Paper Analysis

Paper Analysis

Systematic scientific paper reading, summarization, and critical analysis.

Paper Acquisition

1. If user provides a DOI: fetch via https://doi.org/DOI or Semantic Scholar API
2. If user provides arXiv ID: fetch via https://arxiv.org/abs/ID
3. If user provides a URL: use web_fetch to extract content
4. If user provides a PDF file: read directly or use summarize skill

Analysis Framework

Quick Summary (default)

Provide in ~200 words:

• Research question / objective
• Method (1-2 sentences)
• Key findings (2-3 bullet points)
• Main contribution
• One key limitation

Deep Analysis

When requested, provide structured analysis:

1. Paper Metadata

• Title, authors, year, journal/venue, DOI
• Citation count (via Semantic Scholar)

2. Research Context

• Problem statement and motivation
• Research gap being addressed
• Theoretical framework

3. Methodology Assessment

• Study design (experimental/observational/computational/theoretical)
• Sample/dataset description
• Variables (independent, dependent, controls)
• Analysis methods
• Reproducibility assessment (data/code availability)

4. Results Evaluation

• Key findings with effect sizes and confidence intervals
• Statistical significance vs practical significance
• Figures and tables interpretation
• Are results consistent with claims?

5. Critical Assessment

Check for:

• Internal validity: confounds, selection bias, measurement error
• External validity: generalizability, ecological validity
• Statistical issues: multiple comparisons, p-hacking, small N
• Logical issues: correlation ≠ causation, survivorship bias
• Reporting issues: selective reporting, missing negative results
• Methodological rigor: appropriate controls, blinding, randomization

6. Contribution & Impact

• Novelty assessment
• Practical implications
• Theoretical implications
• Future directions suggested

Comparison Mode

When comparing multiple papers:

Domain-Specific Checklists

RCT (Randomized Controlled Trial)

• CONSORT checklist compliance
• Randomization method, allocation concealment
• Blinding (single/double/triple)
• ITT vs per-protocol analysis
• Dropout rates and handling

Observational Studies

• STROBE checklist
• Confounding control methods
• Selection bias assessment

Machine Learning Papers

• Train/val/test split methodology
• Baseline comparisons
• Ablation studies
• Statistical significance of improvements
• Computational cost reporting
• Code/data availability

Qualitative Research

• Sampling strategy (purposive, theoretical, snowball)
• Data saturation
• Coding methodology (thematic, grounded theory)
• Reflexivity and positionality
• Member checking / triangulation

Output Style

• Use academic but accessible language
• Cite specific sections/figures/tables from the paper
• Distinguish between what the paper claims and what the evidence supports
• Flag any red flags clearly but diplomatically