Agent skill
paper-rebuttal
Guides writing effective rebuttals after receiving peer review feedback. Covers review diagnosis (score-driven color-coding), response strategy (champion identification, common-theme consolidation), tactical writing (18 rules), and counterintuitive rebuttal principles. Use when: user received reviewer scores/comments, needs to write a rebuttal or author response, wants to respond to specific criticism (e.g. 'limited novelty', 'missing baselines'), mentions 'rebuttal', 'reviewer comments', 'author response', or 'respond to reviewers'. Do NOT use for pre-submission self-review (use paper-review instead).
Install this agent skill to your Project
npx add-skill https://github.com/EvoScientist/EvoSkills/tree/main/skills/paper-rebuttal
Metadata
Additional technical details for this skill
- tags
-
core writing academic-writing peer-review
- author
- EvoScientist
- version
- 1.0.0
SKILL.md
Paper Rebuttal
A systematic approach to writing rebuttals after receiving peer review feedback. The goal is not to defend every point — it's to move scores by addressing the concerns that actually drive them.
When to Use This Skill
- User received reviewer comments and needs to write a rebuttal
- User asks how to respond to specific reviewer criticism
- User wants to analyze reviews strategically before responding
- User mentions "rebuttal", "reviewer comments", "review feedback", "respond to reviewers"
For pre-submission self-review and catching weaknesses before they become reviewer complaints, use the
paper-reviewskill.
Step 1: Diagnose Reviews
Before writing a single word, answer: "Why did this reviewer give this exact score?" Not what they wrote — what drove the score. Most researchers skip this and address every comment equally. That is a mistake.
Score Diagnosis
For each reviewer, ask: "What would move this reviewer from their current score to acceptance?"
| Score Range | Typical Situation | Your Strategy |
|---|---|---|
| 7+ | Already your champion | Arm them with ammunition for the discussion phase |
| 5-6 | On the fence, 1-2 concerns holding them back | Identify and resolve those specific concerns |
| 3-4 | Fundamental objection | Determine if the objection is addressable; if not, focus elsewhere |
Color-Code Every Comment
Read through each review and mark every comment:
| Color | Meaning | Action | Budget |
|---|---|---|---|
| Red | Score-driving concern — this is why the score is low | Address first, maximum effort and evidence | 60% |
| Orange | Addressable concern — can be resolved | Respond with concrete data or revision | 30% |
| Gray | Minor or cosmetic | Acknowledge briefly, confirm fix | 10% |
| Green | Positive comment or praise | Note as ammunition for your champion | — |
Identify the Invisible Question
Behind every reviewer comment is an unspoken question. A comment like "The baselines are outdated" really asks: "Is this method actually competitive with current approaches?" Address the invisible question, not just the surface request.
Step 2: Plan Response Strategy
Categorize Every Concern
| Category | Response Strategy |
|---|---|
| Misunderstanding | Clarify with specific references to the paper; restate the key point |
| Missing experiment | Provide the experiment inline if feasible; otherwise explain constraints honestly |
| Missing baseline | Add comparison or explain precisely why the baseline is not applicable |
| Writing clarity | Acknowledge and provide revised text in the rebuttal |
| Fundamental concern | Address directly with technical arguments AND additional evidence |
| Minor issue | Thank the reviewer and confirm the fix |
Identify Common Themes
If multiple reviewers raise the same concern, it's almost certainly a real weakness. Consolidate these into a "Common Response" section — this saves word count and demonstrates that you understand the pattern.
Distinguish Actionable vs. Subjective
- Actionable: "Missing comparison with Method X" — you can do this
- Subjective: "The novelty is limited" — harder to address, but can be reframed with evidence
The Champion Strategy
Your rebuttal's real audience is not the negative reviewer — it's the positive one.
Your champion argues on your behalf in the AC discussion, often using your exact words. Write your rebuttal to arm them:
- Make key arguments copy-pasteable — your champion will quote you directly
- Highlight where reviewers agree with each other — consensus strengthens the champion's position
- Flag contradictions between reviewers — if R1 says "limited novelty" but R2 says "interesting approach," your champion can use this
- Lead with strengths before weaknesses — remind the AC what your paper does well
See references/rebuttal-tactics.md for the full 18 tactical rules.
