skills/utility/stage/rebuttal-self-review/SKILL.md
Systematic quality check for a completed rebuttal document before submission. Use after Stage 3 document compilation or after Stage 5 final remarks writing to verify completeness, tone, factual accuracy, and structural integrity. Catches common rebuttal errors before they reach reviewers.
npx skillsauth add runtsang/rebuttalstudio rebuttal-self-reviewInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
RebuttalStudio Utility — Pre-Submission Check Run this skill after Stage 3 or Stage 5 document generation, before copying the final text into OpenReview or the submission portal. It catches structural gaps, tone issues, and factual risks that stage-specific skills may have missed.
A systematic quality assurance checklist tailored for academic conference rebuttals (ICLR, ICML, NeurIPS, and similar venues).
Unlike a paper, a rebuttal has a strict deadline, tight word limits, and must directly engage with specific reviewer concerns. This review process is optimized for those constraints: fast, targeted, and submission-ready focused.
Recommended timing:
The most common rebuttal failure is missing a reviewer concern.
Coverage Check:
- [ ] Count the total issues in Stage 1 breakdown for each reviewer
- [ ] Confirm each issue appears in the Stage 3 document
- [ ] For multi-round reviewers, confirm each follow-up was answered
- [ ] No reviewer's comment was silently dropped
- [ ] If an issue was not addressed, a reason is stated ("We will address this in the revision")
Tone Check:
- [ ] Opening of each reviewer section thanks the reviewer
- [ ] No response uses defensive phrases ("The reviewer is wrong", "This criticism is unfounded")
- [ ] Disagreements are framed as clarifications ("We believe this may be due to…")
- [ ] Commitments are grounded ("We will add X in Section Y" not "We might consider…")
- [ ] No sarcasm or frustration, even with persistent or unfair reviews
Factual Check:
- [ ] All cited results match the actual paper numbers
- [ ] No experiment is claimed that was not actually run
- [ ] No citation is mentioned that has not been verified (see citation-verification skill)
- [ ] "Changes made" pointers (section/table/figure numbers) are accurate
- [ ] No comparison results were invented to satisfy a reviewer
Conference rebuttals are read quickly. Structure affects perceived quality.
Structure Check:
- [ ] Each reviewer has a clearly labeled section
- [ ] Each concern has a visible label matching the Stage 1 breakdown title
- [ ] Quoted reviewer text appears before each response (using the Stage 2 format)
- [ ] The document reads in a logical order: per-reviewer, then per-issue
- [ ] For Stage 5: section headers (Acknowledgments / Key Strengths / Key Concerns / Commitment) are present
Clarity Check:
- [ ] Each response states its conclusion in the first sentence
- [ ] Technical claims are self-contained (no unexplained jargon for new terms)
- [ ] Tables are labeled and readable without surrounding text
- [ ] Formulas use consistent notation with the paper
- [ ] No response is longer than needed (trim to the minimum convincing length)
Writing Check:
- [ ] No AI filler phrases ("It is worth noting that…", "Furthermore…" as opener)
- [ ] Sentence lengths are varied
- [ ] Claims are specific to this paper (not generic statements)
- [ ] If in doubt, apply writing-anti-ai skill to the full document
Follow these steps for a systematic pass:
Pass 1 — Coverage scan (5 min)
Count all Stage 1 atomic issues. Open the Stage 3 document. Check off each issue.
Pass 2 — Tone pass (10 min)
Read only the opening sentence of each response. Does each one feel collaborative? Fix any that don't.
Pass 3 — Factual spot-check (10 min)
Pick 3–5 specific claims (percentages, section numbers, citation names). Verify them against the paper.
Pass 4 — Structure skim (5 min)
Can you navigate from one reviewer to another, and one issue to another, in 30 seconds? If not, improve headings.
Pass 5 — Final read-aloud (optional)
Read the document aloud or use text-to-speech. Awkward phrases become obvious when heard.
| Error Type | Description | How to Fix | |-----------|-------------|-----------| | Silent skip | An issue has no response | Add even a one-sentence placeholder | | Over-promising | "We will completely redesign Section 4" | Scope it: "We will add a clarifying paragraph to Section 4" | | Unverified citation | Citing a paper you haven't confirmed exists | Apply citation-verification skill | | Wrong pointer | "See Table 3" but the data is in Table 2 | Verify all section/figure/table references | | Defensive pivot | Reframing the concern without addressing it | Address it directly first, then provide context | | Invisible result | "We ran this experiment" with no numbers | Include the result or say "results will appear in the revision" |
Once all checks pass:
Adapted from Claude Scholar's paper-self-review skill for the rebuttal context. Original: https://github.com/Galaxy-Dawn/claude-scholar/blob/main/skills/paper-self-review/SKILL.md
data-ai
Remove AI-generated writing patterns from rebuttal prose to make it sound natural, direct, and authentically human-authored. Use when a Stage 2 refined draft or Stage 4 follow-up response reads too formulaic, robotic, or "GPT-like". Supports academic English.
testing
Condense rebuttal prose into fewer words without changing the original meaning. Use when a response block, paragraph, or selected passage is too long but all technical content, citations, and commitments must stay intact. Supports academic English.
development
Systematic review response strategy guide for RebuttalStudio. Use when developing response strategies for reviewer comments, deciding how to classify concerns, or choosing between Accept/Defend/Clarify/Experiment approaches at any stage of the rebuttal pipeline.
testing
Verification guide for citations added during rebuttal writing. Use when Stage 2 responses introduce new references, when the Area Chair asks about a cited paper, or when any citation in the rebuttal might have been AI-generated. Prevents the serious credibility damage of fabricated references in reviewer-facing documents.