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Parse peer reviewer comments and generate a structured Response to Reviewers document with tracked manuscript changes. Classifies comments as MAJOR/MINOR/REBUTTAL, coordinates new analyses with /analyze-stats and /make-figures, and produces cover letter for editor.

Data & Documents 292 stars 71 forks Updated 4 days ago MIT

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Skill Content

# Revision Skill -- Response to Peer Reviewers ## Purpose Parse reviewer decision letters, classify each comment by type, generate a formal Response to Reviewers document, track required manuscript changes, and coordinate with /analyze-stats or /make-figures when new analyses or visuals are needed. --- ## Activation When the user provides reviewer comments (pasted text, PDF, or file path), or requests revision of a manuscript, this skill activates. Before proceeding, confirm: 1. The reviewer decision letter (pasted text or file path) 2. The current manuscript file (`paper/main.tex` or `paper/main.qmd`) 3. The revision round number (default: R1) 4. The journal name (affects cover letter format) --- ## Reference Files - **Response-letter voice gallery**: `${CLAUDE_SKILL_DIR}/references/r2r_voice.md` -- before/after examples, three response skeletons (accept / partial-accept / polite-rebuttal), and a meta-phrase-to-natural conversion table. Read it before drafting the Response to Reviewers document. --- ## Step 1: Parse and Number All Comments Read the full decision letter. Extract every discrete comment from every reviewer and the editor. ### Numbering Convention ``` E-1, E-2, ... <- Editor comments R1-1, R1-2, ... <- Reviewer 1 comments R2-1, R2-2, ... <- Reviewer 2 comments R3-1, R3-2, ... <- Reviewer 3 (if present) ``` If a reviewer groups multiple requests in one paragraph, split them into sub-items: `R1-3a, R1-3b, R1-3c` ### Classification...

Details

Author
Aperivue
Repository
Aperivue/medsci-skills
Created
5 months ago
Last Updated
4 days ago
Language
Python
License
MIT

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