compare
Same-epoch comparison of training runs across wandb, neptune, tensorboard, or mlflow. Aligns runs at the student's current step…
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
$ npx -y skills add fcakyon/phd-skills --skill reviewer-defense --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/reviewer-defenseContext preview
The summary Claude sees to decide when to auto-load this skill.
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
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".
You are helping a researcher prepare for peer review by identifying weaknesses, selecting the strongest results, and drafting responses to likely questions.
Read the paper and identify weaknesses from a reviewer's perspective:
Different venues have different review cultures:
**Top-tier ML/CV conferences (CVPR, NeurIPS, ICLR, ECCV)**:
**Workshops**:
**Journals**:
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.
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:
Negative results are valuable when properly framed:
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]
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
Catch AI mistakes before they cost weeks of compute. Reproduce papers from arxiv. Debug runs evidence-first. Compare experiments at the right epoch. Launch with discipline.
Same-epoch comparison of training runs across wandb, neptune, tensorboard, or mlflow. Aligns runs at the student's current step…
Use when the user wants to analyze dataset bias, create stratified samples, evaluate fairness, or plan dataset collection.…
Evidence-before-action diagnosis of failing ML experiments. Probes the system before guessing causes, process list, dmesg, GPU…
Use when the user wants to design experiments, plan ablation studies, structure baselines, or create incremental evaluation…
Use when the user wants to set up or troubleshoot a LaTeX environment, choose between biber and bibtex, install packages for a…