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Agent skill

Reviewer Defense

by fcakyon in fcakyon/phd-skills

A skill your agent uses when the user wants to anticipate reviewer questions, select the strongest ablations to present, prepare rebuttals, or identify paper weaknesses before submission.

MITAuto-check passedResearch & Science

Install Reviewer Defense

skills CLI
$ npx skills add fcakyon/phd-skills --skill reviewer-defense -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install fcakyon/phd-skills reviewer-defense --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/fcakyon/phd-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugin/skills/reviewer-defense .claude/skills/reviewer-defense && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
reviewer-defense
GitHub stars
415
Token cost
~1.2k tokens
SKILL.md length
511 words
Files
1
Skills in repo
12
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when the user wants to anticipate reviewer questions, select the strongest ablations to present, prepare rebuttals, or identify paper weaknesses before submission.

  • Works in 6 steps: Vulnerability Analysis → Venue-Specific Anticipation → Question Generation → …
  • The user wants to anticipate reviewer questions
  • SKILL.md covers Step 1: Vulnerability Analysis, Step 2: Venue-Specific…, Step 3: Question Generation and Step 4: Ablation Selection, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Reviewer Defense is an agent skill from fcakyon/phd-skills. 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".

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Research & Science, covering Peer review. The repository describes itself as: PhD Research Skills for Claude Code: paper reproduction, experiment design, paper review, result comparison and more. The licence is MIT.

When your agent uses it

  • The user wants to anticipate reviewer questions
  • Select the strongest ablations to present
  • Prepare rebuttals
  • Identify paper weaknesses before submission

Example prompts

  • “reviewer questions”
  • “anticipate reviewers”
  • “rebuttal”
  • “/reviewer-defense”

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Vulnerability Analysis
  2. Venue-Specific Anticipation
  3. Question Generation
  4. Ablation Selection
  5. Negative Results
  6. Rebuttal Preparation

What it can do on your machine

Read from SKILL.md and the folder at commit 67acd61. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Reviewer Defense loads about 1.2k tokens when it runs. Until then it costs about 84 tokens; SKILL.md has 511 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~84
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from fcakyon/phd-skills at commit 67acd61, republished under its MIT licence (© fcakyon). 511 words, ~1,179 tokens.

Download SKILL.mdSave it as .claude/skills/reviewer-defense/SKILL.md (or your agent's skills folder).
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.

Show full SKILL.md (241 more words)Show less

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

© fcakyon, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in plugin/skills/reviewer-defense of fcakyon/phd-skills.

Open the folder on GitHubat commit 67acd61

Compare with similar skills

Reviewer Defense next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Reviewer Defense compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Reviewer Defense this skillfcakyon/phd-skills415—~1.2kAutomated safety check: PassMIT
Peer Reviewspacering-net/codeg3.9k17 repos~5.9kAutomated safety check: NotesMIT
Scholar Evaluationspacering-net/codeg3.9k11 repos~3.2kAutomated safety check: PassMIT
Academic Paper Writing PipelineImbad0202/academic-research-skills51k—~16kAutomated safety check: PassCustom licence
Academic Paper ReviewerImbad0202/academic-research-skills51k—~11kAutomated safety check: PassCustom licence
Peer ReviewK-Dense-AI/claude-scientific-writer2.4k2 repos~3.1kAutomated safety check: NotesMIT

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Categories

Questions about Reviewer Defense

What does Reviewer Defense do?

A skill your agent uses when the user wants to anticipate reviewer questions, select the strongest ablations to present, prepare rebuttals, or identify paper weaknesses before submission. Reviewer Defense is an agent skill from fcakyon/phd-skills. Use when the user wants to anticipate reviewer questions, select the strongest ablations to present, prepare rebuttals, or identify paper weaknesses before submission.

When should I use Reviewer Defense?

Reviewer Defense fits situations like: the user wants to anticipate reviewer questions; select the strongest ablations to present; prepare rebuttals; identify paper weaknesses before submission.

How do I install Reviewer Defense in Claude Code?

Run `npx skills add fcakyon/phd-skills --skill reviewer-defense -a claude-code`. Or copy the skill folder (plugin/skills/reviewer-defense in fcakyon/phd-skills) into .claude/skills/reviewer-defense in your project. Claude Code loads it when a task matches its description.

How do I install Reviewer Defense in Codex?

Run `npx skills add fcakyon/phd-skills --skill reviewer-defense -a codex`. Or copy the skill folder (plugin/skills/reviewer-defense in fcakyon/phd-skills) into .agents/skills/reviewer-defense in your project. Codex loads it when a task matches its description.

Can I use Reviewer Defense in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add fcakyon/phd-skills --skill reviewer-defense -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/reviewer-defense, .gemini/skills/reviewer-defense, .github/skills/reviewer-defense and .opencode/skills/reviewer-defense in your project.

What does Reviewer Defense need to run?

SKILL.md names no scripts, command-line tools or credentials: Reviewer Defense is instructions for the agent only.

Does Reviewer Defense access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Reviewer Defense safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Reviewer Defense use?

Reviewer Defense is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Reviewer Defense use?

About 1.2k tokens (SKILL.md is roughly 4.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Reviewer Defense?

Skills that share tags, products or a category with Reviewer Defense: Peer Review (spacering-net/codeg, 3.9k stars), Scholar Evaluation (spacering-net/codeg, 3.9k stars), Academic Paper Writing Pipeline (Imbad0202/academic-research-skills, 51k stars) and Academic Paper Reviewer (Imbad0202/academic-research-skills, 51k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Reviewer Defense?

fcakyon (a GitHub user) maintains it in fcakyon/phd-skills, which has 415 GitHub stars. The repository holds 12 skills in this directory. The repository was last updated on September 16, 2026.

Source: fcakyon/phd-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.