What problem does it solve? Responding to peer review requires turning scattered reviewer comments into a coordinated set of experiments, text changes, and honest reply letters, which is easy to do inconsistently or dishonestly under deadline pressure. ## Core Features & Use Cases - Review Normalization: Parses reviewer comments, meta-reviews, and decision letters into a stable atomic review matrix with ids like R1-C1, classifying each item as editorial, text-only, evidence gap, experiment gap, claim scope, or unaddressable. - Routed Action Planning: Decides per item whether the right response is text revision, evidence repackaging, literature positioning, baseline recovery, supplementary experiments, claim downgrade, or an explicit limitation, then routes experiments to analysis campaigns and edits to the writing workflow. - Evidence-Backed Response Letter: Assembles a point-by-point response letter, text deltas, and evidence update notes using templates, with strict rules against inventing results or overpromising. - Use Case: A researcher receives three reviewer reports demanding extra baselines and narrower claims; the skill builds the review matrix, launches only the genuinely needed supplementary experiments tied to named reviewer items, revises the manuscript scope, and drafts the rebuttal letter. ## Quick Start Use the rebuttal skill to organize these reviewer comments into an action plan and draft a point-by-point response letter for my paper.
