Scholar Evaluation

Scholar Evaluation

Overview

Apply the ScholarEval framework to systematically evaluate scholarly and research work. This skill provides structured evaluation methodology based on peer-reviewed research assessment criteria, enabling comprehensive analysis of academic papers, research proposals, literature reviews, and scholarly writing across multiple quality dimensions.

Mindset

Evaluation is a service to the author, not a verdict. Three principles govern every assessment:

1. Evidence first — every strength and weakness claim must cite a specific section, figure, or sentence from the work. Generic statements ("the methodology is weak") without evidence are useless.
2. Stage-appropriate expectations — a first draft is not a submission-ready manuscript. ALWAYS adjust thresholds to the work's stated stage and purpose before scoring.
3. Constructive framing — identify what needs improving and why it matters, not just what is wrong. The evaluation is complete only when the author knows what to do next.

# Load the evaluation framework before scoring any dimension
# See references/evaluation_framework.md for detailed rubrics

When to Use This Skill

Use this skill when:

• Evaluating research papers for quality and rigor
• Assessing literature review comprehensiveness and quality
• Reviewing research methodology design
• Scoring data analysis approaches
• Evaluating scholarly writing and presentation
• Providing structured feedback on academic work
• Benchmarking research quality against established criteria
• Assessing publication readiness for target venues
• Providing quantitative evaluation to complement qualitative peer review

When Not to Use

• The goal is factual verification (claim checking), not quality assessment — use a fact-check or reproducibility workflow instead
• The work requires domain-specific technical review beyond the ScholarEval dimensions (e.g., clinical safety review, legal analysis)
• The author has requested a positive endorsement rather than an honest evaluation — do not produce a biased report

Visual Enhancement with Scientific Schematics

When creating documents with this skill, always consider adding scientific diagrams and schematics to enhance visual communication.

If your document does not already contain schematics or diagrams:

• Use the scientific-schematics skill to generate AI-powered publication-quality diagrams
• Simply describe your desired diagram in natural language
• Claude will automatically generate, review, and refine the schematic

For new documents: Scientific schematics should be generated by default to visually represent key concepts, workflows, architectures, or relationships described in the text.

How to generate schematics:

python scripts/generate_schematic.py "your diagram description" -o figures/output.png

The AI will automatically:

• Create publication-quality images with proper formatting
• Review and refine through multiple iterations
• Ensure accessibility (colorblind-friendly, high contrast)
• Save outputs in the figures/ directory

When to add schematics:

• Evaluation framework diagrams
• Quality assessment criteria decision trees
• Scholarly workflow visualizations
• Assessment methodology flowcharts
• Scoring rubric visualizations
• Evaluation process diagrams
• Any complex concept that benefits from visualization

For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.

Evaluation Workflow

Step 1: Initial Assessment and Scope Definition

Begin by identifying the type of scholarly work being evaluated and the evaluation scope:

Work Types:

• Full research paper (empirical, theoretical, or review)
• Research proposal or protocol
• Literature review (systematic, narrative, or scoping)
• Thesis or dissertation chapter
• Conference abstract or short paper

Evaluation Scope:

• Comprehensive (all dimensions)
• Targeted (specific aspects like methodology or writing)
• Comparative (benchmarking against other work)

Ask the user to clarify if the scope is ambiguous.

Step 2: Dimension-Based Evaluation

Systematically evaluate the work across the ScholarEval dimensions. For each applicable dimension, assess quality, identify strengths and weaknesses, and provide scores where appropriate.

Refer to references/evaluation_framework.md for detailed criteria and rubrics for each dimension.

