reviewer-defense

reviewer-defense is a skill for Claude Code from fcakyon/phd-skills. It costs 71 tokens per session (1,015 once invoked), scanned A, original, MIT.

A review-preparation aid for researchers getting an academic paper ready for peer review, where other experts assess the work before publication.

In plain words
What is it for?
It is for anticipating reviewer questions, choosing useful ablation studies, drafting rebuttals, checking missing baselines or analyses, and identifying technical, experimental, and presentation problems.
Why use it?
It helps reveal weaknesses and likely objections before submission, giving the author time to strengthen the evidence and prepare responses.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the phd-skills plugin — 12 skills, 6 commands, 2 agents, 4 hooks shipped together

Good fit It is for anticipating reviewer questions, choosing useful ablation studies, drafting rebuttals, checking missing baselines or analyses, and identifying technical, experimental, and presentation problems.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/fcakyon/phd-skills/reviewer-defense
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add fcakyon/phd-skills --skill reviewer-defense
Clone the repo
git clone --depth 1 https://github.com/fcakyon/phd-skills

Made for: Claude Code.

Or install phd-skills, the plugin that ships this one along with the rest of its 12 skills, 6 commands, 2 agents, 4 hooks.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for reviewer-defense

README.md
[![agentmods](https://agentmods.dev/badge/skills/fcakyon/phd-skills/reviewer-defense/github.svg)](https://agentmods.dev/skills/fcakyon/phd-skills/reviewer-defense)
Your own site
<a href="https://agentmods.dev/skills/fcakyon/phd-skills/reviewer-defense"><img src="https://agentmods.dev/badge/skills/fcakyon/phd-skills/reviewer-defense/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for reviewer-defense

Your own site · 80×15
<a href="https://agentmods.dev/skills/fcakyon/phd-skills/reviewer-defense"><img src="https://agentmods.dev/badge/skills/fcakyon/phd-skills/reviewer-defense.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 71 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,015 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00071 $0.01015
Opus 5 $0.00036 $0.00508
Sonnet 5 $0.00014 $0.00203
Haiku 4.5 $0.00007 $0.00102

Measured 11d ago against content hash 99a87d2e4a2f, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

reviewer-defense scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugin/skills/reviewer-defense/SKILL.md · 133 lines

How it starts

The opening of the file, as written. The whole thing — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

Read the full file on GitHub · 133 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 11d ago First seen · 133 lines · 71 tokens per session scan A 99a87d2e4a2f

Subscribe to this mod's changes

reviewer-defense is a skill published in the GitHub repository fcakyon/phd-skills (389 stars, last pushed 2mo ago), licensed MIT. It adds 71 tokens to every session and 1,015 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.