Paper Reviewer

When creating documents with this skill, consider adding diagrams when they clarify a workflow, architecture, or evaluation framework.

If the document does not already contain suitable figures:

• Use the inno-figure-gen skill to generate publication-style diagrams.
• Describe the desired figure in natural language and specify academic style constraints.
• Iterate prompts until the figure is clear, readable, and suitable for review materials.

Example command:

uv run ~/.codex/skills/inno-figure-gen/scripts/generate_image.py \
  --prompt "Publication-style diagram of the review workflow; white background; clean labels; colorblind-friendly palette; high contrast" \
  --filename "figures/review-workflow.png" \
  --resolution 2K

Requires GEMINI_API_KEY or an explicit --api-key.

When to add figures:

• Peer review workflow diagrams
• Evaluation criteria decision trees
• Review process flowcharts
• Methodology assessment frameworks
• Quality assessment visualizations
• Reporting guidelines compliance diagrams
• Any complex concept that benefits from visualization

For detailed guidance on creating figures, refer to the inno-figure-gen skill documentation.

Peer Review Workflow

Conduct peer review systematically through the following stages, adapting depth and focus based on the manuscript type and discipline.

Stage 1: Initial Assessment

Begin with a high-level evaluation to determine the manuscript's scope, novelty, and overall quality.

Key Questions:

• What is the central research question or hypothesis?
• What are the main findings and conclusions?
• Is the work scientifically sound and significant?
• Is the work appropriate for the intended venue?
• Are there any immediate major flaws that would preclude publication?

Output: Brief summary (2-3 sentences) capturing the manuscript's essence and initial impression.

Stage 2: Detailed Section-by-Section Review

Conduct a thorough evaluation of each manuscript section, documenting specific concerns and strengths.

Abstract and Title

• Accuracy: Does the abstract accurately reflect the study's content and conclusions?
• Clarity: Is the title specific, accurate, and informative?
• Completeness: Are key findings and methods summarized appropriately?
• Accessibility: Is the abstract comprehensible to a broad scientific audience?

Introduction

• Context: Is the background information adequate and current?
• Rationale: Is the research question clearly motivated and justified?
• Novelty: Is the work's originality and significance clearly articulated?
• Literature: Are relevant prior studies appropriately cited?
• Objectives: Are research aims/hypotheses clearly stated?

Methods

• Reproducibility: Can another researcher replicate the study from the description provided?
• Rigor: Are the methods appropriate for addressing the research questions?
• Detail: Are protocols, reagents, equipment, and parameters sufficiently described?
• Ethics: Are ethical approvals, consent, and data handling properly documented?
• Statistics: Are statistical methods appropriate, clearly described, and justified?
• Validation: Are controls, replicates, and validation approaches adequate?

Critical elements to verify:

• Sample sizes and power calculations
• Randomization and blinding procedures
• Inclusion/exclusion criteria
• Data collection protocols
• Computational methods and software versions
• Statistical tests and correction for multiple comparisons

Results

• Presentation: Are results presented logically and clearly?
• Figures/Tables: Are visualizations appropriate, clear, and properly labeled?
• Statistics: Are statistical results properly reported (effect sizes, confidence intervals, p-values)?
• Objectivity: Are results presented without over-interpretation?
• Completeness: Are all relevant results included, including negative results?
• Reproducibility: Are raw data or summary statistics provided?

Common issues to identify:

• Selective reporting of results
• Inappropriate statistical tests
• Missing error bars or measures of variability
• Over-fitting or circular analysis
• Batch effects or confounding variables
• Missing controls or validation experiments

Discussion

• Interpretation: Are conclusions supported by the data?
• Limitations: Are study limitations acknowledged and discussed?
• Context: Are findings placed appropriately within existing literature?
• Speculation: Is speculation clearly distinguished from data-supported conclusions?
• Significance: Are implications and importance clearly articulated?
• Future directions: Are next steps or unanswered questions discussed?

Red flags:

• Overstated conclusions
• Ignoring contradictory evidence
• Causal claims from correlational data
• Inadequate discussion of limitations
• Mechanistic claims without mechanistic evidence

References

• Completeness: Are key relevant papers cited?
• Currency: Are recent important studies included?
• Balance: Are contrary viewpoints appropriately cited?
• Accuracy: Are citations accurate and appropriate?
• Self-citation: Is there excessive or inappropriate self-citation?