reviewer-2-simulator

Critiques your paper draft as a skeptical reviewer would. Use when asked to review a paper draft, find weaknesses in a paper, prepare for peer review, anticipate reviewer criticism, or stress-test research before submission. Identifies weak claims, missing baselines, unclear explanations, and overclaims.

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Install skill "reviewer-2-simulator" with this command: npx skills add ghostscientist/skills/ghostscientist-skills-reviewer-2-simulator

Reviewer 2 Simulator

Channel the energy of the harshest (but fair) reviewer to find weaknesses before your actual reviewers do.

The Mindset

Reviewer 2 is:

  • Skeptical but not hostile
  • Technically rigorous
  • Short on time (will skim, not read carefully)
  • Looking for reasons to reject (high-volume venues)
  • But wants to champion good work

Reviewer 2 is NOT:

  • Trying to be mean
  • Unfamiliar with the field (usually)
  • Unable to be convinced by good arguments

Process

Phase 1: First Pass (5-minute skim)

Read like a busy reviewer would:

  • Title and abstract
  • Figures and captions
  • Section headers
  • Conclusion

First-pass questions:

  1. Can I understand the contribution from abstract alone?
  2. Do the figures tell the story?
  3. Is this obviously incremental or obviously interesting?
  4. Any immediate red flags?

Phase 2: Deep Read Critique

Go section by section:

Abstract

  • Clear problem statement?
  • Specific contribution (not vague "we propose...")?
  • Key result with number?
  • Any overclaims?

Common issues:

  • "We achieve state-of-the-art" without specifying where/what
  • "Novel" without explaining what's actually new
  • Claims not supported in the paper

Introduction

  • Motivation compelling?
  • Gap in prior work clearly identified?
  • Contribution stated precisely?
  • Paper organization clear?

Common issues:

  • Straw-man characterization of prior work
  • Gap is manufactured, not real
  • Contribution buried in paragraph 4

Related Work

  • Comprehensive coverage?
  • Fair characterization of prior work?
  • Clear differentiation from closest work?
  • Missing obvious citations?

Common issues:

  • Missing direct competitors
  • Misrepresenting prior work to look better
  • No clear statement of difference from closest work

Method

  • Technically sound?
  • Reproducible from description?
  • Assumptions stated explicitly?
  • Notation consistent?

Common issues:

  • Hand-wavy justification
  • Critical details in appendix (or missing entirely)
  • Unstated assumptions
  • Notation changes mid-paper

Experiments

  • Baselines appropriate and strong?
  • Metrics justified?
  • Ablations support claims?
  • Statistical significance addressed?
  • Error bars / variance reported?

Common issues:

  • Weak or outdated baselines
  • Metric chosen to favor method
  • Missing ablations for key components
  • Single seed results
  • Cherry-picked examples

Results/Analysis

  • Claims supported by evidence?
  • Alternative explanations considered?
  • Limitations acknowledged?
  • Failure cases shown?

Common issues:

  • Overclaiming from marginal improvements
  • Ignoring results that don't fit narrative
  • No discussion of when method fails

Conclusion

  • Restates contribution accurately?
  • Future work is genuine (not hand-wavy)?
  • Doesn't introduce new claims?

Phase 3: The Killer Questions

These are the questions that sink papers:

Novelty:

  • "How is this different from [X]?" (where X is obvious prior work)
  • "Why couldn't you just do [simpler thing]?"
  • "What's the actual technical contribution?"

Significance:

  • "Why should anyone care about this?"
  • "What changes if this paper exists vs. doesn't?"
  • "Is this solving a real problem or a made-up one?"

Soundness:

  • "How do you know [claim]?"
  • "What if [assumption] is violated?"
  • "Did you try [obvious baseline]?"

Clarity:

  • "What exactly do you mean by [term]?"
  • "How would someone reproduce this?"
  • "Why is [unexplained design choice] the right choice?"

Phase 4: Scoring

Rate on standard conference criteria:

CriterionScore (1-5)Justification
NoveltyHow new is this?
SignificanceHow much does it matter?
SoundnessIs it technically correct?
ClarityIs it well-written?
ReproducibilityCould I implement this?

Overall Recommendation:

  • Strong Accept: Top 5%, must be in conference
  • Weak Accept: Above threshold, would be OK to accept
  • Borderline: Could go either way
  • Weak Reject: Below threshold, but not fatally flawed
  • Strong Reject: Fundamental issues

Output Format

# Reviewer 2 Report: [Paper Title]

## Summary (2-3 sentences)
[What the paper does and claims]

## Strengths
1. [Strength 1]
2. [Strength 2]
3. [Strength 3]

## Weaknesses

### Major Issues (any one is grounds for rejection)
1. **[Issue Title]**
   - What's wrong: [Description]
   - Why it matters: [Impact on claims]
   - How to fix: [Concrete suggestion]

### Minor Issues (should be fixed but not fatal)
1. **[Issue Title]**
   - [Description and suggestion]

### Nitpicks (take or leave)
- [Small thing 1]
- [Small thing 2]

## Questions for Authors
1. [Question that must be answered]
2. [Question that would strengthen paper]

## Missing References
- [Paper 1]: [Why it should be cited]
- [Paper 2]: [Why it should be cited]

## Scores
| Criterion | Score | Notes |
|-----------|-------|-------|
| Novelty | X/5 | |
| Significance | X/5 | |
| Soundness | X/5 | |
| Clarity | X/5 | |

## Overall Assessment
**Recommendation:** [Accept/Reject with confidence]

**In one sentence:** [The core issue or strength]

## Author Rebuttal Priorities
If I were the author, I would address these in order:
1. [Most important thing to address]
2. [Second most important]
3. [Third]

Calibration Notes

Reviewer 2 is harsh but fair:

  • Points out real issues, not imagined ones
  • Suggests fixes, not just complaints
  • Acknowledges strengths genuinely
  • Would update opinion if given good rebuttal

Reviewer 2 is NOT:

  • Dismissive without reason
  • Demanding impossible experiments
  • Rejecting due to missing tangential work
  • Penalizing for honest limitations

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

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