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Research
Skill

/self-review

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.

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medsci-skills
34462 skills
Install
$ npx -y skills add Aperivue/medsci-skills --skill self-review --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/self-review

Context 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.

SKILL.md

self-review.SKILL.md
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"

Self-Review Skill

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.

Optional Flags

  • `--fix`: after the report, apply fixes for every issue with `fixable_by_ai: true`, editing the manuscript in place, then report a diff summary. Never fix `fixable_by_ai: false` issues (missing data, design flaws). Maximum 2 fix-and-re-review iterations; if the score is still below threshold after the second, stop and report what remains as structural (inside `/write-paper` this routes to Phase 7.4a Audit Recovery).
  • `--json`: also emit the structured JSON block (Phase 3c). Default when called from `/write-paper` Phase 7.
  • `--panel`: run the multi-agent panel review (Phase 2.6) — domain-expert reviewers in parallel plus an editor synthesis — instead of the single-pass review. Opt-in and **off by default**, because it spawns N reviewer agents + 1 editor and costs several times more tokens; reserve it for a high-stakes final pass on a top-tier target. Do **not** combine with `--fix`: a panel diagnoses and prioritizes; run `--fix` as a separate pass once the author has triaged the panel's findings.

Severity Framing

  • **Fatal**: a design flaw existing data cannot fix (data leakage that invalidates all results, no reference standard, label–feature circularity). Submission would likely be rejected. Reserve Fatal for true design-level problems.
  • **Fixable**: significant but addressable with existing data (missing calibration, unclear exclusion criteria, absent CIs, incomplete reporting). Most issues are Fixable.

Two Objectives: the Floor and the Ceiling

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.

Workflow

Phase 1: Intake

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>.md

Review 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.

Phase 2: Systematic Check

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** |

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Ships withmedsci-skills

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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