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 checking your own manuscript before submission from a reviewer's perspective. Returns anticipated Major/Minor comments with fixes, including numerical, citation and leakage checks, with an optional multi-reviewer panel. Someone else's paper is /peer-review.
$ npx -y skills add Aperivue/medsci-skills --skill self-review --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/self-reviewContext preview
The summary Claude sees to decide when to auto-load this skill.
Use when checking your own manuscript before submission from a reviewer's perspective. Returns anticipated Major/Minor comments with fixes, including numerical, citation and leakage checks, with an optional multi-reviewer panel. Someone else's paper is /peer-review.
name: self-review description: Use when checking your own manuscript before submission from a reviewer's perspective. Returns anticipated Major/Minor comments with fixes, including numerical, citation and leakage checks, with an optional multi-reviewer panel. Someone else's paper is /peer-review. metadata: triggers: "self-review, pre-submission check, check my paper, reviewer perspective, manuscript self-check"
Check the user's own manuscript before submission and produce an actionable list of anticipated reviewer comments, each with a specific fix — not a written review.
The **floor** (categories A–K and the gates of Phases 2.5–2.5f) minimizes rejection-for-cause — fabricated citations, numbers that do not reconcile, overclaims, missing checklist items, leakage — and much of it works by **adding** a hedge, caveat, disclosure or audit trail. Iterated, that over-hardens a manuscript: every finding is correct, yet the whole reads as a defensive audit (over-hedged, audit trail in the body, Abstract buried under caveats, the strongest robustness result hidden in Limitations). The **ceiling** pass (category L / Phase 2.5g) runs **after** the floor gates, reads the accurate manuscript as a whole, and recommends only SUBTRACTION — REMOVE, MOVE, or TIGHTEN. It is advisory, never blocks, and cannot relax a floor gate. Report its findings as their own block (Phase 3), not folded into the "add this" comments. Phase 2.5i then reads the floor + ceiling state to declare the loop done, including a zero-edit PASS.
1. Get the manuscript — PDF, Word doc, or pasted text. 2. Ask the user: target journal (sets reporting standards and scope); manuscript type (original research / review / perspective / technical note / letter / meta-analysis / case report); anything they are already worried about. On an interactive run, offer the `--panel` review (Phase 2.6) **once**, in one line, then proceed single-pass unless the user opts in. Never offer or apply the panel under `--json` or when called from `/write-paper`. 3. Read the full manuscript. 4. **SSOT gate — confirm there is one manuscript, not several.** Self-review reads a single file, so drift between a legacy working copy and the live submission copy is invisible to it. Before a `--panel` run or any pre-submission pass, check for multiple copies:
find . \( -path '*manuscript*' -o -path '*main_document*' \) -name '*.md' | grep -v node_modules
If more than one manuscript-like file exists, confirm which is the SSOT and run `/sync-submission`'s divergence gate — a `STALE_COPY` (an SSOT numeric claim or heading that did not propagate to the other copy) is a P0 that must clear first:
python3 "${CLAUDE_SKILL_DIR}/../sync-submission/scripts/detect_copy_divergence.py" \
--ssot <ssot>.md --copy <other-copy>.mdReview the SSOT copy, never a stale one. **Under `--panel` this is blocking:** if `find` returns more than one file and the SSOT is not pinned (no `SSOT.yaml` with `truth.manuscript_md`, no explicit `--ssot <path>`), STOP before spawning any reviewer and have the user name the SSOT (and clear any `STALE_COPY`), because a panel on a stale copy wastes the whole pass. Do not auto-pick the longest/newest file. A single-pass review may proceed on the one file it was given.
Work every category the Research-Type Adaptation table (below) marks as applicable, and for each item decide whether a reviewer would raise it as a Major or Minor comment. The per-item check tables are in `references/phases/phase2_systematic_check.md` — read it once you have the manuscript and know its type.
| | Category | What it asks | |---|---|---| | **A** | Study Design & Data Integrity | patient-level splits, leakage, input-text contamination, analysis unit | | **B** | Reference Standard & Ground Truth | definition specificity, timing, annotator independence | | **C** | Validation & Statistical Reporting | CIs, **calibration**, comparator, effect size, power-aware nulls, equivalence margins, interaction anchoring | | **D** | Clinical Framing & Importance | intended use, overclaiming, novelty, **endpoint↔conclusion scope** | | **E** | Reproducibility | preprocessing, model detail, hardware/software, data & code availability | | **F** | Reporting Completeness | abstract↔body consistency, flow diagram, ethics, missing data, word cap | | **G** |
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
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 a target journal has no profile yet. Reads the journal's author guidelines and writes a detailed /write-paper profile…
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Use when analyzing a researcher's publication record from PubMed. Fetches an author's papers, classifies study types and author…
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