Step 3: Write the Rebuttal
Structure
- Opening: One line thanking reviewers (keep it short)
- Common concerns: Address issues raised by multiple reviewers first — these are highest priority
- Per-reviewer responses: Address remaining concerns in priority order (red → orange → gray), NOT in the order the reviewer wrote them
Per-Concern Format
For each concern, follow this three-part structure:
- Acknowledge: Show you understand the concern (one sentence)
- Respond: Provide your answer — evidence, clarification, new experiment results
- Action: State what you changed in the revision (specific section/table/figure)
Use a fillable template at assets/rebuttal-template.md.
The Neutral Third-Party Test
Before submitting, have someone who hasn't read your paper read only the reviews and your rebuttal. Ask: "Can you tell whether the concerns were addressed?" If not, rewrite.
Counterintuitive Rebuttal Principles
-
Submit a rebuttal even with extreme scores. A paper with scores of 3/8/8 has better odds than you think. The negative reviewer may realize they are an outlier during discussion. But only if you submit a rebuttal — without one, the AC has nothing to work with.
-
Concede something small, win something big. Acknowledging a minor weakness ("We agree that Table 2 could include dataset X for completeness") makes your defense of major points more credible. Pure defense with zero concession reads as unobjective.
-
One new experiment beats three paragraphs of explanation. Reviewers are trained to be skeptical of arguments. They are not trained to be skeptical of data. A small new experiment that directly addresses a concern is worth more than any amount of reasoning.
-
The best rebuttal is written before submission. Draft responses to likely attacks while writing the paper ("prebuttal"). Two benefits: you often realize the attack is valid and fix the paper, and if the attack comes, you have a polished response ready.
-
Don't defend every point equally. Equal effort signals you don't know which points matter. Allocate your word budget according to the color-coding: 60% red, 30% orange, 10% gray. Reviewers notice when you nail the big issues.
Common Reviewer Concerns
Prepare responses for these frequent concerns. Having a prepared response doesn't mean copying it verbatim — adapt to your specific paper and the reviewer's specific framing.
| Common Concern | Response Strategy |
|---|---|
| "Limited novelty" | Articulate the specific insight; show what prior work cannot do; narrow and sharpen the claim |
| "Marginal improvement" | Emphasize other advantages (speed, generalizability, simplicity); add challenging test cases |
| "Missing ablations" | Provide the ablation table inline in the rebuttal |
| "Missing baselines" | Add the comparison or explain precisely why it's not applicable |
| "Not reproducible" | Add implementation details; commit to code release with a specific timeline |
| "Limited evaluation" | Add diverse datasets or metrics; if infeasible, explain resource constraints honestly |
| "No limitation discussed" | Add a limitation section in the revision; acknowledge this was an oversight |
| "Overclaimed results" | Weaken specific claims to match evidence; show the revised wording |
| "Unfair comparison" | Use standard evaluation protocols; add commonly reported baselines |
| "Method is engineering, not research" | Identify the scientific insight behind the design; explain why the choice is non-obvious |
| "Metrics don't match claims" | Align each claim with a specific metric; add the missing metric if feasible |
| "Related work incomplete" | Add the missing references; explain the relationship to your work |
Need to run new experiments for the rebuttal? Use the
experiment-craftskill for targeted debugging, orexperiment-pipelinefor a full new experiment stage.
