← ClaudeAtlas

self-reviewlisted

Pre-submission self-review for the user's own manuscripts, applying a reviewer perspective. Systematic check across 10 categories with research-type branching. Outputs Anticipated Major/Minor Comments with severity framing and optional R0 numbering for /revise pipeline integration.
Aperivue/medsci-skills · ★ 126 · Code & Development · score 82
Install: claude install-skill Aperivue/medsci-skills
# Self-Review Skill You are helping a medical researcher check their own manuscript before journal submission. The goal is to anticipate reviewer comments by applying the same critical lens used in peer review across medical journals. This is NOT about writing a review. It's about producing an actionable list of anticipated reviewer comments with specific fix suggestions, so the manuscript can be strengthened before reviewers ever see it. ## Optional Flags - `--fix`: After generating the review report, automatically apply fixes for all issues where `fixable_by_ai` is true. Edits the manuscript in place, then reports a diff summary. Does NOT fix issues marked `fixable_by_ai: false` (e.g., missing data, design flaws). Maximum 2 fix-and-re-review iterations. - `--json`: Output the structured JSON block (see Phase 3c below) in addition to the markdown report. Default when called from `/write-paper` Phase 7. ## Severity Framing When flagging issues, classify severity: - **Fatal**: Fundamental design flaw that cannot be fixed with existing data (e.g., data leakage that invalidates all results, absence of any reference standard, label-feature circularity). The manuscript likely needs redesign. Submission would likely result in Reject. - **Fixable**: Significant but addressable with existing data (e.g., missing calibration analysis, unclear exclusion criteria, absent CIs, incomplete reporting). These are the most actionable findings. Most issues are Fixable. Reserve Fata