| name | self-review |
| description | Automatically review an academic paper using the NeurIPS review form with three reviewer personas, ensemble scoring, and reflection refinement. Extracts text from PDF, runs structured review, and outputs actionable feedback. Use when the user wants to review a paper before submission or get feedback on a draft. |
| argument-hint | ["pdf-or-tex-file"] |
Self-Review
Review an academic paper using a structured review form with multiple reviewer personas.
Input
$ARGUMENTS — Path to PDF file or .tex file
Scripts
Extract text from PDF
python ~/.claude/skills/self-review/scripts/extract_pdf_text.py paper.pdf --output paper_text.txt
python ~/.claude/skills/self-review/scripts/extract_pdf_text.py paper.pdf --format markdown
Tries pymupdf4llm (best) → pymupdf → pypdf. Install: pip install pymupdf4llm pymupdf pypdf
Parse PDF into structured sections
python ~/.claude/skills/self-review/scripts/parse_pdf_sections.py \
--pdf paper.pdf --output sections.json
Extracts title (via font size), section headings, and section text. Requires: pip install pymupdf
Key flags: --format text, --verbose
Workflow
Step 1: Load Paper
- If PDF: use
extract_pdf_text.py to extract text
- If
.tex: read the LaTeX source directly
Step 2: Three-Persona Review
Run three independent reviews using different personas (from references/review-form.md):
- Harsh but fair reviewer: Expects good experiments that lead to insights
- Harsh and critical reviewer: Looking for impactful ideas in the field
- Open-minded reviewer: Looking for novel ideas not proposed before
For each persona, generate a review following the NeurIPS review JSON format in references/review-form.md.
Step 3: Reflection Refinement (up to 3 rounds per reviewer)
After each review, apply the reflection prompt: re-evaluate accuracy and soundness, refine if needed. Stop when "I am done".
Step 4: Aggregate
- Combine all three reviews
- Average numerical scores (round to nearest integer)
- Synthesize a meta-review finding consensus
- Weight scores using AgentLaboratory weights: Overall (1.0), Contribution (0.4), Presentation (0.2), others (0.1 each)
Step 5: Actionable Report
Output format:
## Review Summary
- **Overall Score**: X/10 (Weighted: Y/10)
- **Decision**: Accept / Reject
- **Confidence**: Z/5
## Strengths (consensus across reviewers)
1. ...
2. ...
## Weaknesses (consensus across reviewers)
1. ...
2. ...
## Questions for Authors
1. ...
## Specific Suggestions for Improvement
1. [Section X, Page Y]: ...
2. [Section Z, Page W]: ...
## Score Breakdown
| Dimension | R1 | R2 | R3 | Avg |
|-----------|----|----|-----|-----|
| Overall | ... | ... | ... | ... |
| Contribution | ... | ... | ... | ... |
| ... | ... | ... | ... | ... |
References
- NeurIPS review form, scoring weights, personas, reflection prompts:
~/.claude/skills/self-review/references/review-form.md
- PDF text extraction:
~/.claude/skills/self-review/scripts/extract_pdf_text.py
Missing Sections Check
You MUST verify that all required sections are present: Abstract, Introduction, Methods/Approach, Experiments/Results, Discussion/Conclusion. Reduce scores if any are missing.
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