Handoff from Paper Review
This skill picks up where paper-review leaves off. If you used paper-review before submission, these artifacts are especially useful for rebuttal:
| Artifact from paper-review | How It Helps Rebuttal |
|---|---|
| Reject-first simulation | You've already anticipated likely attacks |
| Claim-evidence audit table | Quickly verify whether a reviewer's concern about unsupported claims is valid |
| Prebuttal drafts (Phase 6) | Ready-made response templates for common criticisms |
| Trust scorecard | Identifies weaknesses you can proactively concede |
Reference Navigation
| Topic | Reference File | When to Use |
|---|---|---|
| 18 tactical rules | rebuttal-tactics.md | Detailed writing guidance for structure, content, tone |
| Rebuttal template | rebuttal-template.md | Starting a new rebuttal document |
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
paper-writing
Guides writing academic papers section by section using an 11-step workflow with LaTeX templates and counterintuitive writing tactics. Covers Abstract, Introduction, Method, Experiments, Related Work, Conclusion, and Supplementary. Use when: user asks to write or draft a paper section, needs LaTeX templates, wants to improve academic writing quality, optimize novelty framing, or mentions 'write introduction', 'draft method', 'paper writing'. Do NOT use for pre-submission review (use paper-review), experiment execution (use experiment-pipeline), or paper planning/story design (use paper-planning).
evo-memory
Manages persistent research memory across ideation and experimentation cycles. Maintains two stores: Ideation Memory M_I (feasible/unsuccessful directions) and Experimentation Memory M_E (reusable strategies for data processing, model training, architecture, debugging). Three evolution mechanisms: IDE (after idea-tournament), IVE (after experiment failure — classifies failures as implementation vs fundamental), ESE (after experiment success — extracts reusable strategies). Use when: updating memory after completing idea tournaments or experiment pipelines, classifying why a method failed (implementation vs fundamental failure), starting a new research cycle needing prior knowledge, user mentions 'update memory', 'classify failure', 'what worked before', 'research history', 'evolution'. Do NOT use for running experiments (use experiment-pipeline), debugging experiment code (use experiment-craft), or generating ideas (use idea-tournament).
paper-navigator
End-to-end academic paper workflow: disambiguate queries, discover papers (search, citation traversal, recommendations, arXiv monitoring, trending, GitHub search), evaluate (TLDR, citations, code, SOTA), read with structured analysis (3-level strategy), and organize into literature maps or reports. Use when: finding papers, reading a paper, related work, literature survey, citation analysis, research trends, SOTA results, datasets, or literature reports. Do NOT use for writing a literature review section (use paper-writing), comparing research ideas (use idea-tournament), or planning paper structure (use paper-planning).
paper-review
Guides self-review of YOUR OWN academic paper before submission with adversarial stress-testing. Core method: 5-aspect checklist (contribution sufficiency, writing clarity, results quality, testing completeness, method design), counterintuitive protocol (reject-first simulation, delete unsupported claims, score trust, promote limitations, attack novelty), reverse-outlining, and figure/table quality checks. Use when: user wants to self-review or self-check their own paper draft before submission, stress-test their claims, prepare for reviewer criticism, or mentions 'self-review', 'check my draft', 'is my paper ready'. Do NOT use for writing a peer review of someone else's paper, and do NOT use after receiving actual reviews (use paper-rebuttal instead).
experiment-craft
Use this skill when the user wants to debug, diagnose, or systematically iterate on an experiment that already exists, or when they need a structured experiment log for tracking runs, hypotheses, failures, results, and next steps during active research. Apply it to underperforming methods, training that will not converge, regressions after a change, inconsistent results across datasets, aimless experimentation without progress, and questions like 'why doesn't this work?', 'no progress after many attempts', or 'how should I investigate this failure?'. Also use it for setting up practical experiment logging/record-keeping that supports debugging and iteration. Do not use it for designing a brand-new experiment pipeline or full experiment program (use experiment-pipeline), generating research ideas, fixing isolated coding/syntax errors, or writing retrospective summaries into research memory/notes/knowledge bases.
experiment-pipeline
Guides structured 4-stage experiment execution with attempt budgets and gate conditions: Stage 1 initial implementation (reproduce baseline), Stage 2 hyperparameter tuning, Stage 3 proposed method validation, Stage 4 ablation study. Integrates with evo-memory (load prior strategies, trigger IVE/ESE) and experiment-craft (5-step diagnostic on failure). Use when: user has a planned experiment, needs to reproduce baselines, organize experiment workflow, or systematically validate a method. Do NOT use for debugging a specific experiment failure (use experiment-craft) or designing which experiments to run (use paper-planning).
Didn't find tool you were looking for?