Core Evaluation Dimensions:

1. Problem Formulation & Research Questions

   • Clarity and specificity of research questions
   • Theoretical or practical significance
   • Feasibility and scope appropriateness
   • Novelty and contribution potential

2. Literature Review

   • Comprehensiveness of coverage
   • Critical synthesis vs. mere summarization
   • Identification of research gaps
   • Currency and relevance of sources
   • Proper contextualization

3. Methodology & Research Design

   • Appropriateness for research questions
   • Rigor and validity
   • Reproducibility and transparency
   • Ethical considerations
   • Limitations acknowledgment

4. Data Collection & Sources

   • Quality and appropriateness of data
   • Sample size and representativeness
   • Data collection procedures
   • Source credibility and reliability

5. Analysis & Interpretation

   • Appropriateness of analytical methods
   • Rigor of analysis
   • Logical coherence
   • Alternative explanations considered
   • Results-claims alignment

6. Results & Findings

   • Clarity of presentation
   • Statistical or qualitative rigor
   • Visualization quality
   • Interpretation accuracy
   • Implications discussion

7. Scholarly Writing & Presentation

   • Clarity and organization
   • Academic tone and style
   • Grammar and mechanics
   • Logical flow
   • Accessibility to target audience

8. Citations & References

   • Citation completeness
   • Source quality and appropriateness
   • Citation accuracy
   • Balance of perspectives
   • Adherence to citation standards

Step 3: Scoring and Rating

For each evaluated dimension, provide:

Qualitative Assessment:

• Key strengths (2-3 specific points)
• Areas for improvement (2-3 specific points)
• Critical issues (if any)

Quantitative Scoring (Optional): Use a 5-point scale where applicable:

• 5: Excellent - Exemplary quality, publishable in top venues
• 4: Good - Strong quality with minor improvements needed
• 3: Adequate - Acceptable quality with notable areas for improvement
• 2: Needs Improvement - Significant revisions required
• 1: Poor - Fundamental issues requiring major revision

To calculate aggregate scores programmatically, use scripts/calculate_scores.py.

Step 4: Synthesize Overall Assessment

Provide an integrated evaluation summary:

1. Overall Quality Assessment - Holistic judgment of the work's scholarly merit
2. Major Strengths - 3-5 key strengths across dimensions
3. Critical Weaknesses - 3-5 primary areas requiring attention
4. Priority Recommendations - Ranked list of improvements by impact
5. Publication Readiness (if applicable) - Assessment of suitability for target venues

Step 5: Provide Actionable Feedback

Transform evaluation findings into constructive, actionable feedback:

Feedback Structure:

• Specific - Reference exact sections, paragraphs, or page numbers
• Actionable - Provide concrete suggestions for improvement
• Prioritized - Rank recommendations by importance and feasibility
• Balanced - Acknowledge strengths while addressing weaknesses
• Evidence-based - Ground feedback in evaluation criteria

Feedback Format Options:

• Structured report with dimension-by-dimension analysis
• Annotated comments mapped to specific document sections
• Executive summary with key findings and recommendations
• Comparative analysis against benchmark standards

Step 6: Contextual Considerations

Adjust evaluation approach based on:

Stage of Development:

• Early draft: Focus on conceptual and structural issues
• Advanced draft: Focus on refinement and polish
• Final submission: Comprehensive quality check

Purpose and Venue:

• Journal article: High standards for rigor and contribution
• Conference paper: Balance novelty with presentation clarity
• Student work: Educational feedback with developmental focus
• Grant proposal: Emphasis on feasibility and impact

Discipline-Specific Norms:

• STEM fields: Emphasis on reproducibility and statistical rigor
• Social sciences: Balance quantitative and qualitative standards
• Humanities: Focus on argumentation and scholarly interpretation

Resources

references/evaluation_framework.md

Detailed evaluation criteria, rubrics, and quality indicators for each ScholarEval dimension. Load this reference when conducting evaluations to access specific assessment guidelines and scoring rubrics.

Search patterns for quick access:

• "Problem Formulation criteria"
• "Literature Review rubric"
• "Methodology assessment"
• "Data quality indicators"
• "Analysis rigor standards"
• "Writing quality checklist"

scripts/calculate_scores.py

Python script for calculating aggregate evaluation scores from dimension-level ratings. Supports weighted averaging, threshold analysis, and score visualization.

