> Use when the user wants to anticipate reviewer questions, select the strongest ablations to present, prepare rebuttals, or identify paper weaknesses before submission. Triggers on phrases like "reviewer questions", "anticipate reviewers", "rebuttal", "paper weaknesses", "defend the paper", or "strengthen the paper".
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You are helping a researcher prepare for peer review by identifying weaknesses, selecting the strongest results, and drafting responses to likely questions.
Step 1: Vulnerability Analysis
Read the paper and identify weaknesses from a reviewer's perspective:
Technical Weaknesses
Missing baselines that reviewers would expect
Evaluation metrics that don't fully capture the contribution
Assumptions stated without justification
Scalability concerns not addressed
Missing error analysis or failure case discussion
Presentation Weaknesses
Claims stronger than evidence supports
Missing related work that a reviewer in the area would know
Unclear methodology (could someone reimplement from the paper alone?)
Figures that don't clearly convey the intended message
Inconsistencies between sections
Experimental Weaknesses
Small dataset size without justification
Missing statistical significance tests
No comparison with state-of-the-art on standard benchmarks
Interesting ideas valued over exhaustive evaluation
Novel applications of existing methods are acceptable
Journals:
Expect thorough related work discussion
Deeper analysis and more experiments than conferences
Writing quality and organization matter more
Step 3: Question Generation
Generate likely reviewer questions, ranked by probability:
For each question:
The question — phrased as a reviewer would write it
Why they'd ask — what triggers this concern
Can existing data answer it? — yes (point to specific data) or no (new experiment needed)
Draft response — if answerable, write a concise response
Template:
Q: [Reviewer question]
Motivation: [Why this would be asked]
Answerable: [Yes — cite Table X / No — would need experiment Y]
Draft response: [If answerable, 2-3 sentences]
Generate at least 10 questions, prioritized by likelihood.
Step 4: Ablation Selection
From all available experiments, select the subset that:
Proves the core contribution — the single most important ablation
Shows each component's value — incremental additions showing improvement
Tells a coherent story — the progression makes narrative sense
Ranking criteria for each ablation:
Impact magnitude: how much does it change the primary metric?
Narrative strength: does it clearly support a specific claim?
Uniqueness: does it show something no other ablation shows?
Cost: main paper vs appendix (based on space constraints)
Step 5: Negative Results
Negative results are valuable when properly framed:
"We explored X but found it did not improve over Y because Z"
This shows thoroughness and provides insight
Frame as "analysis" not "failure"
Include in supplementary if not in main paper
Step 6: Rebuttal Preparation
If responding to actual reviews:
Read ALL reviews before responding to any
Identify common concerns across reviewers
Prioritize: address factual errors first, then major concerns, then minor ones
Be respectful: thank reviewers, acknowledge valid points
Be specific: point to exact sections, tables, figures
New experiments: only promise what you can deliver in the rebuttal period
Rebuttal structure per reviewer:
We thank Reviewer X for their thoughtful feedback.
**[Major concern]**: [Direct response with evidence]
**[Specific question]**: [Concrete answer]
**[Suggestion]**: [How we will incorporate it]
Output Format
Produce:
Weakness table: categorized weaknesses with severity
Top 10 anticipated questions: with answerability and draft responses
Recommended ablation subset: with justification for each
Suggested text edits: specific paragraphs to strengthen before submission
How to use it
Copy the folder
Take fcakyon/reviewer-defense from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
Check the name does not clash
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.