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reviewer-defense

Use when the user wants to anticipate reviewer questions, select the strongest ablations to present, prepare rebuttals, or identify paper weaknesses before submission.

Install / Use

npx skills add fcakyon/phd-skills --skill reviewer-defense

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

85/100

Category

Security

Supported Platforms

Universal

Our assessment of reviewer-defense

reviewer-defense scores 85/100 on our quality scale, 769th of 1,083 Security skills we index.

Its SKILL.md is 4.6 KB long, well organised into 11 sections with 2 code examples: a solid amount of guidance for an agent.

It has 406 GitHub stars, a meaningful sign that others use it.

Substance
26/30
Structure
18/20
Description
15/15
Adoption
11/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 18 days ago, so reviewer-defense is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

reviewer-defense compared with similar skills

All 4 of these similar skills score higher than reviewer-defense; compare them before choosing.

SkillScoreStarsUpdatedFormat
reviewer-defense (this skill)by fcakyon8540618d agoSKILL.md
algorithmic-artby anthropics100177.9k12d agoSKILL.md
pptxby anthropics100177.9k12d agoSKILL.md
designby nextlevelbuilder100130.2k13d agoSKILL.md
ui-ux-pro-maxby nextlevelbuilder100130.2k13d agoSKILL.md

Frequently asked questions

How do I install reviewer-defense?
Run npx skills add fcakyon/phd-skills --skill reviewer-defense. The install tabs above show the steps for each supported agent.
Which AI agents does reviewer-defense work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is reviewer-defense safe to use?
It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is reviewer-defense still maintained?
The repository was last updated 18 days ago, so reviewer-defense is actively maintained.

name: reviewer-defense description: > 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".

Reviewer Defense Methodology

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
  • Hyperparameter sensitivity not explored
  • No computational cost comparison

Step 2: Venue-Specific Anticipation

Different venues have different review cultures:

Top-tier ML/CV conferences (CVPR, NeurIPS, ICLR, ECCV):

  • Expect extensive ablation studies
  • Strong baseline comparisons required
  • Novelty must be clearly articulated
  • Reproducibility is valued

Workshops:

  • More tolerant of work-in-progress
  • 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:

  1. The question — phrased as a reviewer would write it
  2. Why they'd ask — what triggers this concern
  3. Can existing data answer it? — yes (point to specific data) or no (new experiment needed)
  4. 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:

  1. Proves the core contribution — the single most important ablation
  2. Shows each component's value — incremental additions showing improvement
  3. Addresses anticipated weaknesses — preemptively answers likely questions
  4. 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:

  1. Read ALL reviews before responding to any
  2. Identify common concerns across reviewers
  3. Prioritize: address factual errors first, then major concerns, then minor ones
  4. Be respectful: thank reviewers, acknowledge valid points
  5. Be specific: point to exact sections, tables, figures
  6. 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:

  1. Weakness table: categorized weaknesses with severity
  2. Top 10 anticipated questions: with answerability and draft responses
  3. Recommended ablation subset: with justification for each
  4. Suggested text edits: specific paragraphs to strengthen before submission

Related Skills

View on GitHub
GitHub Stars406
CategorySecurity
Updated18d ago
Forks34

Languages

Shell

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions