or synthesis methods, nor for study-selection results. The thematic body by spine axis goes inside Results → synthesis; frontiers and the evaluation-metrics critique go inside Discussion.
The narrative 7-part skeleton:
1. **Abstract** — a 4-5 move version (structured for scoping/systematic). 2. **Introduction** — clinical motivation → technology → **scope + non-overlap block (required field, all formats)** → "this review…". 3. **Background / technical principles** — tight; cite once, do not re-survey the field. 4. **Thematic body by spine axis** — each section ends with a **summary table** (stub generated to match the type, Step 2). 5. **Frontiers / what is advancing.** 6. **Challenges / discussion** — include an **evaluation-metrics critique subsection** (a required quality signal: how the field measures itself, and where those metrics mislead). 7. **Conclusion** — measured. A scoping review maps the evidence; it issues no recommendation-grade language or clinical recommendations.
Every citekey must resolve to the project's verified `_src/refs.bib` (produced by `/search-lit` → `/lit-sync` → `/verify-refs`). If a claim needs a reference not yet in the library, leave a `[@NEW:short-topic]` placeholder and route it to `/search-lit`; never fabricate a DOI, author, year, or citekey.
The stub ships with column headers and one placeholder row. Cells (study, year, metric, finding) are filled only from sources the user supplies or that are verified; an unknown cell stays a placeholder.
over-enforce it).
2020, not SANRA), track a manual 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:
1. `/self-review` — the RV1-RV9 narrative-review probes auto-activate for a review article. 2. `/check-reporting` — PRISMA-ScR or PRISMA 2020 (SANRA via the Step 3 manual table). 3. `/verify-refs` — every citation resolves; 0 FABRICATED / MISMATCH. 4. `/humanize` — AI-pattern density below threshold. 5. `/academic-aio` — discoverability pass (optional).
**Convergence gate** — the draft is not done until: self-review fatal = 0; verify-refs FABRICATED/MISMATCH = 0; no `[@NEW:...]` placeholder citations remain; humanize density < 2.0.
has contributed to the area being reviewed.
Created & maintained by Yoojin Nam, MD Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea MedSci Skills is a set of Agent Skills for clinical research: literature
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