Master Peer Suite

Conduct systematic peer review analysis and manage the peer review process for academic manuscripts. This skill covers review generation, manuscript evaluation frameworks, referee report structuring, and tools for improving research quality through critical assessment.

When to Use This Skill

Choose Master Peer Suite when you need to:

• Structure peer review feedback for academic manuscripts using standard frameworks
• Evaluate research methodology, statistical analysis, and presentation quality
• Generate reviewer checklists tailored to specific journal or conference guidelines
• Organize multiple rounds of revision with tracked reviewer responses

Consider alternatives when:

• You need to write the research paper itself (use academic writing tools)
• You need plagiarism detection (use Turnitin or iThenticate)
• You need reference management and citation formatting (use Zotero or BibTeX)

Quick Start

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pip install python-docx pandas jinja2

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from dataclasses import dataclass, field
from typing import List

@dataclass
class ReviewCriterion:
    category: str
    question: str
    score: int = 0  # 1-5 scale
    comment: str = ""

@dataclass
class PeerReview:
    manuscript_title: str
    reviewer: str
    criteria: List[ReviewCriterion] = field(default_factory=list)
    recommendation: str = ""  # accept, minor, major, reject
    confidential_notes: str = ""

    def overall_score(self):
        if not self.criteria:
            return 0
        return sum(c.score for c in self.criteria) / len(self.criteria)

# Create a review
review = PeerReview(
    manuscript_title="A Novel Approach to Protein Folding",
    reviewer="Reviewer 1"
)

# Standard review criteria
criteria = [
    ReviewCriterion("Originality", "Does the work present novel findings?"),
    ReviewCriterion("Methods", "Are the methods appropriate and rigorous?"),
    ReviewCriterion("Results", "Are the results clearly presented?"),
    ReviewCriterion("Analysis", "Is the statistical analysis sound?"),
    ReviewCriterion("Writing", "Is the manuscript well-written?"),
    ReviewCriterion("Figures", "Are figures clear and informative?"),
    ReviewCriterion("References", "Is the literature review comprehensive?"),
]
review.criteria = criteria

Core Concepts

Review Framework Components

Component	Purpose	Output
Summary	Describe the paper's contribution	2-3 sentence overview
Strengths	Identify positive aspects	Numbered list of merits
Weaknesses	Identify areas for improvement	Numbered list with specifics
Major Issues	Critical problems requiring revision	Detailed with suggestions
Minor Issues	Small corrections and suggestions	Line-specific feedback
Questions	Clarifications needed from authors	Numbered questions
Recommendation	Accept/Minor/Major/Reject	With justification

Structured Review Generation

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from docx import Document
from datetime import datetime

def generate_review_report(review, output_path="review_report.docx"):
    """Generate a formatted peer review document."""
    doc = Document()
    doc.add_heading("Peer Review Report", 0)

    # Header information
    doc.add_paragraph(f"Manuscript: {review.manuscript_title}")
    doc.add_paragraph(f"Reviewer: {review.reviewer}")
    doc.add_paragraph(f"Date: {datetime.now().strftime('%Y-%m-%d')}")
    doc.add_paragraph(f"Recommendation: {review.recommendation.upper()}")
    doc.add_paragraph(
        f"Overall Score: {review.overall_score():.1f}/5.0"
    )

    # Criteria scores
    doc.add_heading("Evaluation Criteria", level=1)
    table = doc.add_table(rows=1, cols=3)
    table.style = "Table Grid"
    header = table.rows[0].cells
    header[0].text = "Category"
    header[1].text = "Score"
    header[2].text = "Comments"

    for criterion in review.criteria:
        row = table.add_row().cells
        row[0].text = f"{criterion.category}: {criterion.question}"
        row[1].text = f"{criterion.score}/5"
        row[2].text = criterion.comment