Usage:

python scripts/calculate_scores.py --scores <dimension_scores.json> --output <report.txt>

Best Practices

1. Maintain Objectivity - Base evaluations on established criteria, not personal preferences
2. Be Comprehensive - Evaluate all applicable dimensions systematically
3. Provide Evidence - Support assessments with specific examples from the work
4. Stay Constructive - Frame weaknesses as opportunities for improvement
5. Consider Context - Adjust expectations based on work stage and purpose
6. Document Rationale - Explain the reasoning behind assessments and scores
7. Encourage Strengths - Explicitly acknowledge what the work does well
8. Prioritize Feedback - Focus on high-impact improvements first

Anti-Patterns

NEVER score without evidence

WHY: Unsupported scores are worse than no scores — they create false confidence and give the author nothing actionable to fix.

BAD — no evidence cited:

Methodology: 3/5 — adequate.

GOOD — specific evidence supports the score:

Methodology: 3/5 — the cross-validation protocol is sound (Section 3.2),
but the train/test split is not stratified, risking class-imbalance leakage (Section 3.3).

NEVER skip dimensions because they appear weak

WHY: Omitted dimensions are not "neutral" — they read as tacit approval and leave gaps the author cannot address.

BAD Skip "Data Quality" because the paper's data section is thin. → GOOD Evaluate and score it low with specific notes on what is missing.

NEVER treat all work types as equivalent

WHY: A first-year PhD proposal and a Nature submission are held to different standards. Applying journal-level criteria to a draft proposal produces misleading, demoralising feedback.

BAD Apply publication-readiness criteria to a draft proposal. → GOOD ALWAYS confirm the work type and adjust thresholds before scoring.

NEVER produce a purely negative report

WHY: Feedback without acknowledged strengths demotivates authors and misses the full picture of the work's quality.

BAD List only weaknesses without acknowledging what the work does well. → GOOD ALWAYS lead with concrete strengths before addressing areas for improvement.

# Calculate aggregate scores from dimension-level ratings
python ./scripts/calculate_scores.py --scores dimension_scores.json --output report.txt

Example Evaluation Workflow

User Request: "Evaluate this research paper on machine learning for drug discovery"

Response Process:

1. Identify work type (empirical research paper) and scope (comprehensive evaluation)
2. Load references/evaluation_framework.md for detailed criteria
3. Systematically assess each dimension:
   • Problem formulation: Clear research question about ML model performance
   • Literature review: Comprehensive coverage of recent ML and drug discovery work
   • Methodology: Appropriate deep learning architecture with validation procedures
   • [Continue through all dimensions...]
4. Calculate dimension scores and overall assessment
5. Synthesize findings into structured report highlighting:
   • Strong methodology and reproducible code
   • Needs more diverse dataset evaluation
   • Writing could improve clarity in results section
6. Provide prioritized recommendations with specific suggestions

Integration with Scientific Writer

This skill integrates seamlessly with the scientific writer workflow:

After Paper Generation:

• Use Scholar Evaluation as an alternative or complement to peer review
• Generate SCHOLAR_EVALUATION.md alongside PEER_REVIEW.md
• Provide quantitative scores to track improvement across revisions

During Revision:

• Re-evaluate specific dimensions after addressing feedback
• Track score improvements over multiple versions
• Identify persistent weaknesses requiring attention

Publication Preparation:

• Assess readiness for target journal/conference
• Identify gaps before submission
• Benchmark against publication standards

Notes

• Evaluation rigor should match the work's purpose and stage
• Some dimensions may not apply to all work types (e.g., data collection for purely theoretical papers)
• Cultural and disciplinary differences in scholarly norms should be considered
• This framework complements, not replaces, domain-specific expertise
• Use in combination with peer-review skill for comprehensive assessment

References

• Evaluation Framework — detailed rubrics and scoring criteria for each ScholarEval dimension
• scripts/calculate_scores.py — aggregate dimension scores, threshold analysis, and report output (not yet implemented)
• ScholarEval Paper — Moussa et al. (2025), ScholarEval: Research Idea Evaluation Grounded in Literature