    # Detailed sections
    sections = {
        "Summary": "Brief summary of the manuscript and its contributions.",
        "Strengths": "Key strengths of the work.",
        "Major Issues": "Critical issues that must be addressed.",
        "Minor Issues": "Smaller improvements suggested.",
        "Questions for Authors": "Clarifications needed."
    }

    for heading, description in sections.items():
        doc.add_heading(heading, level=1)
        doc.add_paragraph(description)

    doc.save(output_path)
    print(f"Review saved to {output_path}")

generate_review_report(review)

Multi-Reviewer Consensus

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import pandas as pd

def consensus_analysis(reviews):
    """Analyze agreement across multiple reviewers."""
    # Build score comparison matrix
    all_categories = set()
    for review in reviews:
        for c in review.criteria:
            all_categories.add(c.category)

    scores = {}
    for review in reviews:
        reviewer_scores = {}
        for c in review.criteria:
            reviewer_scores[c.category] = c.score
        scores[review.reviewer] = reviewer_scores

    df = pd.DataFrame(scores)

    # Agreement analysis
    for cat in df.index:
        row = df.loc[cat].dropna()
        if len(row) > 1:
            spread = row.max() - row.min()
            agreement = "High" if spread <= 1 else "Low" if spread >= 3 else "Moderate"
            print(f"{cat}: mean={row.mean():.1f}, spread={spread}, agreement={agreement}")

    # Overall recommendation consensus
    recommendations = [r.recommendation for r in reviews]
    print(f"\nRecommendations: {recommendations}")
    from collections import Counter
    rec_counts = Counter(recommendations)
    print(f"Consensus: {rec_counts.most_common(1)[0][0]}")

    return df

Configuration

Parameter	Description	Default
score_scale	Rating scale (1-5 or 1-10)	5
criteria_template	Review criteria set	"standard"
output_format	Report format (DOCX, PDF, MD)	"docx"
anonymize	Remove reviewer names from output	true
track_revisions	Enable revision tracking	true
deadline_days	Review completion deadline	21

Best Practices

1. Be specific and actionable in feedback — Instead of "the methods are weak," write "Section 3.2 lacks detail on how participants were randomized. Please specify the randomization method (block, stratified, simple) and whether allocation concealment was used." Specific feedback is useful; vague criticism is not.

2. Separate major from minor issues clearly — Major issues are problems that could change the paper's conclusions (flawed methodology, missing controls, unsupported claims). Minor issues are improvements that don't affect validity (typos, figure formatting, additional references). Mixing these confuses authors about revision priorities.

3. Evaluate the research, not the researchers — Focus on methodology, analysis, and presentation. Avoid personal comments, assumptions about the authors' experience, or comparisons with other groups. Professional peer review evaluates the work objectively.

4. Provide constructive suggestions alongside criticism — For every major weakness identified, suggest a concrete way to address it. "The sample size of 12 is too small for parametric tests — consider using non-parametric alternatives or collecting additional data" is more helpful than just noting the problem.

5. Disclose conflicts of interest immediately — If you have a competing interest (collaborator, competitor, financial interest), disclose it to the editor and recuse yourself if necessary. Undisclosed conflicts undermine the entire review process.

Common Issues

Review feedback is too vague to act on — Authors receive comments like "improve the analysis" without knowing what specifically to change. Structure every critique as: (1) what the issue is, (2) where it appears in the manuscript, (3) why it matters, and (4) how to fix it.

Inconsistent scoring across reviewers — Different reviewers calibrate their scales differently — one reviewer's 3/5 is another's 4/5. Provide detailed scoring rubrics with anchor descriptions for each level (e.g., "5 = outstanding, among top 10% in field; 3 = adequate but could be improved; 1 = fundamentally flawed"). This reduces inter-reviewer variability.

Revision responses don't address all comments — Authors may miss or skip difficult reviewer comments during revision. Create a numbered point-by-point response template requiring authors to address each comment individually with the specific changes made and their location in the revised manuscript.